부산대학교 도서관

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.
Comment: Code for simulations and R package is available on https://github.com/abcdane1/PtSaceCrts