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Author ORCID Identifier
Campus-Only Access for Five (5) Years
Doctor of Philosophy (PhD)
Year Degree Awarded
Month Degree Awarded
Laura B Balzer
In both individually randomized trials and cluster randomized trials, interventions often have differential effects depending on baseline characteristics, such as age or prevalence. Traditionally, effect modification has been examined with subgroup analyses or inclusion of cross-product terms in a parametric regression framework. In the first chapter, we develop a causal framework for evaluating effect modification in the context of sieve analyses in individually randomized vaccine trials. The second chapter, we present an R package for implementing targeted minimum loss-based estimation (TMLE) to assess effect heterogeneity with time-to-event data in the presence of competing risks and time-dependent confounding. In the third chapter, we focus on using TMLE to quantify effect modification in cluster randomized trials with few independent units.
Yang, Guandong, "Targeted Learning for Effect Modification in Randomized Clinical Trials and Cluster Randomized Trials" (2021). Doctoral Dissertations. 2386.