Causal interpretation of the hazard ratio in randomized clinical trials.
Clin Trials
; : 17407745241243308, 2024 Apr 28.
Article
in En
| MEDLINE
| ID: mdl-38679930
ABSTRACT
BACKGROUND:
Although the hazard ratio has no straightforward causal interpretation, clinical trialists commonly use it as a measure of treatment effect.METHODS:
We review the definition and examples of causal estimands. We discuss the causal interpretation of the hazard ratio from a two-arm randomized clinical trial, and the implications of proportional hazards assumptions in the context of potential outcomes. We illustrate the application of these concepts in a synthetic model and in a model of the time-varying effects of COVID-19 vaccination.RESULTS:
We define causal estimands as having either an individual-level or population-level interpretation. Difference-in-expectation estimands are both individual-level and population-level estimands, whereas without strong untestable assumptions the causal rate ratio and hazard ratio have only population-level interpretations. We caution users against making an incorrect individual-level interpretation, emphasizing that in general a hazard ratio does not on average change each individual's hazard by a factor. We discuss a potentially valid interpretation of the constant hazard ratio as a population-level causal effect under the proportional hazards assumption.CONCLUSION:
We conclude that the population-level hazard ratio remains a useful estimand, but one must interpret it with appropriate attention to the underlying causal model. This is especially important for interpreting hazard ratios over time.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Clin Trials
Year:
2024
Document type:
Article