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• W2286593271 abstract "Mediators are intermediate variables in thecausal pathway between an exposure and an outcome. Mediationanalysis investigates the extent to which exposure effects occurthrough these variables, thus revealing causal mechanisms. Oneinteresting question in causal inference area is mediation analysisfor non-linear models. In the first part of thisdissertation, we consider the estimation of mediation effects inzero-inflated (ZI) models intended to accommodate ‘extra’ zeros incount data. Focusing on the ZI negative binomial (ZINB) models, weprovide a mediation formula approach to estimate the (overall)mediation effect in the standard two-stage mediation frameworkunder the key sequential ignorability assumption. We also considera novel decomposition of the overall mediation effect for the ZIcontext using a three-stage mediation model. Simulation studyresults demonstrate low bias of mediation effect estimators andclose-to-nominal coverage probability (CP) of confidence intervals.The method is applied to a retrospective cohort study of dentalcaries in very low birth weight adolescents. For overall mediationeffect estimation, sensitivity analysis was conducted to quantifythe degree to which key assumption must be violated to reverse theoriginal conclusion. The second question we focuson is the mediation analysis for a dichotomous outcome inmultiple-mediator models. We formulate a joint model(probit-normal) using continuous latent variables for any binarymediators to account for correlations among multiple mediators. Amediation formula approach is proposed to estimate the totalmediation effect and decomposed mediation effects based on thisparametric model. We conduct a simulation study that demonstrateslow bias of mediation effect estimators for two-mediator modelswith various combinations of mediator types. The results also showthat the power to detect a non-zero total mediation effectincreases as the correlation coefficient between two mediatorsincreases, while power for individual mediation effects reaches amaximum when the mediators are uncorrelated. We illustrate ourapproach by applying it to a retrospective cohort study of dentalcaries in adolescents with low and high socioeconomic status.Sensitivity analysis is performed to assess the robustness ofconclusions regarding mediation effects when the assumption of nounmeasured mediator-outcome confounders isviolated" @default.
• W2286593271 created "2016-06-24" @default.
• W2286593271 creator A5046597133 @default.
• W2286593271 date "2012-01-01" @default.
• W2286593271 modified "2023-09-23" @default.
• W2286593271 title "CAUSAL MEDIATION ANALYSIS FOR NON-LINEAR MODELS" @default.
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