  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) 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
true treatment effects: 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.
