Causal Survival Analysis from Theory to Practice

Abstract

Causal survival analysis consists in estimating the effect of a treatment on time-to-event outcome(s). We focus on estimating the restricted mean survival time (RMST), the average survival time from baseline to a pre-specified time, between treated and control groups on right-censored data from an observational study. After stating the identifiability conditions, we review and compare different causal estimation methods, both parametric and non-parametric, which require modeling of the propensity score, the survival outcome and the censoring. We illustrate these methods on observational clinical data to answer a medical question about the effect of transfusion on one-year mortality for patients in intensive care. We discuss the interpretability of the findings of this study from a methodological point of view and explain the methodological challenges for causal survival analysis raised by this study (missing values, selection of the study population, selection of the adjustment variables) in the perspective of guiding future practitioners.

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Senate House, University of London, London
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Imke Mayer
PhD, Research Scientist in Statistics and Causal Inference