This term describes knowledge or assumptions made based only on what one already knows before collecting data. It’s typically used to describe a starting hypothesis or the expectations researchers have at the start of developing a research question, before they have any other knowledge or evidence to go on.

In philosophy, a priori knowledge is that which a person has before they begin learning about the world — instead of a blank slate, a priori knowledge is the slate — so when used in science, it’s usually referring to a research question that hasn’t been explored yet or which the researchers have virtually no data to use in coming up with a hypothesis. A priori also refers to the status of a research question before a study begins in a more pragmatic sense, such as a priori registration of a clinical trial—registering it, most often at clinicaltrials.gov, before the trial begins.

A priori registration has become increasingly important as a way to ensure the credibility and transparency of the research process because it reduces the likelihood of p-hacking or changing a hypothesis or endpoint midway through the trial. The term a priori also refers more broadly to the state of knowledge or the data available before a trial versus after the trial (a posteriori), since some research papers explicitly investigate clinical relevance of a trial a priori vs. a posteriori, such as papers looking at findings’ generalizability.

The chance that something will happen within a given amount of time, stated in raw numbers. In medical studies, it’s usually the chance that someone will get a disease or die of it. For example, the statement “According to the National Cancer Institute, one in 68 women will get breast cancer between the ages of 40 and 50” is a statement of absolute risk.

Absolute risk is distinct from relative risk, which is a ratio of likelihood of something occurring. Absolute risk is based on the actual numbers whereas relative risk is based on the proportional level of risk. For example, if one out of 100 infants are born with a birth defect, the absolute risk of a birth defect is 1%. If three out of 100 infants exposed to drug X are born with a birth defect, the absolute risk of a birth defect is 3%. In this example, the increased absolute risk of a birth defect when exposed to drug X is 2% since 3 - 1 = 2. However, the relative risk of a birth defect when exposed to drug A is the change from 1% to 3%, a 200% or threefold increase. That is, infants exposed to drug X are three times more likely than unexposed infants to have a birth defect. It’s just that the increase still represents only an additional 2% more infants since the baseline rate is so low.

Given the massive difference in 2% absolute risk vs. 200% relative risk in this example, it’s clear why it’s important to clarify for your audience what kind of risk you’re talking about. Ideally, you would provide both absolute and relative risk and make clear what the difference is. Sometimes one is more important than another to emphasize. Special care should be taken in headlines not to scaremonger by using relative risk when it suggests a far greater risk than absolute risk would suggest, such as in this example:

“It’s very frightening to think that exposure to drug X will triple a child’s risk of birth defects, but the absolute risk remains very low and may be worth the risk-benefit tradeoff if not taking the drug could be dangerous to the mother.”

Surveillance is the process or system for tracking cases of risk factors, medical conditions, disease cases, adverse events, etc. It’s often used to track incidence of a disease or side effects from drugs or vaccines. The two basic types of surveillance are active and passive. Passive surveillance is collection of data from those voluntarily reporting it, such as hospitals, health care providers, parents or health departments. Active surveillance involves actively looking for cases either with a reporting system or using a systematic protocol, such as calling every health department in a region during a disease outbreak.

With passive surveillance, no one is actively systematically looking for specific cases. Passive surveillance is useful for looking for patterns or “signals,” such as a cluster of disease cases or a higher-than-expected report of side effects with a pharmaceutical. The biggest limitation of passive surveillance is not knowing the denominator, the total number of encounters or people that the cases are reported from, since reporting is voluntary and self-selecting. Active surveillance, on the other hand, has a clear denominator, the total number of individuals, clinics, hospitals, populations, etc. that are assessed. With active surveillance, you can establish incidence or prevalence (the numerator) as a fraction of the population assessed (the denominator).

The Vaccine Adverse Event Reporting System (VAERS) and FDA Adverse Event Reporting System (FAERS) are both passive surveillance systems: adverse events are reported by providers or patients, but the existence of a report does not mean the event resulted from the vaccine or drug. The report may or may not be true, the event may or may not have been caused by the drug, and there’s no way to know how many people did NOT report the same event (or did not experience it at all).

Vaccine Safety Datalink (VSD) is an active surveillance program which researchers can use to search for a specific adverse event among an established number of patients who received a vaccine (the denominator).

In the simplest terms, acute conditions are short-term while chronic conditions are long-term. However, these two ways of categorizing an illness, disease, pain, or other condition involve many other differences aside from duration of the condition.

Acute illnesses are those that tend to have a definitive start and end (the patient or their physician can identify when the condition started and when it has stopped). They also generally affect one or a few specific, identifiable body parts, organs or systems. Most of the time, acute illnesses respond to medication or other treatments, or they resolve on their own over time (e.g., a broken bone healing, a common cold infection eventually defeated by the immune system, etc.). The causes of acute infections or conditions are also usually pretty straightforward.

Chronic illnesses, in addition to affecting a person over a longer period of time, tend to be more complex in general and may not have easily identifiable or isolated causes, or they may have multiple causes. Chronic conditions often involve multiple body areas, organs or systems, or even the entire body. They may or may not respond to treatment, or treatment options for them may not exist. Chronic conditions may require multiple treatment strategies or shifts in treatment. Cancer, for example, is a chronic condition that can affect up to the entire body and may require radiation, surgery and/or multiple drugs, and physicians may need to switch strategies over the course of the illness. An Ebola virus infection, however, is acute even though it may affect the entire body. Treatment of chronic conditions, then, is typically more complex than that of acute conditions and may focus on management, quality of life, self-care and coping skills. With acute conditions, the goal is a cure or healing.

Broadly speaking, chronic illnesses tend to have a greater impact on quality of life, but an acute illness can certainly have intense short-term effects on quality of life and can result in a chronic condition. For example, measles is an acute disease (with a seriously unpleasant short-term experience), but if complications from the measles cause deafness, then the hearing loss becomes a chronic condition requiring management, even if that management is occupational therapy to learn new ways of living. Or, an acute condition may cause a temporary chronic one, such as frequent coughing over several months caused by a pertussis infection, even if the active pertussis infection resolves within the first month. Or, an acute injury, such as a leg laceration, can lead to a permanent disability if the leg requires amputation or if an individual loses function in the limb.

Any incident that occurs following a drug, vaccine, surgery, procedure or other medical intervention. If the adverse effect was actually caused by the intervention, then it’s a side effect. But even though all side effects are types of adverse events, not all adverse events are side effects. For example, in a randomized controlled trial, adverse events that occur at roughly the same rate in the intervention group and the control group probably aren’t caused by the intervention.

Adverse events and side effects are often conflated in news stories, blogs, social media, everyday discussion and even in conversations with medical professionals. However, when writing about research studies, there is a key difference that journalists must understand to avoid inadvertently misleading readers. An adverse event refers to any event that affects a person’s health that occurs after they have received a treatment, whether that treatment is a medication, a surgery, a therapy or another intervention. The adverse event may or may not be caused by the intervention.

A side effect is an adverse effect that has been determined as a direct result of the intervention. In other words, the person who took a certain medication experiences an adverse event, such as a dry mouth or an increased blood pressure, that the medication definitely caused. Side effects are determined by comparing adverse events in randomized controlled trials where one group receives the intervention and one group does not. If the proportion of a certain adverse event is much higher in the group receiving the drug or intervention than in the control group, then the drug or intervention is the cause of that adverse event, which then becomes a side effect.

For example, say 100 people receive the flu vaccine. Then 90 of them have sore arms, 10 have fevers, two have muscle cramps, two of them get into car accidents later that day, and one of them has a heart attack that night. All of those events are adverse effects — including the car accidents and heart attack — even though there is no biological way the flu vaccine could have caused the car accidents and there is no evidence that flu vaccines increase the risk of a heart attack. The sore arms, fevers and muscle cramps, however, very well could have been side effects. It’s not a guarantee that all the fevers were caused by the flu vaccine, but fever is a known possible side effect of the vaccine. The muscle cramps depend on where they occurred. If the cramps are in the arms where the person got the shot, it probably is a side effect. If it’s a Charley horse cramp in the leg, it’s an adverse event that’s probably unrelated to the flu vaccine. If it’s general achiness for a day that feels similar to what the flu feels like, then it likely was caused by the vaccine since that’s a known side effect. This is why reading the list of adverse events in a vaccine package insert tells the reader nothing about actual possible side effects of the vaccine.

ACIP is the CDC committee which reviews all the evidence about vaccines and makes recommendations to the CDC on which ones to recommend, for which demographic populations, and at which doses and frequencies.

The ACIP typically meets three times a year to review data and vote on recommendations, but meetings can be added as needed, as they have been during the Covid-19 pandemic to evaluate the evidence and make recommendations for the new Covid-19 vaccines and their subsequent boosters for different populations. Committee meetings are always public and live-streamed and provide an opportunity for the public to make oral or written comments ahead of each meeting.

All slides, minutes and agendas of the meetings are also publicly available. The CDC nearly always adopts ACIP’s recommendations on vaccines, which then become the CDC’s official recommendations. However, the CDC director always has the option of overruling ACIP, though doing so is rare. One example was in October 2021, when CDC director Rochelle Walensky made a recommendation for Covid-19 booster vaccines that was broader — applying to a larger population — than what ACIP had recommended.

The committee includes 15 voting members: a consumer representative and 14 members with expertise in vaccinology, immunology, pediatrics, internal medicine, nursing, family medicine, virology, public health, infectious diseases, and/or preventive medicine. While committee members are usually unable to speak to the press about regulatory matters on the vaccines whose committees they serve on, they can be good sources for general vaccine stories or for more specific ones after they’re no longer serving on ACIP. You can find a list of archived members here. There are currently 14 Work Groups, excluding the Covid-19 vaccine work group.

