UC Berkeley

This dissertation develops modern statistical methods, targeted maximum likelihood estimation (TMLE) and ensemble machine learning, for two common problems in epidemiology and public health: 1) estimating the population size based on capture recapture designs, and 2) estimating the continuous and discrete treatment effect of multiple exposures. For the first problem, We proposed novel target parameters for each identification assumption and robust estimators based on TMLE, provided the efficient influence curves for each the parameter, proved the statistical properties of the estimators, and applied them to data collected from national-level infectious disease surveillance systems. We also used simulations with identification assumption violations to test the reliability of the estimation. For the second problem, we developed our target parameter and TMLE estimators and applied them to various types of empirical data collected by community-level follow-up surveys and state-level electronic health record data. We provided simulations and sensitivity analysis to show the performance of the TMLE estimators compared to existing ones. In chapter 1, we gave an introduction to TMLE, the road map of targeted learning, and a more detailed summary of the following chapters. In chapter 2, we developed a novel approach to estimate the population size based on capture recapture designs and evaluated the estimation reliability. In chapter 3, we utilized the targeted learning approach to assess the performance of a diabetes care program on glycemic control of type 2 diabetes patients, and identified patient subgroups with most successful treatment effects. In chapter 4, we proposed a robust variable importance measure based on TMLE and applied it in estimating the transmission effects of mother’s eating behavior on the next generation.