“Dose response” refers to the relationship between the amount of a substance or exposure and the extent or magnitude of an outcome or effect. It’s something we already intuitively understand with everyday experiences: the more calories you consume, the more weight you gain over time. The less water you drink, the more dehydrated you become. The more miles you drive (exposure), the more gas you use (effect). In medical studies, examining the dose response relationship may help provide evidence supporting or refuting a causal relationship between an exposure/substance and an outcome/effect.
Deeper dive
A study may find an association between an exposure and an outcome, but it may not be clear yet if the association is cause-and-effect. A dose response effect is a phenomenon to look for in the study. It’s most commonly seen in pharmaceutical or toxicology studies, such as the decreasing IQ points that accompany increasing levels of lead exposure. In toxicology, dose response is basically the technical term for “the dose makes the poison.” In pharmaceutical studies, dose response curves can illustrate the proportion of the study population that respond to the medication (and the extent to which they respond).
Although a clear dose-response relationship — x follows a predictable curve with y — cannot show causation on its own, it’s part of the nine criteria necessary to conclude from observational studies that an exposure and an associated outcome are, indeed, causally linked when randomized controlled studies are not available or ethical. For example, it’s now established that smoking causes lung cancer. The more a person smokes (amount and duration), the higher their risk for lung cancer is. That dose-response is part of what helped scientists determine the causal link. These curves typically taper off at the low end (one lifetime cigarette isn’t that much different than 10 lifetime cigarettes) and at the high end (smoking three packs a day isn’t that much different than four packs a day).
One value of understanding dose response is that NOT seeing it may indicate a flaw in a study or another explanation for an association. If substance X and outcome Y are related to each other statistically, but Y doesn’t increase as X does (maybe it stays the same or has no clear pattern), the lack of a dose response could be a red flag. For example, say you are provided the following information in a study about a toxic substance and tumors occurring in mice:
- 5 mg of the substance is linked with 6 malignant tumors.
- 10 mcg, 3 malignant tumors.
- 15 mcg, 7 malignant tumors.
- 20 mcg, 1 malignant tumor.
- 25 mcg, 5 malignant tumors.
If you were to graph those numbers, the line would go up and down without a clear pattern — no dose response relationship. However, it’s important to realize that not all dose response relationships are linear or inversely linear. Some substances have a different dose response curve, such as endocrine disruptors, which typically have an upside down U-shaped dose response. A very low and a very high exposure may have similar modest effects where an exposure somewhere in the middle has the greatest effect. It’s therefore important to be aware of what the natural dose response curve should look like for the type of substance/exposure the study includes.
Double-blinded (sometimes called double-masked)
Double blinding is 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 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.