  Patient-centered outcomes, such as quality of life and length of hospital
stay, are the focus in a wide array of clinical studies. However, participants
in randomized trials for elderly or critically and severely ill patient
populations may have truncated or undefined non-mortality outcomes if they do
not survive through the measurement time point. To address truncation by death,
the survivor average causal effect (SACE) has been proposed as a causally
interpretable subgroup treatment effect defined under the principal
stratification framework. However, the majority of methods for estimating SACE
have been developed in the context of individually-randomized trials. Only
limited discussions have been centered around cluster-randomized trials (CRTs),
where methods typically involve strong distributional assumptions for outcome
modeling. In this paper, we propose two weighting methods to estimate SACE in
CRTs that obviate the need for potentially complicated outcome distribution
modeling. We establish the requisite assumptions that address latent clustering
effects to enable point identification of SACE, and we provide
computationally-efficient asymptotic variance estimators for each weighting
estimator. In simulations, we evaluate our weighting estimators, demonstrating
their finite-sample operating characteristics and robustness to certain
departures from the identification assumptions. We illustrate our methods using
data from a CRT to assess the impact of a sedation protocol on mechanical
ventilation among children with acute respiratory failure.
