Estimation of Population Average Treatment Effects in the FIRST Trial: Application of a Propensity Score‐Based Stratification Approach
利用FIRST试验数据，通过倾向得分分层法估计住院医师工作时间政策变化对患者和住院医师结局的样本与总体平均处理效应，发现总体估计因稀疏分层而不精确。
OBJECTIVE/STUDY QUESTION: To estimate and compare sample average treatment effects (SATE) and population average treatment effects (PATE) of a resident duty hour policy change on patient and resident outcomes using data from the Flexibility in Duty Hour Requirements for Surgical Trainees Trial ("FIRST Trial"). DATA SOURCES/STUDY SETTING: Secondary data from the National Surgical Quality Improvement Program and the FIRST Trial (2014-2015). STUDY DESIGN: The FIRST Trial was a cluster-randomized pragmatic noninferiority trial designed to evaluate the effects of a resident work hour policy change to permit greater flexibility in scheduling on patient and resident outcomes. We estimated hierarchical logistic regression models to estimate the SATE of a policy change on outcomes within an intent-to-treat framework. Propensity score-based poststratification was used to estimate PATE. DATA COLLECTION/EXTRACTION METHODS: This study was a secondary analysis of previously collected data. PRINCIPAL FINDINGS: Although SATE estimates suggested noninferiority of outcomes under flexible duty hour policy versus standard policy, the noninferiority of a policy change was inconclusively noninferior based on PATE estimates due to imprecision. CONCLUSIONS: Propensity score-based poststratification can be valuable tools to address trial generalizability but may yield imprecise estimates of PATE when sparse strata exist.