Attributable risk is a way of measuring prevalence of a disease or condition and refers to how many cases in a population of exposed individuals can be directly linked to that exposure. It’s a calculation made in biostatistics. Attributable risk is usually presented in percentages, such as stating that the attributable risk of smoking for lung cancer is 85% — that is, 85% of all lung cancers are caused by smoking. In genetics, the “exposure” is how often a specific genetic variant or mutation occurs. So the attributable risk of a particular condition that can be caused by that variant or mutation would be the percentage of people who have the condition out of the total people who are estimated to have that variant (its frequency in the population).

Attrition is the loss of participants in a study over time. All studies have individuals who may drop out for some reason, such as disliking the side effects, and others change their numbers, move away, become sick, die or otherwise fall out of a study. A study with unusually high attrition rates should be viewed skeptically, and journalists should ask the researcher why the attrition was high and what they did to account for the big losses.

Attrition bias is the potential skewing of data/results that arises due to the attrition, or dropout rate, in a study. A certain amount of people leaving a study is normal. Some move away or move within the area and get lost to follow-up, some may die (depending on the condition, their age, the length of the study, etc.), some have work or family or other circumstances come up that require their time, some cannot manage the side effects of a condition, and some simply choose never to show up again. What should draw your attention is any study where attrition is particularly high (such as more than a third of participants) or where attrition is substantially higher in one arm of the study than in another.

Attrition bias is just one of many biases that can potentially distort findings, but it’s one of the easier ones for journalists to look for since studies should report the starting and ending number of participants in each study arm. If an intervention group, for example, has much higher attrition than the placebo arm, that may call into question how reliable the statistical analysis is at comparing effects between the two unless the researchers specifically account for those attrition differences, if they’re able to. Either way, it’s worth at least asking about. Sometimes higher attrition in an intervention group occurs for reasons directly related to the intervention, such as low tolerance for serious or bothersome side effects or difficulty adhering to a particular regimen.

While attrition bias is especially important to look at in randomized controlled trials, you should also look at differences in groups in case control and other observational studies. If one group is substantially higher than another, there may be confounding factors that influence the findings and which the researchers should either account for in their statistical analysis, list as a limitation of the study, or at least speculate about in their discussion.

For example, consider a study looking at drinking in pregnancy that includes one group of women who completely abstain, one group of women who have a few drinks during pregnancy, and one group who drink frequently during pregnancy. If attrition is twice as high in the group of abstainers than in the other two groups, it’s important to ask why and how that might affect the results. Is there some characteristic specific to abstainers that influenced their higher rate of attrition? If so, could that characteristic also influence the outcomes of their child? Enough to confound the comparison to the children born to women in the other two groups? Does that characteristic relate to why the women abstain in the first place?

Sometimes it just works out that one group has higher attrition than another, and sometimes attrition has little effect on the results, but more often than not, noticeable differences or large amounts of attrition are likely to introduce bias that the researchers should address in their statistical analysis and/or discussion.

The background rate of a particular condition refers to how often it typically occurs in a particular population or in the population at large.

Researchers use the background rate of certain conditions to look for adverse events linked to medical interventions, such as pharmaceuticals or surgeries, and determine whether they occur at a higher rate among patients receiving the intervention. For example, if researchers want to determine how much higher the risk for a blood clot is following a particular surgery, they have to first find out how many people in the general population experience a blood clot even if no one in the population has had that surgery. If the background rate is one blood clot per 1,000 people, and those who undergo a specific surgery experience blood clots at a rate of five per 1,000 people, then the researchers know there is a good chance the surgery is what is contributing to the blood clot.

The next step would be to see if those individuals had other underlying conditions that might predispose them to blood clots.

The measurements/assessments taken at the beginning of a study before any interventions have begun represent the baseline. Outcomes assessed during the study and at the end of the study will be compared to the baseline. Baseline can also refer to a baseline study or survey, intended to capture a snapshot of current circumstances before any further research is planned or carried out.

Also called basic, fundamental, or bench research, basic science involves pre-clinical research (research not performed in humans) that focuses on the fundamental building blocks of life, such as cells, organelles, amino acids or genes. Basic science also includes research in animals, such as examining outcomes of certain exposures, testing a potential drug, or exploring a physiological pathway. Basic science studies are critically important to new discoveries since they often uncover biological targets for new drugs, for example, or identify the causal mechanisms behind physiological processes, but they don't have an immediate practical application. For this reason, reporters should take great care communicating the preliminary nature of basic scientific findings to the public.

Biases are systematic errors in the design or reporting of medical studies that produce a false pattern of differences between observed and actual results. Biases skew the actual effect of a treatment or medication.

There’s a long list of biases that can occur in research, but some of the ones reporters should particularly look for include: Selection bias, or key baseline differences between groups that are to be compared; Attrition biases, or between-group differences who leaves a study; Performance biases, or differences in the care that is provided between two groups, or differences in exposures; and Reporting biases, or differences between reported and unreported findings.

For some examples, see "Don’t fudge the facts on chocolate studies."

When two things are associated, such as a condition and an outcome, researchers often seek to find out whether one causes the other. If the relationship is bidirectional, then they contribute to one another, much as in a feedback loop.

For example, in investigating the relationship between smoking and lung cancer, researchers eventually determined that smoking causes lung cancer; the causation runs in one direction, or unidirectional. But what if behavior A contributes to outcome B and outcome B also contributes to behavior A? That would be a bidirectional relationship.

Consider the example of spanking. Research has shown that physical punishment is linked to increased aggression in children and teens. But the question remained for many years whether it was physical punishment that caused aggression or whether more aggressive children simply got spanked more often because they acted out more. Or, is it a bit of column A and a bit of column B?

In the case of spanking, researchers measured baseline aggression in children and then compared children who were and were not physically punished but started out with the same level of aggression. Longitudinal studies eventually revealed that children who were spanked became more aggressive, even compared to non-spanked children who started out with a similar level of aggression. Still, however, it is likely that the relationship is partly bidirectional over time: the more aggressive a child becomes, the more often the child may be physically punished.

Bioavailability refers to quantifying the ability of the human body to extract from a substance the nutrients or other chemicals it needs for a particular function.

Simply ingesting a drug or supplement does not necessarily mean it will interact with the body. If the drug or supplement is intended as treatment, it needs to be bioavailable: that is, some part of it must actually enter the bloodstream and be used by the body for some kind of function. Bioavailability, often expressed as a percent, refers to the proportion of the substance that the body can absorb and use.

For example, taking vitamin D supplements may not actually increase the body’s vitamin D levels if the supplements are not taken in a bioavailable form. Bioavailability can also be applied to contaminants, such as the bioavailability of lead in contaminated soil.

Although bioavailability requires absorption, absorption does not necessarily mean something becomes bioavailable—it has to survive the digestive process. For the nerdy specifics, this link discusses the equation in which bioavailability and absorption are different variables.

Two different pharmaceutical products are bioequivalent if they contain the same chemical compounds in the same proportions (ideally) and are absorbed and used by the body in such a way that they should have identical or very similar therapeutic and adverse effects. Basically, one should be just as safe and effective as the other.

Bioequivalence comes into play with generic versus name brand drugs. For example, in theory, escitalopram manufactured by any pharmaceutical company should have the same effects on the body as any other, whether it’s officially the brand name Lexapro or a generic version. In reality, however, two supposedly bioequivalent products may have differences that impact therapeutic benefit or side effect risks, such as findings about differences in Wellbutrin generics that led to requests for post-market studies from the U.S. Food and Drug Administration. Further, since the standards for bioequivalence can vary across nations or agencies, such as the World Health Organization or FDA, it’s important for journalists to pay attention to what the standards are in any countries that export drugs into the U.S.

Blinding, also called masking, refers to concealing from participants and/or study teams who is and is not receiving an intervention or placebo in a clinical trial.

When researchers conduct clinical trials that randomize participants, they have to contend with the possibility that participants will have psychosomatic responses to their treatments, such as expecting that they will get better — and subsequently feeling better — if they know they are receiving a medication that is intended to treat a condition they have. Similarly, the person administering a treatment might convey through body language, verbal language or some other way, even inadvertently, that a participant is receiving the real medication as opposed to the placebo. If participants know who is receiving the actual, pharmacologically active medication and who is receiving the placebo, it could bias the results based on what is known about the placebo effect and nocebo effect and other types of biases.

Therefore, when possible, researchers employ blinding during trials so that participants and/or those administering a medication or treatment do not know which is the real treatment and which is the placebo or control treatment. Those administering the treatments may also be blinded from knowing which participants have a condition and which don’t if the control group is individuals who do not have a condition.

During a single-blind trial, only the participants (or, more rarely, only the researchers/administrators of a medication/treatment) do not know who is in the control group and who is in the experimental group. With double blinding (double masking), both the participants and the researchers are prevented from knowing who is in the control group and who is in the experimental group. (Note: Sometimes “triple blinding” is used if the participants, the researchers and the person administering a treatment do not know who is in the control versus experimental groups, but this same scenario is often covered under double blinding as well.)

Blinding can also refer to removing results or a time period from an analysis if leaving them in could bias the analysis of the data. For example, if a long-term study involving women looks at side effects that could result from use of birth control, and some women in the study become pregnant, their period of gestation may be “blinded” in the analysis since any of the outcomes studied that occur during that time would likely be caused by the pregnancy or some other factor and not from the birth control.

In some single-blind procedures, patients are not receiving a placebo but instead a “sham treatment” that resembles a non-pharmacological intervention being tested. In acupuncture, for example, a sham procedure would involve inserting needles into the control participants but at different body locations than what traditional acupuncture would call for.

A Bonferroni correction is a calculation intended to reduce the likelihood of a false positive in study results by accounting for a high number of comparisons — high enough that it could lead to a statistically significant result simply because of how many comparisons are made.

As described in the key concept of P-hacking, if the data from a study are examined in enough different ways, some kind of association will usually emerge, based purely on the statistical chance that you’ll find something if you look hard enough in enough different places. If you’re only comparing four things, a statistically significant result is likely a true effect. But if you’re comparing 30 different exposures, you would expect to find associations between some of them simply because the chances of a random association increase as the number of comparisons increases. The Bonferroni correction is a statistical adjustment that’s intended to offset this possibility by establishing a lower threshold for statistical significance.

