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Causal Interpretation of Hazard Ratios from Randomized Experiments

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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