Abstract Number:
3014
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Paper
Participants:
Michael Fay (1), Fan Li (2)
Institutions:
(1) National Institute of Allergy and Infectious Diseases, N/A, (2) Yale School of Public Health, N/A
Co-Author:
Fan Li
Yale School of Public Health
First Author:
Michael Fay
National Institute of Allergy and Infectious Diseases
Presenting Author:
Michael Fay
National Institute of Allergy and Infectious Diseases
Abstract Text:
Hazard ratios are often used to describe a treatment effect in randomized trials, but their causal interpretation is not straightforward. We discuss hazard ratios in the context of potential outcomes. We first review two classes of causal estimands. An individual-level estimand compares potential outcomes within each subject, then summarize those pairwise comparisons over the population. A population-level estimand summarizes the marginal distribution of each potential outcome first, then compares those marginal distributions. Difference-in-means estimands are both individual-level and population-level estimands, but hazard ratios are typically only population-level estimands. Practitioners rarely make a distinction between the two estimands, and as a result often confuse the causal meaning we can get from hazard ratio estimators from randomized trials. We argue that the population-level hazard ratio causal estimand is useful, but care must be made in its interpretation. This care is especially important when it appears that the hazard ratios are changing over time. We highlight this issue with an example interpreting COVID-19 vaccination efficacy over time.
Keywords:
Cox regression|estimand|causal inference|randomized trial|proportional hazards|
Sponsors:
Lifetime Data Science Section
Tracks:
Miscellaneous
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