The mathematical mechanics of it (described here and here) aren’t important so much as simply understanding that it exists for two reasons. First, you might come across it in a study’s methods, which tells you that the researchers are aware of how many different outcome possibilities might be in their paper and they want to be sure none of them result from statistical chance. Second, you want to note the absence of Bonferroni correction if you’re reading a study that seems to have a lot of possible outcomes (and/or sub-analyses), but the authors don’t note any attempts to control for statistically significant results that emerge by chance. Having multiple dependent or independent variables or statistical tests conducted on a single data set is a situation where a Bonferroni correction should be used to ensure the results are “real.”

A qualitative, descriptive study that focuses on an individual patient (a case series includes multiple individuals) and a particular condition, procedure, association or other phenomenon that is unusual and interesting enough to be written up on its own. Case studies in and of themselves cannot show causation, establish a trend, generalize to other individuals, inform incidence or prevalence or otherwise “prove” anything. They are used to generate hypotheses, raise awareness of a potential issue, provide instruction on a procedure, seek a mechanism for a suspected cause-and-effect relationship or related goals. 

A qualitative, descriptive study that focuses on an individual patient (a case series includes multiple individuals) and a particular condition, procedure, association or other phenomenon that is unusual and interesting enough to be written up on its own. Case studies in and of themselves cannot show causation, establish a trend, generalize to other individuals, inform incidence or prevalence or otherwise “prove” anything. They are used to generate hypotheses, raise awareness of a potential issue, provide instruction on a procedure, seek a mechanism for a suspected cause-and-effect relationship, or similar purposes.

This type of retrospective study design identifies a group of individuals who have already experienced a particular outcome or who already have a particular condition and compares them to a similar group without the outcome/condition to look for differences between the groups that might reveal associations with the outcome/condition.

Those who have the condition/outcome of interest are called the “cases.” The “controls” are other individuals who are substantially similar to the cases in important ways, usually matched to the cases on the basis of age, sex, and similar demographic factors, such as geography, race/ethnicity, socioeconomic status, doctor or clinic, etc. How controls are matched to the cases will depend on the study, and many case control studies will match multiple controls to a single case (three controls to one case or 10 to one, etc.).

Researchers then compare certain pre-identified factors or characteristics between the cases and the controls with the hope of identifying risk factors in the case group for the shared condition they have. For example, a case control study might bring together a group of individuals who all have high blood pressure and then match controls without high blood pressure to these cases. Then the researchers might look at dietary patterns or physical activity in the cases and the controls to see if any patterns suggest that certain aspects of diet or physical activity may contribute to high blood pressure.

Statistical significance measures how likely it is that a research finding occurred due to a real effect versus chance, but whether that finding is actually meaningful for doctors and patients is a separate issue. Clinical significance, also called practical significance or clinical importance, attempts to answer whether a new finding will make a big enough difference to change the way a doctor treats a patient’s condition. While statistical significance is usually measured with P values in clear, objective numbers (even if they have arbitrary cut-offs), clinical significance is more subjective. It relies on clinical judgment and various other factors, such as the condition being treated, side effects of an intervention, details about the patient population, the cost of a drug or intervention, a doctor and/or patient’s comfort with trying something new, and various other risks and benefits.

Any time a journalist is covering a study, they should ask the researcher and other interviewees about the clinical significance of the findings: what are the implications for patients and doctors now or in the future?

Clinical significance most often depends on whether a drug exceeds the threshold of a minimal clinically important difference, or the smallest effect needed to cause a doctor to change how they manage a patient’s condition. For example, let’s say the minimal clinically important difference for a new pain medication is that it reduces someone’s pain by at least two points on a scale of 1 to 10. Now, consider a new pain drug assessed in a series of studies that uses a pain scale of 1 to 10 to measure its effectiveness. The drug proves to be effective multiple times with a P value of less than 0.01. That means the results are almost certainly a real effect, not due to chance.

However, let’s say the improvement in pain is a change of 0.4 points on the scale. In other words, if a person’s pain is an 8, then taking this medication will have an effect, but it will only drop their pain, on average, to a 7.6. Such a small change in pain is unlikely to make it worth taking the drug, especially if it is expensive or has other unpleasant side effects. If relying on the minimal clinically important difference of 2, it doesn’t meet the threshold. How that threshold is determined varies. For a condition that is excruciatingly painful, even a slight reduction in pain from a new drug may be worthwhile. But for another condition that is only mildly painful, a patient might barely notice a slight reduction in pain.

Although clinical significance does not have a single, objective measurement tool, there are several objective numbers that can contribute to clinical judgment. Two are Number Needed to Treat (NNT) and Number Needed to Harm (NNH). If a drug has a big effect and is statistically significant but 100 people must be treated for just one of them to experience that benefit, then a doctor may be more likely to stick with a drug that is slightly less effective but works for more people. Switching to a new drug might risk having too many people who get little benefit. Or, if a drug is very effective and works for a lot of people (say one in every two people benefits, an NNT of 2) but harms one of every four people who uses it (NNH of 4), then a doctor may determine the benefit isn’t worth the risk for a group of patients.

Confidence intervals are another objective measurement often used in assessing clinical significance. They provide the upper and lower ranges of the effect likely to occur 95% of the time. If the range is narrow, the doctor can be more confident about the expected effects of the treatment. If the range is wide, the extent to which the drug works for a patient becomes less predictable. For example, let’s say a new drug for psoriasis reduces the number of flare-ups by an average of 10 per year. If the confidence interval is between 8.2 and 11.4, a doctor can be fairly confident that a patient taking this medication will have 8 to 11 fewer flare-ups in a year.

However, if the confidence interval is 1.4 to 20.1, then some patients may see a huge benefit (20 fewer flare-ups) while others see very little benefit (just two fewer flare-ups). Or, we consider the pain drug example above, a confidence interval of 0.1 to 4 might make the drug worth trying if some individuals will experience a reduction in pain from an 8 to a 4 — that’s a 50% reduction in pain.

Finally, comparing relative risk and absolute risk may offer clues to whether a finding should be regarded as clinically significant. If, for example, taking antidepressants during pregnancy increases the likelihood of a specific birth defect by five times, that sounds pretty frightening. But if the birth defect in question only occurs in one in 1 million babies, then a fivefold increase in risk means that five in 1 million babies will experience it, perhaps not enough to justify telling a pregnant person not to take it.

As illustrated with that example, clinical significance can also relate to harms. If a risk from a procedure is blood loss, but the average blood loss amounts to an average of 70 mL with a narrow confidence interval, that’s not enough blood loss to pose a serious risk. Sometimes a research finding may not offer much clinical significance because it’s simply not ready for prime time: the findings were conducted in animal studies, can’t yet be generalized to a broader population or do not yet have enough evidence to support them from multiple studies. If a study finds that eating apples during pregnancy is associated with a higher risk that the fetus will later develop ADHD, for example, much more research is needed before doctors start telling pregnant women not to eat apples. The finding may be interesting and statistically significant, but not yet clinically significant.

For additional reading on how researchers and doctors consider objective measures of clinical significance, read this review paper.

A comorbidity refers to having two or more conditions or diseases at the same time in a person, such as a person with both diabetes and liver disease. Comorbid conditions are typically associated with more severe disease and more complex care. They are often, but not always, related, such as sharing underlying causes or risk factors or one causing or worsening the other. Common conditions that often co-occur with other conditions include arthritis, asthma, chronic pain, cancer, diabetes, cardiovascular disease, hepatitis and mental health conditions.

Comorbidities may coexist because they are related to one another (one causes the other or they both result from a shared underlying factor) or because of coincidence. In a person with an anxiety disorder and severe tooth decay, the conditions are most likely not related. But sometimes conditions can have unexpected links.

A person with obsessive compulsive disorder, a type of anxiety issue, may develop severe dental problems if their OCD leads to excessive teeth-brushing that damages their enamel. Other times, the conditions may not be directly related, but one can exacerbate another. A person might have cirrhosis of the liver and major depressive disorder at the same time that were not initially related, but reduced quality of life from cirrhosis might worsen depression symptoms.

Comorbidities typically refer to chronic conditions, but a person may have an acute condition along with chronic comorbidities. Most often, the comorbid conditions are related because they share risk factors or affect the same body systems, or because one increases the risk of the other.

Certain mental health disorders often co-occur with substance use disorders in those who self-medicate and/or did not receive appropriate treatment for their mental disorder. Diabetes is a risk factor for cardiovascular disease and often occurs with a comorbidity of atherosclerosis.

Unsurprisingly, comorbidities increase with age. Comorbid conditions are also associated with more severe disease and more complex care and treatment needs. Some of the most common conditions that involve comorbidities are arthritis, asthma, chronic pain, hepatitis, dementia, back problems, cancer, diabetes, cardiovascular disease, mental health conditions, autoimmune conditions and chronic obstructive pulmonary disease.

In covering medical studies, understanding comorbidities becomes especially important because the presence or absence of certain comorbidities may influence study findings, especially if the researchers don’t control for them. If researchers were to conduct a study on risk factors for cardiovascular disease, and they did not account for participants with high cholesterol, diabetes, obesity, smoking, or other comorbidities, the results would not be valid. Looking at what comorbidities a study does or doesn’t account for may be a place reporters find significant limitations in a study, or at least questions to ask the researchers.

When researchers measure a combination of possible clinical events in a clinical trial, they have created a composite endpoint. Composite endpoints increase the statistical power of studies, which allows scientists to run smaller, quicker and usually less expensive trials.

Composite endpoints can be useful when they measure events that are generally of equal severity and importance to patients, such as heart attacks and strokes. But composite endpoints can be misleading if they include surrogate endpoints, especially if those surrogates are more common and less significant than the other outcomes, i.e., a composite endpoint that includes heart attacks, strokes and cholesterol counts. In such muddled composites, the less significant events often drive the supposed effect of the intervention that’s being tested, and they can make a treatment look more effective than it actually is.

