Flexible Regression Models for Estimating Interactions between a Treatment and Scalar/Functional Predictors

In this dissertation, we develop regression models for estimating interactions between a treatment variable and a set of baseline predictors in their eect on the outcome in a randomized trial, without restriction to a linear relationship. The proposed semiparametric/nonparametric regression approaches for representing interactions generalize the notion of an interaction between a categorical treatment variable and a set of predictors on the outcome, from a linear model context. In Chapter 2, we develop a model for determining a composite predictor from a set of baseline predictors that can have a nonlinear interaction with the treatment indicator, implying that the treatment efficacy can vary across values of such a predictor without a linearity restriction. We introduce a parsimonious generalization of the single-index models that targets the eect of the interaction between the treatment conditions and the vector of predictors on the outcome. A common approach to interrogate such treatment-by-predictor interaction is to t a regression curve as a function of the predictors separately for each treatment group. For parsimony and insight, we propose a single-index model with multiple-links that estimates a single linear combination of the predictors (i.e., a single-index), with treatment-specic nonparametrically-dened link functions. The approach emphasizes a focus on the treatment-by-predictors interaction eects on the treatment outcome that are relevant for making optimal treatment decisions. Asymptotic results for estimator are obtained under possible model misspecication. A treatment decision rule based on the derived single-index is dened, and it is compared to other methods for estimating optimal treatment decision rules. An application to a clinical trial for the treatment of depression is presented to illustrate the proposed approach for deriving treatment decision rules. In Chapter 3, we allow the proposed single-index model with multiple-links to have an unspecified main effect of the predictors on the outcome. This extension greatly increases the utility of the proposed regression approach for estimating the treatment-by-predictors interactions. By obviating the need to model the main eect, the proposed method extends the modied covariate approach of [Tian et al., 2014] into a semiparametric regression framework. Also, the approach extends [Tian et al., 2014] into general K treatment arms. In Chapter 4, we introduce a regularization method to deal with the potential high dimensionality of the predictor space and to simultaneously select relevant treatment effect modiers exhibiting possibly nonlinear associations with the outcome. We present a set of extensive simulations to illustrate the performance of the treatment decision rules estimated from the proposed method. An application to a clinical trial for the treatment of depression is presented to illustrate the proposed approach for deriving treatment decision rules. In Chapter 5, we develop a novel additive regression model for estimating interactions between a treatment and a potentially large number of functional/scalar predictor. If the main effect of baseline predictors is misspecied or high-dimensional (or, innite dimensional), any standard nonparametric or semiparametric approach for estimating the treatment-bypredictors interactions tends to be not satisfactory because it is prone to (possibly severe) inconsistency and poor approximation to the true treatment-by-predictors interaction effect. To deal with this problem, we impose a constraint on the model space, giving the orthogonality between the main and the interaction effects. This modeling method is particularly appealing in the functional regression context, since a functional predictor, due to its infinite dimensional nature, must go through some sort of dimension reduction, which essentially involves a main effect model misspecication. The main effect and the interaction effect can be estimated separately due to the orthogonality between the two effects, which side-steps the issue of misspecication of the main effect. The proposed approach extends the modied covariate approach of [Tian et al., 2014] into an additive regression model framework. We impose a concave penalty in estimation, and the method simultaneously selects functional/scalar treatment effect modifiers that exhibit possibly nonlinear interaction effects with the treatment indicator. The dissertation concludes in Chapter 6

Examining Consumer Pre-purchase Deliberation Process in an Online Marketplace

The research objective of this study was to examine the consumer pre-purchase deliberation process in an online marketplace. Especially, examining the role of extrinsic quality (website-related quality), intrinsic quality (business operation-related quality), reputation of the retailer (other consumers’ opinions), attitude toward the retailer, and intention to transact with the retailer in an online marketplace context was main purpose of this study. To collect the data, extensive online survey was performed. The data were analyzed through structural equation modeling test. Findings revealed that there are significant positive relationships among extrinsic quality, intrinsic quality, reputation of the retailer, attitude toward the retailer, and intention to transact with the retailer in an online marketplace. Especially, influence of extrinsic quality on intrinsic quality was considerably strong. However, influence of the reputation of the retailer showed weak influence on attitude toward the retailer unlike the suggestions of previous studies. In addition, the result of this study revealed that there is double-layered structure (extrinsic and intrinsic quality) in the quality of the retailer, which has not been conceptualized and empirically tested in the previous studies. Managerial and academic implications and future research directions based on the findings were offered

China Medical Board and Modern Medicine in Seoul

This report describes the role of China Medical Board (CMB), a Rockefeller-endowed philanthropy, in promoting modern medical research and education at Seoul National University (SNU). Although the Rockefeller Foundation refused to fund Keijo Imperial University, a predecessor of SNU during Japanese Colonial Rule (1910-1945), CMB actively supported the schools of medicine and nursing at SNU after 1963, through its extensive fellowship program as well as research grant awards. Moreover, CMB provided funding for designing the new main building at Seoul National University Hospital (SNUH), as well as its medical library and research laboratory. Hence, CMB, along with the United States federal government, became a primary agency of promoting modern medicine in South Korea. However, Korean professionals at SNU had their own ideas and agendas, which made them respond to CMB's plans and strategies in their own way. The interplay between the two formed a key part of Korea's story of making modern medicine