Estimation of Population Average Treatment Effects in the FIRST Trial: Application of a Propensity Score-Based Stratification Approach

Objective/Study QuestionTo 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 SettingSecondary data from the National Surgical Quality Improvement Program and the FIRST Trial (2014-2015). Study DesignThe 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 MethodsThis study was a secondary analysis of previously collected data. Principal FindingsAlthough 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. ConclusionsPropensity score-based poststratification can be valuable tools to address trial generalizability but may yield imprecise estimates of PATE when sparse strata exist.