Outcomes truncated by death in RCTs: a simulation study on the survivor average causal effect
arxiv(2023)
摘要
Continuous outcome measurements truncated by death present a challenge for
the estimation of unbiased treatment effects in randomized controlled trials
(RCTs). One way to deal with such situations is to estimate the survivor
average causal effect (SACE), but this requires making non-testable
assumptions. Motivated by an ongoing RCT in very preterm infants with
intraventricular hemorrhage, we performed a simulation study to compare a SACE
estimator with complete case analysis (CCA, benchmark for a biased analysis)
and an analysis after multiple imputation of missing outcomes. We set up 9
scenarios combining positive, negative and no treatment effect on the outcome
(cognitive development) and on survival at 2 years of age. Treatment effect
estimates from all methods were compared in terms of bias, mean squared error
and coverage with regard to two estimands: the treatment effect on the outcome
used in the simulation and the SACE, which was derived by simulation of both
potential outcomes per patient. Despite targeting different estimands
(principal stratum estimand, hypothetical estimand), the SACE-estimator and
multiple imputation gave similar estimates of the treatment effect and
efficiently reduced the bias compared to CCA. Also, both methods were
relatively robust to omission of one covariate in the analysis, and thus
violation of relevant assumptions. Although the SACE is not without
controversy, we find it useful if mortality is inherent to the study
population. Some degree of violation of the required assumptions is almost
certain, but may be acceptable in practice.
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