Confidence intervals are one way that researchers report statistical significance in a study. The other is the p-value.

Unlike p-values, confidence intervals report the range of possible treatment effects, rather than just the average effect. Because of this, they can be useful sources of information for health reporters. Wide confidence intervals are generally distrusted in studies, since they indicate that the treatment effect is not very precise or reproducible. Narrow confidence intervals, on the other hand, are usually a sign that the study is well done and that the effect of a drug or treatment is reliably reproduced from patient to patient.

Confidence intervals are not statistically significant if they include the value of no effect; normally, the effect of no treatment is seen in the control group, sometimes called the reference group. Typically, the control group is given the value of 1, so confidence intervals that cross the number 1 are generally not statistically significant, though there are exceptions.

A set of circumstances that creates a real or perceived risk that professional judgment or actions concerning a primary interest will be unduly influenced by a secondary interest, such as a financial or ideological interest. A conflict of interest exists whether or not a particular individual or institution is actually influenced by the secondary interest.

In observational studies, confounding variables are factors that confuse or obscure the association between a primary exposure of interest and an outcome.

For example, scientists studying the relationship between birth order and Down syndrome found that later born children had much higher risks of Down syndrome than first-born children. When they delved deeper into the association, however, they found much of that risk was explained by maternal age. Mothers over age 40 were far more likely to have babies born with Down syndrome than younger mothers. At the same time, mothers having a third, fourth, or fifth child are also more likely to be older. Therefore, the association between birth order and Down syndrome was confounded by maternal age.

Confounding is very common, and it is not always easy to tease out or control for in observational studies. It’s the main reason that randomized, controlled trials are considered to a higher level of evidence than observational studies.

For some examples, see "Don’t fudge the facts on chocolate studies" and this column, "Epidemiology and how confounding statistics can confuse," by Marya Zilberberg, M.D., M.P.H.

One of the ways results can be skewed in an observational/epidemiological study is through confounding, when a factor affects both the independent and dependent variables in a study. Put more plainly, the confounder is the extra or underlying factor that can affect the outcome even if it’s also affecting the exposure or reason for the exposure. Confounding by indication occurs when the reason (the medical condition or circumstances) that someone takes a treatment is also the reason for an observed effect, rather than the effect being from the treatment.

For an example of confounding, consider observational studies that find that women who drink one or two glasses of wine during the second and/or third trimester of pregnancy have children with better outcomes – higher IQs, better academic achievement, better health, etc. – or at least no worse outcomes, than children born to mothers who drank no alcohol at all. However, a confounder here is the socioeconomic status and educational status of the women who tend to have a little wine: they are usually white, college-educated and in higher income brackets. Those women’s children are already more likely to have better outcomes than women from lower socioeconomic or educational backgrounds. Unless the researchers make adjustments to account for those factors, the results are not reliable.

Confounding by indication relates to the reason for the exposure and nearly always has to do with medication: why the medication was indicated. That is, could the reason a person took a certain medication be the reason for the outcomes observed instead of the medication itself? The easiest example is antidepressants and pregnancy. Thousands of studies look at pregnancy and infant outcomes when a mother takes antidepressants during pregnancy. Most find a very small increased absolute risk (about 1% to 3%) among women who take antidepressants. However, most of these studies compare women taking antidepressants to women not taking antidepressants regardless of mental state. Many of these studies did not consider why the antidepressants were indicated – usually a diagnosis of anxiety or depression – and whether that indication (the anxiety/depression) might contribute to poorer outcomes among those women’s children rather than the medication itself.

Some studies have compared women taking antidepressants to two groups of women not taking them: women with a mood/anxiety disorder and women without one. Most of these studies have found that women with a mood/anxiety disorder NOT taking antidepressants have similar pregnancy and infant outcomes as women who did take them. So is it the medication, the depression/anxiety or both that might contribute to a higher risk of certain outcomes? Or an entirely different factor such as a genetic predisposition for a mood/anxiety disorder that also affects an infant? When reading a study that looks at outcomes among people taking a certain medication (depression in women taking birth control, asthma in children exposed to acetaminophen in infancy, etc.), consider why the medication was indicated and whether that might affect outcomes.

A congenital disease, defect, abnormality, difference or other condition is one that has been present since birth. What’s important to know about congenital diseases or abnormalities is that they might be inherited or they might be de novo—spontaneous and new and not related to family history. Congenital conditions can have genetic causes such as an inherited disease or a new genetic mutation, including extra or fewer chromosomes, or environmental causes such as alcohol exposure during pregnancy or high levels of air pollution around a pregnant person.

Congenital conditions may be correctable or curable or may be lifelong disabilities or differences. They can also be as benign as a birthmark. They may be outwardly visible, such as a missing or extra finger, or not visible, such as a heart defect.

Context refers to the background information about a condition, treatment, and/or scientific question, and what the research to date has uncovered about the topic. It’s important for journalists to provide context when reporting on study results to avoid confusing their audience and avoid the appearance that different or conflicting results from different studies means science is “broken” or a different study is “wrong.” The most common examples of the frustration readers can feel with conflicting results given without context are findings from observational studies about the effects of coffee, chocolate or red wine, all of which have allegedly been shown to have health benefits and health risks, sometimes in ways that conflict.

No matter what study you’re covering, or what dramatic truth it seems to tell, the fact is that science doesn’t happen in a vacuum. Evidence came before; evidence will come after. No one study is ever the answer. It requires repetition of studies and retesting of hypotheses and research questions to gradually reach a consensus on a particular question.

However, doing so means that multiple studies will have different results, sometimes with small differences and sometimes with dramatically different or even opposite results. All this testing and refuting may be good for science, but it can be really bad for readers who are trying to make choices about their own health. Health reporters can help keep readers from getting whiplash with every headline by putting studies in context.

Think of context as showing readers the lay of the land. Here’s what doctors and patients have known about this idea in the past. Here’s what previous studies have shown. In a sense, you’re trying to give them an idea of how much certainty or skepticism to use as they read about the findings. Is there a mountain of evidence to back up the findings of the study you’re covering (smoking causes lung cancer), or is your study the kind of research that turns everything we thought we knew on its head (i.e. lower sodium diets may actually be bad for health)?

Many reporters who cover medical studies may start by working with just a copy of the study and perhaps a press release that goes along with it. Study authors usually provide some background in the introduction section of their studies. Press releases may offer some context, but keep in mind that the aim of the press release is to promote the study. Press releases may ignore previous evidence if it doesn’t support the validity of the research they’re selling.

When a medical journal flags a study as noteworthy, it will sometimes pair the study with an editorial or commentary, and these opinion pieces can be valuable sources of contextual information. That’s a good place to start, but it’s never a place to stop since commentaries are usually the opinion of just one or two people.

Medical journals also are increasingly aware that their audience extends beyond doctors and researchers. Some make an effort to quickly provide some contextual information about the study they’re presenting. The journal Pediatrics is an example. They post a little blue box at the top of each study letting readers know what came before and what the study adds. It looked like this on a recent study of background television exposure in young children:

Other sources you can use to find out the context of a study include PubMed, the searchable database of medical literature. Searching with a couple of key terms can help you find other recent studies on the same topics you’re covering, or you can look at the references of the study you’re covering for previous studies. Keep in mind that publishers may exclude studies that do not “fit” with their results, whether because they conflict or because they simply weren’t aware of those other studies.

PubMed also helps you quickly find research reviews, which summarize the body of literature available on a given question. To filter the reviews from your PubMed search, check “Systematic Reviews” under “Article Types.” It also may be helpful to interview the author of a recent review since they’re likely to be up to speed on the latest thinking on the subject you’re interested in.

The Cochrane Collaboration is another source of in-depth reviews on medical topics. You get free access to the entire Cochrane library as a benefit of AHCJ membership. Read more about how to sign up here.

A method of including participants (or data) that are convenient to reach but not randomly selected. It’s a type of “non-probability sampling” because each person does not have an equally random possibility of being selected to participate. Instead, little or no criteria is needed, and people (or data) are chosen because they are easy to reach, geographically convenient, or the first to arrive or show up in a search or at a clinic or study site.

Correlation is a relationship between two variables, and causation occurs when one of those variables has an effect on the other. A common mistake reporters make when writing about medical studies is confusing correlation and causation. Two variables in a study can be related without one actually being directly caused by the other.

For example, many people who suffer from alcohol use disorder also smoke cigarettes. Alcohol addiction and tobacco addiction are correlated, but one doesn’t cause the other. In a study that compares drinkers and non-drinkers, heavy drinkers would have higher rates of pancreatic cancer than non-drinkers. But it’s impossible to know if the cancer was caused by their drinking or by something else that made them different from the nondrinkers, such as higher rates of smoking.

Here’s another example of how correlation can cloud the interpretation of a study. The amount of sodium a person gets in their diet is closely correlated to the total calories they eat. In other words, the more a person eats, the more sodium they’re likely to take in. Eating a lot of calories often also leads to obesity. Both obesity and high-sodium diets are believed to contribute to high blood pressure. So what’s the primary driver of high blood pressure in a scenario like this: sodium or obesity? Those are the kinds of questions researchers try to disentangle in their studies.

Observational studies can only show correlation. They can’t show causation. When covering observational studies, it’s important to use language that makes the limits of the research clear.

Seasoned health reporters will eschew wording in their leads or headlines that reads like this: “A new study shows that short sleep may cause weight gain.”

Instead, they aim for wording that suggests a less direct relationship: “A new study shows that people who don’t get at least seven hours of sleep a night are more likely to gain weight compared to those who snooze less.”

That’s the most accurate way of describing the comparison that’s being made in the study, but it can also be a little wordy. Here’s another way that would work if you’re tight on space: “A new study shows short sleep is linked to weight gain.”

A covariate is a variable particular to each participant in a study (or each subject being studied, if it’s not an individual but rather, for example, a clinic) which could potentially influence the outcome.

