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Author ORCID Identifier
https://orcid.org/0000-0002-1093-7097
AccessType
Campus-Only Access for Five (5) Years
Document Type
dissertation
Degree Name
Doctor of Philosophy (PhD)
Degree Program
Public Health
Year Degree Awarded
2021
Month Degree Awarded
September
First Advisor
Laura B Balzer
Second Advisor
David Benkeser
Third Advisor
Leontine Alkema
Subject Categories
Biostatistics
Abstract
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.
DOI
https://doi.org/10.7275/23472542
Recommended Citation
Yang, Guandong, "Targeted Learning for Effect Modification in Randomized Clinical Trials and Cluster Randomized Trials" (2021). Doctoral Dissertations. 2386.
https://doi.org/10.7275/23472542
https://scholarworks.umass.edu/dissertations_2/2386