Lyden, Grace2022-02-152022-02-152021-12https://hdl.handle.net/11299/226386University of Minnesota Ph.D. dissertation. 2021. Major: Biostatistics. Advisor: David Vock. 1 computer file (PDF); 144 pages.Most statistical methods for causal inference are designed to handle contrasts between well-defined treatment groups, e.g., vaccine versus placebo. In real-world applications, however, these contrasts might fail to answer relevant questions for patients and policy makers. This dissertation introduces new policy-relevant causal estimators that target the effects of heterogeneous treatments and exposures in observational data. Chapter 2 is motivated by correlated chemical mixtures. Historically, environmental health researchers have estimated separate effects of each chemical in a family. More recently, federal agencies have called for estimation of overall mixture effects, which acknowledge the potential real-world burden of simultaneous exposure. A secondary goal of mixtures research is to identify the most harmful components for regulation. Weighted Quantile Sum (WQS) regression has emerged to answer this call. WQS assigns a regularized weight to each chemical in a mixture through a form of bootstrap aggregation, then tests the effect of the weighted sum in a held-out dataset. Although popular, WQS is limited by its dependence on data splitting, which is needed to preserve Type I error. In Chapter 2, we propose the first modification of WQS that does not rely on data splitting and replaces the second step of WQS with a permutation test that yields correct p-values. To offset the added computational burden of a permutation test, we suggest alternatives to the bootstrap for regularization of the weights, namely L1 and L2 penalization, and discuss how to choose the appropriate penalty given expert knowledge about the mixture of interest. Chapters 3 and 4 are motivated by the difficult decisions faced by candidates for organ transplant. Due to organ scarcity, these patients typically have to wait to receive an offer of a suitable deceased-donor organ or possibly pursue living-donor transplant, depending on the organ needed. For patients who have that choice, the difference in post-transplant survival between living- and deceased-donor transplant is a straightforward quantity to estimate, but might not be particularly helpful to patients who experience real-world variability in wait time and offered-organ quality. A more useful contrast, therefore, is the survival difference between treatment strategies that account for this uncertainty, such as "wait for deceased-donor transplant," which could encompass many possible wait times and donor organ qualities. Decisions for patients today are further complicated if versions of treatment have changed over time, for example if the rate of transplant has changed due to evolutions in allocation policy. We, therefore, introduce the concept of a generalized representative intervention (GRI): a random dynamic treatment regime that assigns version of treatment with the distribution of similar patients in a target population under a loosely defined strategy of interest. Chapter 3 proposes a class of weighted product-limit estimators of marginal survival under a GRI, which are consistent, asymptotically normal, and have excellent finite-sample performance in a realistic simulation study. Chapter 4 extends this work to determine the optimal strategy for an individual based on their expected rate of treatment in the target population. Specifically, we propose a marginal structural modeling approach that allows a patient-specific relative rate of treatment to modify the effects of the GRIs under consideration. We apply our methods to data from the Scientific Registry of Transplant Recipients to determine the optimal strategy for kidney-pancreas transplant candidates under the current organ allocation system.enCausal inferenceChemical mixturesDynamic treatment regimesMarginal structural modelsOrgan transplantPermutation methodsThesis or Dissertation