The term covariate technically includes the independent variable(s) the researcher is specifically investigating, but most often, in practice, the term refers to potential confounding variables in a study, such as age, sex, income, education, underlying conditions or other characteristics particular to the research area. Covariates and confounders can overlap but are not the same thing.

All confounders definitely affect the outcomes whereas not all covariates do. In some studies, adjusting for covariates does not change the results, showing that those covariates were (usually) not influencing outcomes. If adjusting for covariates does change the results, then some of the covariates adjusted for are also confounders. Also, covariates are explicitly selected, assessed, recorded, and usually calculated in a study. Confounders, on the other hand, may be covariates that were considered in the study, or they may be other variables that were not considered.

Read more on how the term can become confusing here.

In a crossover trial, both groups are exposed to the intervention and to the placebo at different times, or both groups are exposed to an intervention but in a different order.

The purpose of using randomization to divide study participants into groups in a randomized controlled trial is to avoid bias in how the participants are assigned to an intervention or placebo.

But even with randomization, groups can end up being different enough to potentially influence the reliability of the results. For example, one group could end up with more comorbidities (e.g., a higher proportion of participants with Type 2 diabetes) or a significantly higher proportion of a certain demographic (e.g., more low-income patients). Or, a trial could be so small — perhaps only one or two dozen people — that even seemingly minor differences between the groups could affect the results.

In other trials, it may not be that the participants in different arms of a study are different but that receiving interventions in one order has different results than if the treatments had been administered in a different order. In any of these cases, researchers might use a crossover trial. In one type of crossover trial, one group (arm) receives the intervention while the other receives the placebo for the first half of the trial. Then, usually after a brief washout period, the groups switch. This study design is frequently used in diet studies, where individual differences can profoundly affect the results, and in trials where the passing of time itself might influence the results.

The other type of crossover study, in which interventions are administered to all study groups but in a different order, is common in cancer studies. One example is patients receiving radiation before chemo vs. chemo before radiation, which could have dramatically different outcomes. In these trials, there may not be a placebo group or traditional “control” group at all. Both the groups basically receive the same treatment, but the crossover design allows researchers to see if the sequence of interventions makes a difference.

A kind of observational study that lacks temporality, or a relationship with time. Cross-sectional studies gather data about their participants at one point in time. These studies can show relationships, or associations, between different factors, but they can't show which happened first.

Clinical trial are expected to be overseen by a Data and Safety Monitoring Board (also called Data and Safety Monitoring Committee), an independent group of experts who monitor, first and foremost, the safety of the trial participants as well as the efficacy of the intervention. DSMBs have access to all the data generated by a clinical trial as soon as it’s available, and the DSMB can recommend that a trial be paused or stopped entirely if the risks of the intervention appear to be too great for participants—such as a pattern of very severe adverse events—or the intervention appears so ineffective that the risks outweigh the benefits of completing the trial. The NIH guidelines on DSMBs offer a broad overview of what the group’s responsibilities and powers are, though this may vary by institution.

The term “de novo” literally means “of new” in Latin, but in it’s used most often in research to refer to the origins of something, especially a genetic mutation. Some genetic mutations, such as the BRCA genes related to breast and ovarian cancer, are inherited. Other mutations, however, are de novo—they originated on their own and are occurring for the first time in someone. A de novo mutation may also be called a de novo variant, new mutation or a new variant. De novo can also refer to a new protein design, the synthesis of molecules into a new molecule or the creation of a new gene.

A diagnostic clinical trial aims to identify better ways of diagnosing a condition, such as testing a new procedure, screening method, lab test, clinical assessment tool or similar intervention for identifying the condition. Most of these methods will also require validation to ensure they are consistent in identifying the condition.

A characteristic of a clinical trial in which neither the participants (the patients) nor the people administering the intervention (the clinicians giving participants the drug or other agent) know which intervention is real and which is the placebo. For example, in a double-blinded trial is testing a new antidepressant, none of the participants would be told whether they were receiving the antidepressant or the placebo, and the people giving participants the drug would not know which was which either. In practical terms, however, participants often quickly and easily figure out if they are the placebo group or the intervention group because of side effects or other effects from the drug, complicating placebo and nocebo effects and potentially introducing bias into the study.

A press embargo means that a journal article, research study content, announcement or other news item cannot be publicized in any way until a specified date and time, typically dictated by the source of the information. For medical and other scientific studies, the release date usually is the date of publication. An embargo provides journalists extra time to do sufficient reporting and write the article before the information becomes public knowledge. The value of advance access is that it potentially reduces mistakes as journalist rush to be the first to report the news. Organizations usually require journalists to agree to the embargo, which allows them (except in highly unusual situations) to seek outside comment but not to distribute the information widely or to anyone with a relevant investment stake (including the journalist, especially in cases insider trading may be an issue). Breaking an embargo can get a journalist barred from receiving future embargoed content from that source. The statement “For Immediate Release” at the top of a press release, email, study or other news item means the information is NOT embargoed (except in cases where the source makes a mistake).

In biology, an endemic species is one that is native to specific region, such as the kangaroo being endemic to Australia. The cane toad, on the other hand, was a species introduced to Australia and hence was not endemic (though it is now). In epidemiology, endemic refers to the circulation of a disease within a certain population or geographic area that continues without outside interference or introduction. For example, malaria is endemic to many parts of Africa. Although malaria was once present in the U.S., it would no longer be considered such. Once a disease has been completely eliminated from a geographic region, such as a continent, it is no longer endemic to that region. 

The endpoint of a study is an objective outcome the researchers measure when the study concludes to determine the level of benefit and/or risk of the intervention. Examples of endpoints might be survival months or the reduction of symptoms (usually measured according to an assessment tool or scale specific to the condition being studied). Other examples include side effects, level of immune response, shrinkage of tumor, lab results, quality of life improvement, financial savings, etc. Endpoints should be stated in the study’s objectives. Researchers always have a primary endpoint—the main outcome they’re studying—but may have secondary endpoints as well. Both primary and secondary endpoints can be surrogate endpoints, and sometimes a composite endpoint is reported.

Epigenetics refers to the study of how changes to genes during a person’s lifetime can then be passed on in some form to their offspring. In the early study of genetics, it was believed that a person’s genetics were set at birth, and whatever genes they were born with became the ones that were passed on to the next generation. However, researchers now understand that genes can be flipped on and off during a person’s life without the actual gene sequences changing. A wide range of environmental exposures can change this flipping on and off, called changes in gene expression. Examples of these exposures include age, diet, stress and life experiences, drugs, chemicals, environmental contaminants and physical activity. These changes in genetic expression can affect not only the individual’s risk of disease but also gene expression and risk of disease in their biological children and grandchildren.

The cause of a disease or condition; most often etiology refers specifically to the biological mechanisms underpinning a particular condition

These are demographic, health-related or other personal/individual factors that exclude a person from participating in a clinical study. They could be a person’s age, medications they’re taking, prior therapies they’ve received, comorbidities they have, or other factors that could bias or limit the results.

This lapse in responsible reporting refers to using outliers’ voices to state opinions that contradict the facts—or the currently accepted consensus based on the evidence—simply to provide “balance” to a story. It is also called false equivalence or “both-sidesism.” Stories about any topic certainly need to include as many perspectives on an issue as possible as long as those perspectives are purely opinion-based (something that science cannot show to be true or untrue either way) or those perspectives are supported by some scientific evidence, even if that evidence diverges from other evidence. However, if such a strong consensus from the evidence exists that something is regarded as a fact, then including a person who doesn’t believe that fact does not provide accurate or appropriate balance to a story — it just confuses the reader about what the facts are.

A flip example of false balance would be including a quote from someone who believes the earth is flat in a story related to weather or the curvature of the earth, or quoting someone who believes the moon landing was a hoax in a memorial story about the moonwalk. In reporting on medical research, the lines not to cross aren’t always as obvious. It becomes tricky because scientists are learning more information all the time, and it’s reasonable for journalists to seek countering opinions, particularly on new research, such as new findings about the gut microbiome or a new treatment. Other topics, such as breast cancer screening, may have contradictory evidence or involve controversial opinions on what to do about the evidence, all of which should be considered for a story.

One of the most common examples of a topic that falls prey to false balance, or false equivalency, is vaccines, most often among reporters who are less familiar with the health or science beat. The way the media’s falsely balanced vaccine reporting damaged public health reporting (and consequently public health) is such a well-worn case study that CJR featured outstanding coverage of it in Curtis Brainard’s Sticking with the Truth. Quoting “both sides” on concerns about a safety issue in vaccines that has been demonstrably shown not to exist makes it appear that there is a controversy among experts when there is not. The group Voices for Vaccines offers an excellent primer to false balance and how to avoid it in accurate news stories about vaccines.

The danger of false equivalence remains for any issue on which a broad medical or scientific consensus exists based on the evidence and a handful of outliers attempt to discredit that information for various reasons, often motivated by personal financial gain. Avoiding false balance doesn’t mean journalists take off their skeptical hat in covering these issues, but they should only report these scientifically outlier positions if solid evidence supports it, not just because someone somewhere believes it.

These are graphic representations of data from a meta-analysis, in which the researchers need to show the results of multiple different studies in a way that allows comparison of each individual study to the others. Hence it allows you to see “the forest” as well as each tree.

Each line is one study (usually with the authors and date included)  from the meta-analysis that also includes basic information about the study, such as the population size, the hazard ratio or odds ratio, a mean and/or standard deviation related to the results, etc. What’s included depends on what the authors are focused on or what they’re comparing. Sometimes a forest plot includes a column indicating the percentage weight it contributed to the overall findings of the meta-analysis, perhaps based on number of participants or some other characteristic. Then further to the right, each study is compared based on the common outcome measure used for the results (odds ratio, standard deviation, etc.). Squares indicate the result from each study with lines extending in either direction that represent the confidence interval or range of the results. A diamond is used on the forest plot to indicate where the overall findings of the meta-analysis fall (combining all of them).

