Biostatistics Dissertation Defense - Carrie Caswell

Abstract: Measurement error and missing data are two phenomena which prevent researchers from observing essential quantities in their studies. Measurement error occurs when data are subject to variability which masks an underlying value. Recognition of measurement error is essential to preventing bias in an analysis, and methods to handle it have been well-developed in recent years. However, in time-to-event analyses, competing risks is another important consideration which can invalidate study results if not properly accounted for. Current methods to accommodate competing risks do not account for measurement error, and, as a result, incur a large amount of bias when using covariates measured with error. We first propose a novel method which combines the intuition of the subdistribution model for competing risks with risk set regression calibration, which corrects for measurement error in Cox regression by recalibrating at each failure time. We show through simulations that the proposed estimator removes bias that occurs when measurement error is ignored. The second part of this dissertation addresses missing outcome data in longitudinal models. We propose a novel method to account for missing longitudinal outcome data in the situation where some patients have no recorded outcomes. We accomplish this through use of an auxiliary outcome available for all patients, and we avoid the pitfall of misspecification by estimating its relationship with the true outcome data nonparametrically. We show that this method is more efficient than conventional methods and robust to misspecification. For both proposed methods, we develop asymptotic theory and provide consistent variance estimates. We also apply both proposed methods to neurodegenerative disease data. Finally, we introduce an R package to implement the first proposed method and make it widely available for regular use.  Dissertation Advisor: Sharon Xie, PhD Committee Chair: Warren Bilker, PhD Committee: Murray Grossman, MD, EdD, Justine Shults, PhD, Lauren Massimo, PhD, CRNP