abstract: Mediation analysis is used to investigate how an independent variable, X, is related to an outcome variable, Y, through a mediator variable, M (MacKinnon, 2008). If X represents a randomized intervention it is difficult to make a cause and effect inference regarding indirect effects without making no unmeasured confounding assumptions using the potential outcomes framework (Holland, 1988; MacKinnon, 2008; Robins & Greenland, 1992; VanderWeele, 2015), using longitudinal data to determine the temporal order of M and Y (MacKinnon, 2008), or both. The goals of this dissertation were to (1) define all indirect and direct effects in a three-wave longitudinal mediation model using the causal mediation formula (Pearl, 2012), (2) analytically compare traditional estimators (ANCOVA, difference score, and residualized change score) to the potential outcomes-defined indirect effects, and (3) use a Monte Carlo simulation to compare the performance of regression and potential outcomes-based methods for estimating longitudinal indirect effects and apply the methods to an empirical dataset. The results of the causal mediation formula revealed the potential outcomes definitions of indirect effects are equivalent to the product of coefficient estimators in a three-wave longitudinal mediation model with linear and additive relations. It was demonstrated with analytical comparisons that the ANCOVA, difference score, and residualized change score models’ estimates of two time-specific indirect effects differ as a function of the respective mediator-outcome relations at each time point. The traditional model that performed the best in terms of the evaluation criteria in the Monte Carlo study was the ANCOVA model and the potential outcomes model that performed the best in terms of the evaluation criteria was sequential G-estimation. Implications and future directions are discussed. / Dissertation/Thesis / Doctoral Dissertation Psychology 2018

In advanced democracies, many citizens abstain from participating in the political process. Does low and unequal voter turnout influence partisan election results or public policies? If so, how can participation be increased and how can the electorate become more representative of the greater population? / Government

abstract: Statistical inference from mediation analysis applies to populations, however, researchers and clinicians may be interested in making inference to individual clients or small, localized groups of people. Person-oriented approaches focus on the differences between people, or latent groups of people, to ask how individuals differ across variables, and can help researchers avoid ecological fallacies when making inferences about individuals. Traditional variable-oriented mediation assumes the population undergoes a homogenous reaction to the mediating process. However, mediation is also described as an intra-individual process where each person passes from a predictor, through a mediator, to an outcome (Collins, Graham, & Flaherty, 1998). Configural frequency mediation is a person-oriented analysis of contingency tables that has not been well-studied or implemented since its introduction in the literature (von Eye, Mair, & Mun, 2010; von Eye, Mun, & Mair, 2009). The purpose of this study is to describe CFM and investigate its statistical properties while comparing it to traditional and casual inference mediation methods. The results of this study show that joint significance mediation tests results in better Type I error rates but limit the person-oriented interpretations of CFM. Although the estimator for logistic regression and causal mediation are different, they both perform well in terms of Type I error and power, although the causal estimator had higher bias than expected, which is discussed in the limitations section. / Dissertation/Thesis / Masters Thesis Psychology 2019

Indiana University-Purdue University Indianapolis (IUPUI) / Observational data are frequently used for causal inference of treatment effects on prespecified outcomes. Several widely used causal inference methods have adopted the method of inverse propensity score weighting (IPW) to alleviate the in uence of confounding. However, the IPW-type methods, including the doubly robust methods, are prone to large variation in the estimation of causal e ects due to possible extreme weights. In this research, we developed an ordinary least-squares (OLS)-based causal inference method, which does not involve the inverse weighting of the individual propensity scores. We first considered the scenario of homogeneous treatment effect. We proposed a two-stage estimation procedure, which leads to a model-free estimator of average treatment effect (ATE). At the first stage, two summary scores, the propensity and mean scores, are estimated nonparametrically using regression splines. The targeted ATE is obtained as a plug-in estimator that has a closed form expression. Our simulation studies showed that this model-free estimator of ATE is consistent, asymptotically normal and has superior operational characteristics in comparison to the widely used IPW-type methods. We then extended our method to the scenario of heterogeneous treatment effects, by adding in an additional stage of modeling the covariate-specific treatment effect function nonparametrically while maintaining the model-free feature, and the simplicity of OLS-based estimation. The estimated covariate-specific function serves as an intermediate step in the estimation of ATE and thus can be utilized to study the treatment effect heterogeneity. We discussed ways of using advanced machine learning techniques in the proposed method to accommodate high dimensional covariates. We applied the proposed method to a case study evaluating the effect of early combination of biologic & non-biologic disease-modifying antirheumatic drugs (DMARDs) compared to step-up treatment plan in children with newly onset of juvenile idiopathic arthritis disease (JIA). The proposed method gives strong evidence of significant effect of early combination at 0:05 level. On average early aggressive use of biologic DMARDs leads to around 1:2 to 1:7 more reduction in clinical juvenile disease activity score at 6-month than the step-up plan for treating JIA.

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京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24727号 / 情博第815号 / 新制||情||137(附属図書館) / 京都大学大学院情報学研究科知能情報学専攻 / (主査)教授 鹿島 久嗣, 教授 阿久津 達也, 教授 下平 英寿 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

Causal inference methods have been applied in various fields where researchers want to establish causal effects between different phenomena. The goal of causal inference is to estimate treatment effects by comparing outcomes had units received treatment versus outcomes had units not received treatment. We focus on estimating treatment effects in three different projects. We first proposed linear unbiased estimators (LUEs) for general causal effects under the assumption that treatment effects are additive. Under the assumption of additivity, the set of estimands considered grows as contrasts in exposures are now equivalent. Furthermore, we identified a subset of LUEs that forms an affine basis for LUEs, and we characterized LUEs with minimum integrated variance through defining conditions on the support of the estimator. We also estimated the effect of drug-induced homicide (DIH) prosecutions reported by the media on unintentional drug overdose deaths, which have never been empirically assessed, using various models. Using a difference-in-differences-like logistic generalized additive model (GAM) with smoothed time effects where we assumed a constant treatment effect, we found that DIH prosecutions reported by the media were associated with a potential harmful effect (risk ratio: 1.064; 95% CI: (1.012, 1.118)) on drug overdose deaths. Upon further research, however, there are potential issues using a constant treatment effect model in a setting where treatment is staggered and treatment effects are heterogeneous. Therefore, we also used a GAM with a linear link function where we assumed that treatment effects may depend on the treatment duration. With this second model, we estimated a risk ratio for having any DIH prosecutions reported by the media of 0.956 (95% CI: (0.824, 1.110)) and a risk ratio of 0.986 (95% CI: (0.973, 0.999)) for the effect of being exposed to DIH prosecutions reported by the media for each additional six months. Despite being statistically significant, the effects were not practically significant. However, the results call for further research on the effect of DIH prosecutions on drug overdose deaths. Lastly, we shift our focus to Structural Nested Mean Models (SNMMs). We extended SNMMs to a new class of estimators which estimate treatment effects of different treatment regimes in the risk ratio scale---the Structural Nested Risk Ratio Model (SNRRM). We further generalized previous work on SNMMs by estimating treatment effects by modeling a function of treatment, which we choose to be any function that can be modeled by generalized linear models, as opposed to just a model for treatment initiation. We applied SNRRMs to estimate the effect of DIH prosecutions reported by the media on drug overdose deaths.

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