A formulary can refer to an insurance formulary or a hospital formulary. A formulary in insurance terms is the list of prescription drugs that an insurance plan or prescription drug plan covers in its benefits. The drugs included in the formulary will usually determine what drugs a patient can be described unless they want to pay fully out of pocket. A formulary list is created by each individual payer, whether public or private, and periodically changes in response to new evidence, a drug becoming generic, a newly approved drug and similar events. The drugs in a formulary are usually grouped into tiers, often based largely on cost, that separate generics from brand-name drugs from boutique or specialty drugs. The drugs included in these tiers will also vary from one payer’s formulary to another.

A hospital formulary refers to the list of drugs available in that hospital that physicians have access to, often organized according to which medications are preferred first and then subsequently for particular conditions or clinical situations. The choice of what to include in a hospital formulary reflects clinical judgment of the most effective medications necessary to have available for all conditions regularly treated in that hospital.

Generalizability refers to the extent to which findings in a particular study can be applied or extended to populations beyond the population studied. The differences in populations could be age, sex, gender, health status, race, ethnicity, geography, marital status or any number of other possible ways to categorize populations. For example, extremely few studies on children could be generalized to adults and vice versa. This age restriction is built into the FDA drug approval process: a drug cannot be approved for age groups that were not studied in the clinical trials because there is no evidence that the safety and effectiveness could be generalized to groups outside the age ranges initially studied. Differences can be far more subtle, however. For example, the findings of a study of transitioned transgender children’s mental health in the greater Seattle area may not be able to be generalized to the mental health of their otherwise identical counterparts in rural Mississippi because the social acceptance and cultural environment of those different geographical areas could so greatly influence the mental health of this population. Similarly, findings in a group of patients with a specific condition, such as high blood pressure, cannot be presumed to apply to patients without that condition unless a different study provides evidence for it. Any characteristic that could differ across populations could be a barrier to generalizability of findings in both clinical trials and in observational studies.

An organism’s genotype is the specific genetic material that gives rise to that organism’s characteristics. It usually refers to the specific pairing of alleles (each contribution to the gene from either parent), but it’s sometimes used more generally/casually to refer to the entire genome or collections of genes. Often, researchers in basic science may compare phenotypes among organisms with differences in their genotypes to try to match how a particular gene does or does not affect an observable characteristic.

In medical research, gray literature refers to studies that have been conducted but have not been published in a peer reviewed medical journal. They may be referenced as conference abstracts or in technical or working papers, however, giving them a faint, but not fully transparent presence in the public domain. gray literature is typically not easy to search, and it lacks the full accounting of materials, methods, and results, required for publication in a peer-reviewed journal.

Hazard ratios, which are often abbreviated HR, are one way researchers report the relative effect of a drug, treatment, or exposure. A hazard ratio of 1 indicates no effect of an exposure or treatment. Hazard ratios over 1 indicate increased risks. Hazard ratios under 1 indicate decreased risk. Hazard ratios are similar to, but not exactly the same as Relative Risks, though they are often reported the same way.

This kind of bias may be at work in studies that find an unexpected benefit associated with treatment. It refers to the fact that people who are health conscious--they're more likely to get regular check-ups and more likely to comply with their doctors orders, to take their prescriptions as written, etc. - usually fare better, health-wise than those who do not or cannot. For example, years of observational studies concluded that seniors who got flu shots had half the risk of dying from any cause during the subsequent flu seasons compared to those who didn't get flu vaccines. Thus, researchers reasoned, flu shots appeared to be powerful way to slash an elderly person's death risk. But studies that dug a little deeper, examining individual medical records for other signs of health and frailty, found that seniors who got flu shots were simply healthier to begin with than those who didn't get their annual vaccines. It was healthy users who were surviving the winter months. Those studies found flu shots had little effect on overall survival.

Idiopathic describes a condition or symptom that occurs without a known cause or explanation. It’s typically used to describe conditions where there isn’t a genetic or other underlying mechanism already understood to explain why the condition occurred, such as “idiopathic scoliosis.” Sometimes an idiopathic diagnosis is identified by exclusion, meaning the researchers have ruled out all other likely explanations or conditions and are left with only one.

These are the factors that participants in a clinical trial or other medical study must have in order to enroll in the trial. If the trial is testing a therapeutic intervention, the inclusion criteria will include whatever condition the intervention is intended to treat and will usually include a definition or list of disease characteristics or severity that participants must meet. For example, people in a breast cancer trial may need to have a specific type of breast cancer and have already received two prior lines of therapy, or to be at a specific stage in their cancer.

This is the reason a drug, therapy, surgery or other intervention is recommended or prescribed by a doctor. A sign, symptom, condition or combination of these is the indication for a particular drug or intervention. For example, diabetes is an indication for insulin; insulin is indicated in people with diabetes. Labelled indications are those for which the FDA has officially approved a drug for a specific use. Off-label indications are uses of a drug that has been FDA-approved, but not for the purpose it’s

Informed consent is required for receiving any type of medical intervention, including drugs, surgeries or therapies, and for involvement in any type of human research. Each of these, however, are two different types of informed consent with different requirements. The US Department of Health and Human Services has a list of what’s required for informed consent to participate in human research, and Medline provides a list of what informed consent involves when it comes specifically to receiving medical care independent of research. Both of these concepts are explored in greater detail in the Key Concepts section. 

In a randomized, controlled trial, the intent-to-treat (ITT) population represents all the study subjects who were randomized to the different treatment groups. It's the most inclusive group in the study because it ignores people who drop out of the study or who don't comply with their treatments according to study guidelines. Analysis of the ITT population tends to make two treatments look more similar, while analysis of the per-protocol population tends to emphasize treatment differences. When results are similar for both the intent-to-treat and per-protocol populations, it increases confidence in the study results.

These graphs plot the proportion of individuals surviving without an event over the study period. Time is typically depicted on the horizontal axis, while the proportion of study participants surviving is on the vertical axis. A curve is plotted for each group in the study. Separation between the curves usually indicates differences in treatment effectiveness.

One advantage of Kaplan-Meier curves is that they are able to account for all patients in a study, even those who dropped out or were lost to follow up. They help doctors get an idea of median survival times, and they help researchers compare the effects of different treatments between groups in a study.

A kind of observational study that follows study participants over time. These studies take repeated measurements of the variables of interest and may last years or even decades. Because they measure changes over time, they can establish a sequence of events. But they can't definitively prove cause-and-effect.

The average of numbers, calculated by adding all the numbers together and dividing the sum by the number of items. The average age of study group would mean adding together all the participants’ ages and then dividing by the number of participants. Means can be deceptive if there are a few outliers that push the overall average up or down.

The middle number (midpoint) in a series of numbers. If the median age of breast cancer diagnosis is 62, that means half of the women diagnosed are over age 62 and half the women are under age 62.

A meta-analysis is a statistical technique for combining the results from independent studies that have all looked at the same question. It’s often used to assess the clinical effectiveness of treatments. The value of a meta-analysis depends on the quality of the studies included in the review.

Also called “minimally important difference” or in a slightly different form, “minimally clinically important improvement.” This term refers to the smallest amount of change or effect from a treatment that matters to a patient or would result in a change in a patient’s care. For example, if a doctor is contemplating changing a patient’s medication from drug A to drug B, the minimally clinically important difference might be a 10 percent reduction in risk or a 10 percent improvement in pain. If drug B only offers, say, an 8 percent  improvement, that falls below the threshold necessary for the doctor to make the change, which may especially be relevant if drug B’s side effects are more troublesome. MCID (or MID or MCII) is especially relevant for considering the clinical significance of a research finding.

Monotherapy means a person is taking only one medication to treat a particular condition. It generally refers only to the treatment of a specific condition, regardless of medications the person may be taking for other conditions. Monotherapy for psoriasis, for example, means the person is taking only one drug to treat psoriasis instead of multiple medications, but the same person may be taking blood pressure medication unrelated to the psoriasis.

These medical papers aim to explain the etiology, or origin, of a condition, its natural course and progression, and the range of prognoses that can result from it. These papers usually draw on a large existing evidence base about the condition, but they are also commonly drawn up soon after the emergence of a new disease to help clinicians understand what to expect with the disease. Then, as more is learned, new updated natural history studies are published. These studies are a great starting place for background and context if you’re about to cover a condition you’ve never covered before.

Instead of creating an intervention or designing an observational study in which there is interaction with the participants (surveys, measurements, interviews, etc.), a naturalistic study simply involves the researcher observing and recording the behavior, phenomenon or activity that they are studying. Naturalistic research is usually a type of field research, something employed frequently by zoologists, naturalists, ethologists and similar researchers. However, it may be used for qualitative study of medical conditions, such as a naturalistic study of autistic children in a specific everyday environment. 

This is a measure of accuracy for screening tests that refers to true negatives — the probability that subjects with a negative result test really do not have the disease or condition. So if a person is tested and receives a negative result, how likely is it that result is correct? How relieved can they be? Knowing the negative predictive value (NPV) can determine how many false negatives are occurring with a test.

The opposite of the placebo effect, a nocebo effect describes side effects or increased symptoms, rather than symptom improvement, that occur in people receiving a placebo. The nocebo effect is even less understood than the placebo effect but may relate to anxiety. See this link for more information.

This number is similar to number needed to treat (NNT) in the opposite direction: It is the number of people who need to receive an intervention (a medication, a surgery, a treatment, etc.) before one of them is harmed. Whereas a good NNT is a very low number – such as only two people taking a drug for one to benefit – a good NNH is a very high number, such as giving a medication to 1,000 people for one to experience an adverse event. The smaller the NNH, the more common adverse events are, and the riskier the intervention is.

NNH can also refer to removing a therapy. For example, anti-epileptic drugs for epilepsy can have negative long-term effects. In a systematic review, researchers looked for the appropriate timing for discontinuing medication without having patients suffer a relapse. The NNH for discontinuing medication was 8. For every 8 individuals who discontinued anti-epileptics, one would relapse and experience a seizure.

