Infants with hypoplastic left heart syndrome require an initial Norwood operation, followed some months later by a stage 2 palliation (S2P). The timing of S2P is critical for the operation’s success and the infant’s survival, but the optimal timing, if one exists, is unknown. We attempt to estimate the optimal timing of S2P by analyzing data from the Single Ventricle Reconstruction Trial (SVRT), which randomized patients between two different types of Norwood procedure. In the SVRT, the timing of the S2P was chosen by the medical team; thus with respect to this exposure, the trial constitutes an observational study, and the analysis must adjust for potential confounding. In Chapter 1, we propose an extended propensity score analysis that describes the time to surgery as a function of confounders in a discrete competing-risk model. We then apply inverse probability weighting to estimate a spline hazard model for predicting survival from the time of S2P. In Chapter 2, we address same question by multiply imputing the potential post-S2P survival outcomes with a lognormal model under the Rubin Causal Model framework. With this approach, it is straightforward to estimate the causal effect of S2P timing on post-S2P survival by directly comparing the imputed potential outcomes. We examine the sensitivity of these results by applying a more flexible model that assumes proportional hazards as a function of S2P time, with a restricted cubic spline (RCS) for the baseline hazard. Our analysis suggests that S2P conducted at 6 months after the Norwood gives the patient the best post-S2P survival. In Chapter 3, we build a new 10-year ASCVD (atherosclerotic cardiovascular disease) risk prediction model for Veterans Affairs (VA) women based on data from the VA national EHR (Electronic Health Records) database.
Daniel F. Heitjan
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Chen, Xiaofei, "Causal Inference and Prediction on Observational Data with Survival Outcomes" (2020). Statistical Science Theses and Dissertations. 16.