Individual-level and population-level causal estimands in randomized clinical trials

Speaker

Michael P Fay, PhD. Mathematical Statistician at the National Institute of Allergy and Infectious Disease, USA.

Abstract

Randomized trials are one of the best ways to establish causal effects without making strong untestable assumptions. Although randomization can ensure that the apparent causal effect is not due a confounding factor that affects both the treatment choice and the response, the interpretation of the causal estimand is sometimes not straightforward. To avoid some common misinterpretations of causal estimands from randomized trials, I discuss two overlapping classes of estimands: individual-level and population-level causal estimands. The individual-level causal estimand first compares potential outcomes on each of the two treatment arms within an individual, then summarizes those comparisons across a population. In contrast, the population-level causal estimand first summarizes the marginal distribution of each of the two potential outcomes, then compares the two summaries. Difference-in-means estimands are members of both classes, but some other common estimands (e.g., the Mann-Whitney parameter or the hazard ratio) are only population-level estimands and are often causally misinterpreted as individual-level estimands. I discuss these issues using a placebo-controlled randomized vaccine trial as an example.

More information

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