The number needed to treat, or NNT, is a way to sum up treatment effect, and unlike some statistical concepts, it’s wonderfully straightforward:  It’s simply the number of patients a doctor needs to treat to help just one person.

In observational studies, researchers look for differences between exposed and unexposed groups, after people have already made their own lifestyle or treatment choices. In observational studies, researchers have no control over who’s in the exposed or unexposed groups at the start of the study. As a result, there are often fundamental differences between the two groups that can cloud the nature of the relationships under study. These differences, called confounders, can sometimes be identified and controlled with adjustments to gathered data. But sometimes important confounders exist are never identified. This is why observational studies can’t prove cause-and-effect. They can only show associations.

For an example, see "Don’t fudge the facts on chocolate studies."

When a clinician prescribes a drug for any purpose or to a population other than what the U.S. Food and Drug Administration explicitly has approved and licensed that drug for, the medication is being used “off label,” literally a purpose not on the label issued by the FDA. Prescribing a drug off label is not illegal or even necessarily problematic. About half of all drugs prescribed to children are prescribed off label because no trials were conducted involving children, making it impossible for the FDA to approve the drug in that population. But decades of research on observational studies can be used to establish a drug’s safety and effectiveness either in new population not included in clinical trials or for a new purpose. For example, many mood stabilizers prescribed off-label for bipolar disorder were originally licensed as anti-epileptic medications for those with epilepsy. But it’s important to note that a drug’s off-label use means the FDA itself has reviewed the evidence for that drug’s safety and effectiveness either in the population receiving it or to treat the condition they have.

In an open label study, both the study participants/patients and the researchers/providers know what drug or treatment the participants are receiving. It’s the opposite of a blinded study, where one or both don’t know whether they are receiving the treatment being tested or a placebo. Open label studies can introduce bias into a study, but they also are sometimes used when it’s too difficult or expensive to disguise a drug, such as one that would be provided intravenously. Open label studies are also sometimes done so that the study can go on longer (patients will be more willing to take a drug for a condition they have if they know it’s the drug and not a placebo), thereby providing more information about effectiveness and safety.

In the simplest terms, pathogenesis describes how a disease begins and develops. In medical studies, researchers may discuss pathogenesis in a tangential sense, such as noting that the pathogenesis of a disease has yet to be understood, or in exploring possible biological mechanisms (including exposures in observational studies) that might explain how something might cause a particular disease. If you’re new to reporting on a specific condition, it can be especially helpful to look in PubMed for studies that specifically focus on the pathogenesis of that condition. Not only will such papers discuss risk factors and (any known) causes of the disease, but they will help you understand why and how those factors and causes contribute to the disease. Although pathogenesis is closely related to a disease’s etiology, there’s a subtle difference between the concepts. The etiology is the specific origin of a disease while pathogenesis is the full process of development, focusing more on the biological mechanism and how the disease proceeds.

The per-protocol population is the group of subjects in a randomized-controlled trial that most closely stuck to their treatment regimens. As far as investigators can tell, this is the group that took their medications as directed and came to study meetings for follow-up. The per-protocol group is a smaller subset of the intent-to-treat population.  In medical studies, analysis of the intent-to-treat population tends to make treatments look more similar, while analysis of the per-protocol population tends to emphasize treatment differences. When results are similar for both the intent-to-treat and per-protocol populations, it increases confidence in the study results.

Positron emission tomography, a type of medical imaging test that uses a radioactive dye that doctors can see moving through the body. The patient might swallow, inhale or receive an injection of the dye. What the medical provider looks for depends on the patient, their condition or suspected condition and the parts of the body being examined. PET scans are commonly used with cancer to look for drug response or disease progression or recurrence, but they are also used to assess heart or brain functioning, such as areas of areas of decreased blood flow in the heart or to look for evidence of Alzheimer’s in the brain.

Use of medications to treat a condition is pharmacotherapy. Two types of pharmacotherapy are polypharmacy — use of more than one drug at the same time — and monotherapy, use of only one drug at a time used to treat a particular position.

This term refers to the physical or otherwise observable characteristics of an organism or some aspect of an organism, such as size, shape, color, features, etc. Researchers may compare the phenotypes of organisms or of their organs, tissues, cells, cell organelles, etc., both to one another and to different genotypes, to learn about disease processes.

A placebo is a "fake" medicine or treatment intended to substitute for the real one, most commonly used for the control group in randomized trials. Placebos are not chemically or mechanically bioactive – they're not supposed to have any actual physical effect on the body – but they can work on the mind. Any parent who has kissed a "boo boo" or reluctantly "wasted" a bandage on an unbroken patch of skin that hurts has seen the placebo effect in action. “Sham” procedures (“sham surgery” or “sham acupuncture”) are the placebo version of non-medication interventions. The opposite is the nocebo effect. See this link for more information.

Polypharmacy refers to the use of multiple drugs, whether to treat a single condition or to treat multiple conditions (related or not). Polypharmacy may be necessary for many complex chronic conditions, such as autoimmune disorders, cancer, or severe mental health conditions. However, it can also be a concern because individuals may be prescribed more drugs than they need, the combined side effects could be harmful or burdensome, or potentially harmful drug interactions can occur. In some contexts, the term polypharmacy is used explicitly to indicate taking multiple drugs that are unnecessary for a condition.

This is a measure of accuracy for screening tests that refers to true positives — the probability that subjects with a positive result test truly do have the disease or condition. So if a person is tested and receives a positive result, how likely is it that result is correct? How worried do they need to be? Knowing the positive predictive value (PPV) can determine how many false positives are occurring with a test.

Practice guidelines are developed by a panel of experts, frequently convened as a group within a professional medical society, that outlines the most up-to-date best practices based on the current evidence. Government agencies and public or private organizations may also produce clinical guidelines. These guidelines, developed after an extensive review of current literature on specific clinical areas or conditions, are designed to help both healthcare providers and patients in making clinical decisions. As noted at The George Washington University’s Himmelfarb Health Sciences Library, “Good guidelines clearly define the topic; appraise and summarize the best evidence regarding prevention, diagnosis, prognosis, therapy, harm, and cost-effectiveness; and identify the decision points where this information should be integrated with clinical experience and patient wishes to determine practice.”

Pragmatic trials have a different purpose than explanatory trials, which typically include the usual randomized controlled trials, epidemiological studies and naturalistic studies. The goal of a pragmatic trial is to implement an effective intervention very soon, to introduce or change a specific government or company policy, or to otherwise inform decision-makers on a particular question or problem they are trying to address right now. A pragmatic trial includes studies that test the effectiveness of an intervention in a real-world population — anyone who comes through the door — instead of a carefully selected group of participants in a controlled situation. They tend to be more representative of most people who would receive the intervention, and they are easier to generalize.

A prospective study follows people forward in time. The advantage of prospective research is that researchers can pose a question and then design a study that will (hopefully) gather the best information to answer that question.

Think of a principal investigator (PI) of a clinical trial as similar to the producer of a film. The PI, usually the lead writer on the research grant, is in charge of the whole thing, including designing and carrying out the protocol and collecting, analyzing and reporting the data. Most universities have published guidelines describing the roles and responsibilities of the PI and co-investigators. Examples include this one from the University of Massachusetts Amherst, this one from Washington University in St. Louis, Missouri, and this one from Dartmouth.

Publication bias refers to differences between studies that get published in medical journals and those that do not. A 1991 study published in the Lancet compared published to unpublished research. Investigators found those studies that were positive, meaning they generated statistically significant results, were more than twice as likely to be published as those that did not find significant differences between the treatment and control groups.

The result of publication bias is that the body of studies available in medical literature may portray a drug or treatment as being more effective than it actually is. Systematic reviews of drug or treatment effects often try to control for publication bias. Publication bias is part of a larger group of factors that may skew study reviews called reporting biases.

Instead of testing how well a particular intervention treats a disease or its symptoms, a trial focused on quality of life assesses how much a person can continue to do and enjoy their typical everyday activities without substantial pain, hardship, emotional disturbance or other negative effects from the treatment. These trials are particularly important with chronic conditions and in oncology because their results provide useful information to providers and patients in making decisions about what therapies to try and what the risk-benefit trade-off might be. Increasingly, quality of life has become a common secondary endpoint in many trials, but trials focused primarily on quality of life as a primary endpoint remain important.

Trials that compare an intervention to two groups usually require randomization, where participants enrolled in the trial are randomly assigned to the intervention group or control group without prejudice. Randomization aims to ensure that one group does not end up with too many participants who share a characteristic with a greater frequency than participants in the other group. Table 1 in the study is where you can look to be sure characteristics are similar between groups. Randomization should be done by a computer or a pre-determined plan, such as assigning people to a different group as they enter the trial (first person in group A, second in B, third in A, fourth in B, etc., though this process may not be appropriate for all trials).

A randomized controlled trial, or RCT, is a specific kind of scientific experiment in which researchers screen and recruit people, then randomly assign them to a treatment or control group at the start of the study. In a double-blind trial, neither the researchers nor the study participants know which group they are in. This helps to reduce performance and treatment biases.  A placebo-controlled trial is one in which the control group takes a placebo, or a look-alike treatment that has no effect. High quality RCTs are considered to be the highest levels of scientific evidence, but they are also very expensive to stage and cannot always be done in an ethical way. For example, it’s unlikely that anyone would ever design a randomized controlled trial to test the effects of smoking on lung cancer, since assigning people to smoke would certainly harm their health.

This type of bias refers to a research participant’s difficulty in accurately remembering information they are asked for in a retrospective study. For example, in a retrospective study on alcohol exposure during pregnancy, women may be asked about their alcohol intake during pregnancy much later, such as months after they have given birth. By then, they may not be able to recall their alcohol consumption as accurately as if they had been asked in a prospective study going on during their pregnancy. Another way recall bias might interfere in a case control study is that cases — those with a condition — may be more likely to remember their exposure to a particular substance or experience than those in the control group who do not have the condition being studied. Someone with asthma, for example, may more easily recall the last time they sat in stopped highway traffic than someone without asthma and therefore without as much reason to remember a circumstance that might worsen a condition they don’t have.

