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Estimation of Population Average Treatment Effects in the FIRST Trial: Application of a Propensity Score-Based
Jeanette W Chung1, Karl Y Bilimoria1, Jonah J Stulberg1
1Department of Surgery, Surgical Outcomes and Quality Improvement Center, Feinberg School of Medicine, Northwestern University, Chicago, IL.
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.
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