The cause and effect paradigm underlying medical research has led to an enhanced etiological understanding of many diseases and the development of many lifesaving drugs, but the paradigm does not always include an understanding of the pathways involved. Causal mediation analysis extends the cause and effect relationship to the cause and effect through a mediator, an intermediate variable on the causal pathway. The total effect of an exposure on an outcome is decomposed into two parts: 1) the indirect effect of the exposure on the outcome through the mediator and 2) the direct effect of the exposure on the outcome through all other pathways. In this dissertation, I describe various counterfactual causal mediation frameworks with identifiability assumptions that all lead to the Mediation Formula. The indirect and direct effects can be estimated from observable data using a semi-parametric algorithm derived from the Mediation Formula that I generalize to different types of mediators and outcomes. With an increased interest in causal mediation analysis, thoughtful consideration is necessary in the application of the Mediation Formula to real-world data challenges. Here, I consider three motivating causal mediation questions in the areas of HIV curative research and cardio-respiratory fitness. HIV curative treatments typically target the viral reservoir, cells infected with latent HIV. Quantifying the effect of an HIV curative treatment on viral rebound over a set time horizon mediated by reductions in the viral reservoir can inform future directions for improving curative treatments. In cardiorespiratory fitness research, metabolites, molecules involved with cellular respiration, are believed to mediate the effect of physical activity on cardiorespiratory fitness. I propose three novel adaptations to the semi-parametric estimation algorithm to address three data challenges: 1) Numerical integration and optimization of the observed data likelihood for mediators with an assay lower limit (left-censored mediators); 2) Pseudo-value approach for time-to-event outcomes on a restricted mean survival time scale; 3) Elastic net regression for high-dimensional mediators. My novel approaches provide estimation frameworks that can be applied to a broad spectrum of research questions. I provide simulation studies to assess the properties of the estimators and applications of the methodologies to the motivating data. / 2025-06-16T00:00:00Z
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/46363 |
| Date | 16 June 2023 |
| Creators | Chernofsky, Ariel |
| Contributors | Lok, Judith J. |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
| Rights | Attribution 4.0 International, http://creativecommons.org/licenses/by/4.0/ |
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