Relative risk, usually abbreviated RR, is a comparison of risk levels between two groups in a study, usually the treatment and the control group. Relative risks are similar to, but not exactly the same as hazard ratios, though they are often reported the same way. A relative risk of 1 indicates no change in risk between the exposed group and the control group. Relative risks over 1 indicate that a treatment or exposure increases risk. Relative risks under one indicate decreased risks. For example, a relative risk of 2 indicates a doubling of average risk. While a relative risk of .75 means that that the average risk dropped by 25 percent. 

Also called reverse causation, this becomes a possibility when the "effect" of something could actually be its cause. For example, some studies have observed that diet soda drinkers are more likely to be obese than people who don't drink diet soda. That's led to speculation that artificial sweeteners in diet drinks may somehow cause obesity. Critics of that theory, however, have pointed out that people who are gaining weight may switch to diet drinks as a way to cut calories. Thus, they argue, the tie to obesity may be an example of reverse causality. Jodi Beggs has some funny examples of reverse causality on her blog, “Economists Do It with Models.”

Retrospective studies are observational studies that look back in time.  In retrospective studies, researchers start with a population that's already experienced a given outcome, such as cancer, and try to go back in time to find exposures that make have contributed. In many cases, data used in retrospective studies was generated before the study was conceived, so it isn't always a perfect fit. And researchers may not have all the information they need to definitively answer a question. In other cases, like outbreak investigations, researchers rely on retrospective data gathering to help them determine the source of an illness. They start with sick people and work back in time to find the source of the illness.

A commonly used effect size used to quantify research findings is a risk ratio, another word for relative risk. The risk ratio compares the risk of a disease or outcome in one group to risk of the same outcome in another different group. Risk ratios are not the same as odds ratios, though both are commonly used in cancer studies. If the risk ratio is 1, the risk is the same in the two groups being compared. For example, if the risk of metastasis is 30% in those taking drug A and 30% in those taking drug B, then 30/30 = 1. A risk ratio greater than one indicates an increased relative risk: 1.33 translates to 33% greater risk. A risk ratio less than one indicates a lower risk: 0.75 translates to a 25% lower risk. If you use risk ratio/relative risk in your reporting, try to include absolute risk as well.

Sometimes called a run-in phase, a run-in period describes the period of time before the start of a clinical trial, before the participant receives or participants in any intervention. Data from run-in periods are used for two reasons: to screen out participants who are not eligible based on conditions or exclusions or who show that they are unwilling or unable to be compliant with the intervention, or as a “washout” period to allow the effects of a previous intervention to fade before starting a new one. 

A safety signal is any trend, pattern, set of symptoms or other indicator that a drug or intervention may have an adverse event that requires more examination. Some surveillance systems are specifically set up for the purpose of looking for safety signals, such as the Vaccine Adverse Event Reporting System. If a significant number of people report the same adverse event after administration of the same vaccine, that’s a signal that may require additional research to understand. Safety signals can be known or previously unknown adverse events or side effects.

Scoping reviews are not brand new, but for reasons that are unclear, they seem to be becoming more common. Reporters may encounter them with increasing frequency and wonder what purpose they might serve for journalists. Their greatest use is providing context and understanding of the parameters of a given topic. For example, they might cover the scope of what’s been published about how the most recent blood pressure guidelines are being put into practice. In this way, they are primarily informational and a good way to gain, well, a full scope of the current status of a subject area. They tend to be descriptive and fall short of the more focuses analysis of a systematic review.

In addition to the primary endpoint reported in a study, researchers may measure and report secondary endpoints as well. These are not (supposed to be) the main outcomes of the study but provide additional information to consider in balancing the benefits and risks of an intervention, such as side effects, cost savings or surrogate endpoints such as amount of tumor shrinkage or reduction of specific symptoms. Secondary endpoints are often, but not always, listed in the study objectives with the primary endpoint. Two things journalists should look for when looking at secondary endpoints: a) Were any of them originally a primary endpoint as noted in the clinicaltrials.gov study listing? b) Do the study authors, or the press release writers, focus more on the secondary endpoint results than the primary endpoint results if the primary endpoints did not achieve the desired effect? Sometimes authors and/or press release writers will distract from a negative finding in the primary endpoint by focusing more on the secondary endpoints, but this practice should raise a red flag.

A way of measuring the accuracy of a screening, diagnostic, monitoring or other test in terms of how many people who test positive actually are positive. Sensitivity is therefore the true positive rate. The higher a test’s sensitivity is, the less likely false positives are.

This is a fancy word for all the effects or complications, typically long-term, that occur as a result of a disease, trauma, injury or sometimes of a therapy/intervention/treatment. Sequelae (a plural word) differs from symptoms in that symptoms are features or characteristics of a disease while it’s occurring. Sequelae are what follow afterward, even after recovery, and generally refers to a separate (often chronic) condition or set of symptoms that are distinct from the original condition but were also caused by it.

For example, common symptoms of concussion are loss of vision, blurry vision, nausea, dizziness, etc. But possible long-term sequelae of concussion include loss of short-term memory, headaches, nerve damage, reduced focus/concentration, irritability, etc. Sometimes later sequelae may be used to retroactively diagnose an earlier disease or injury that had been missed.

A measure of a screening, diagnostic, monitoring or other lab test’s accuracy in terms of the true negative rate — that is, the number of people with a negative test result who truly are negative. High specificity means a higher likelihood that a positive result really is a true positive.

Surveillance refers to how researchers and public health officials identify, locate, count, and track a particular disease or other condition. Surveillance is used to understand a disease’s epidemiology, including its spread, its risk factors, and groups with the highest risk. It’s also used as a tool for identifying and implementing preventive or treatment-related interventions and, as the WHO put it, documenting “the impact of an intervention, or track progress towards specified goals.” For example, worldwide polio surveillance is vital to the goal of eradicating polio through vaccination.

A systematic review is a type of study that comprehensively review all other relevant studies on a specific research question or clinical topic. On the hierarchy of evidence — the weakest evidence to the strongest evidence in terms of methodology — systematic reviews are second highest, just under meta-analyses. During a systematic review, researchers review and combine all the information from published and unpublished studies they have identified that meet pre-defined criteria. They involve five steps: framing a question; identifying the relevant research; assessing the quality of the studies; summarizing the evidence; and interpreting the findings.

In nearly every clinical trial or observational study, the researchers provide the baseline characteristics of the study participants in Table 1. This table will include any demographics the researchers recorded — age, geography, insurance status, income status, race/ethnicity, etc. — and any other characteristics that were relevant to the recruitment or randomization of the participants, such as comorbidities, severity of disease or prior exposure to a treatment.

In a randomized controlled trial, Table 1 ideally has no statistically significant differences between groups, and it’s worth skimming it to see if any differences do exist. If they do, the authors should address it in their methods and/or limitations section. In observational trials, some statistically significant differences between groups may be unavoidable, depending on the populations, recruitment methods and what’s being studied, but the authors should still address how they adjust for these differences in their methods and/or limitations. Table 1 is also a useful for seeing what ISN’T there — what characteristics/baseline data did the authors not collect or consider that they should have or that might have confounded the results?

Another time it may be important to look at Table 1 is in meta-analyses or systematic reviews where individual populations across trials might be quite different. Reviews/meta-analyses do not always include a Table 1 of participants across trials, but they should at least address how homogenous or heterogenous the study populations were and the potential significance of those differences.

In translational or applied scientific studies, researchers use a body of scientific knowledge to solve a practical problem. For example, basic research identified a gene that causes a common form of the blood clotting disorder hemophilia. In a subsequent translational study, researchers used a virus to replace the broken gene in human cells, successfully treating six patients with the disease.

A category of adverse events that can particularly occur with cancer or autoimmune condition treatments during a clinical trial is the treatment emergent adverse event. This is an often unexpected adverse (negative) outcome or event that arises during the course of treatment that did not appear to exist beforehand or appears to be worsening a pre-existing condition or problem. Whereas adverse events may or may not be related to a treatment, a TEAE is distinguished by its appearing specifically while treatment is ongoing or very soon thereafter, often with an infusion therapy or a treatment that requires multiple visits over time.

Being treatment-naive means that an individual has not yet received any treatment for a particular condition. A treatment-naive person who is HIV-positive, for example, is someone who has not started anti-retroviral therapy. The term “naive” can also be paired with a particular drug to identify a patient who has not received treatment with that particular drug (or class of drugs). Someone with major depression who is SSRI-naive, for example, has not taken any selective serotonin reuptake inhibitors (SSRI), though they may have taken a different antidepressant. Someone who is “antipsychotic-naive” has not taken any antipsychotics, but they may have taken something else to treat psychosis. Naive can refer to non-medication interventions as well, but it’s most often used with drugs or an overall treatment regimen (drugs plus another intervention, such as psychotherapy for mental health conditions).

A vector is any agent or intermediary that carries and transmits a pathogen from one individual to another. Common vectors of infectious disease include insects, such as mosquitos and fleas, and arachnids, such as ticks or spiders. However, plants and fungi can also be vectors.

A washout period can describe two scenarios:
a) the run-in period before a study begins during which researchers are waiting for a previous drug or intervention’s effects to wear off before they start a new one (to avoid confounding between the two interventions), or
b) the period between two different interventions when researchers are waiting for the participants’ bodies to normalize after the first intervention before beginning the next one.

An example of the latter might be a study in which people participate in a strictly defined sleep or exercise or diet routine for a couple of weeks and then, after a washout period of perhaps two weeks, participates in a different strictly defined sleep or exercise or diet routine to compare the effects of each one in each person (a type of self-controlled case series design or cross-over study).