The Health and Retirement Study (HRS) is a longitudinal study of U.S. adults enrolled at age 50 and older. We were interested in investigating the effect of a sudden large decline in wealth on the cognitive ability of subjects measured using a dataset provided composite score. However, our analysis was complicated by the lack of randomization, time-dependent confounding, and a substantial fraction of the sample and population will die during follow-up leading to some of our outcomes being censored. The common method to handle this type of problem is marginal structural models (MSM). Although MSM produces valid estimates, this may not be the most appropriate method to reflect a useful real-world situation because MSM upweights subjects who are more likely to die to obtain a hypothetical population that over time, resembles that would have been obtained in the absence of death. A more refined and practical framework, principal stratification (PS), would be to restrict analysis to the strata of the population that would survive regardless of negative wealth shock experience. In this work, we propose a new algorithm for the estimation of the treatment effect under PS by imputing the counterfactual survival status and outcomes. Simulation studies suggest that our algorithm works well in various scenarios. We found no evidence that a negative wealth shock experience would affect the cognitive score of HRS subjects.

%B Statistics in Medicine %G eng %R 10.1002/sim.8921 %0 Report %D 2018 %T Accounting for selection bias due to death in estimating the effect of wealth shock on cognition for the Health and Retirement Study %A Yaoyuan V. Tan %A Carol A C Flannagan %A Lindsay R Pool %A Michael R. Elliott %K Cognition & Reasoning %K Survey Methodology %K Wealth Shocks %X The Health and Retirement Study is a longitudinal study of US adults enrolled at age 50 and older. We were interested in investigating the effect of a sudden large decline in wealth on the cognitive score of subjects. Our analysis was complicated by the lack of randomization, confounding by indication, and a substantial fraction of the sample and population will die during follow-up leading to some of our outcomes being censored. Common methods to handle these problems for example marginal structural models, may not be appropriate because it upweights subjects who are more likely to die to obtain a population that over time resembles that would have been obtained in the absence of death. We propose a refined approach by comparing the treatment effect among subjects who would survive under both sets of treatment regimes being considered. We do so by viewing this as a large missing data problem and impute the survival status and outcomes of the counterfactual. To improve the robustness of our imputation, we used a modified version of the penalized spline of propensity methods in treatment comparisons approach. We found that our proposed method worked well in various simulation scenarios and our data analysis. %B Statistics > Applications %I arXiv.org %C Ithaca %8 12/2018 %G eng %U https://arxiv.org/abs/1812.08855 %0 Thesis %B Biostatistics %D 2018 %T Novel Applications and Extensions for Bayesian Additive Regression Trees (BART) in Prediction, Imputation, and Causal Inference %A Yaoyuan V. Tan %K 0308:Biostatistics %K Bayesian additive regression trees %K Biological sciences %K Biostatistics %K Causal inference %K Imputation %K Prediction %X The Bayesian additive regression trees (BART) is a method proposed by Chipman et al. (2010) that can handle non-linear main and multiple-way interaction effects for independent continuous or binary outcomes. It has enjoyed much success in areas like causal inference, economics, environmental sciences, and genomics. However, extensions of BART and application of these extensions are limited. This thesis discusses three novel applications and extensions for BART. We first discuss how BART can be extended to clustered outcomes by adding a random intercept. This work was motivated by the need to accurately predict driver behavior using observable speed and location information with application to communication of key human-driver intention to nearby vehicles in traffic. Although our extension can be considered a special case of the spatial BART (Zhang et al., 2007), our approach differs by providing a relatively simple algorithm that allows application to clustered binary outcomes. We next focus on the use of BART in missing data settings. Doubly robust (DR) methods allow consistent estimation of population means when either non-response propensity or modeling of the mean of the outcome is correctly specified. Kang and Schafer (2007) showed that DR methods produce biased and inefficient estimates when both propensity and mean models are misspecified. We consider the use of BART for modeling means and/or propensities to provide a ``robust-squared'' estimator that reduces bias and improves efficiency. We demonstrate this result, using simulations, for the two commonly used DR methods: Augmented Inverse Probability Weighting (AIPWT, Robbins et al., 1994) and penalized splines of propensity prediction (PSPP, Zhang and Little, 2009). We successfully applied our proposed model to two national crash datasets to impute missing change in deceleration values (delta-v) and missing Blood Alcohol Concentration (BAC) levels respectively. Our final effort considers how a negative wealth shock (sudden large decline in wealth) affects the cognitive outcome of late middle aged US adults using the Health Retirement Study, a longitudinal study of US adults, enrolled at age 50 and older and surveyed biennially since 1992. Our analysis faced three issues: lack of randomization, confounding by indication, and censoring of the cognitive outcome by a substantial number of deaths in our subjects. Marginal structural models (MSM), a commonly used method to deal with censoring by death, is arguably inappropriate because it upweights subjects who are more likely to die, creating a pseudo-population which resembles one where death is absent. We propose to compare the negative wealth shock effect only among subjects who survived under both sets of treatment regimens - a special case of principal stratification (Frangakis and Rubin, 2002). Because the counterfactual survival status would be unobserved, we imputed their survival status and restrict analysis to subjects who were observed and predicted to survive under both treatment regimes. We used a modified version of penalized spline of propensity methods in treatment comparisons (PENCOMP, Zhou et. al, 2018) to obtain a robust imputation of the counterfactual cognitive outcomes. Finally, we consider several possible extensions of these efforts for future work. %B Biostatistics %I University of Michigan %C Ann Arbor, MI %V PhD %P 201 %@ 9780438885981 %G eng %U https://deepblue.lib.umich.edu/handle/2027.42/147594 %9 phd