Title of Document: A MIXTURE RASCH MODEL WITH A COVARIATE: A SIMULATION STUDY VIA BAYESIAN MARKOV CHAIN MONTE CARLO ESTIMATION Yunyun Dai, Doctor of Philosophy, 2009 Directed By: Professor, Robert J. Mislevy, Department of Measurement, Statistics and Evaluation Mixtures of item response theory models have been proposed as a technique to explore response patterns in test data related to cognitiv...

In health economic studies that use observational data, a key concern is how to adjust for imbalances in baseline covariates due to the non-random assignment of the programs under evaluation. Traditional methods of covariate adjustment such as regression, depend on correct model specification. Alternatives such as propensity score matching depend on covariate balance being achieved. We demonstr...

The consistency of propensity score (PS) estimators relies on correct specification of the PS model. The PS is frequently estimated using main-effects logistic regression. However, the underlying model assumptions may not hold. Machine learning methods provide an alternative nonparametric approach to PS estimation. In this simulation study, we evaluated the benefit of using Super Learner (SL) f...

In the presence of covariate measurement error with the proportional hazards model, several functional modeling methods have been proposed. These include the conditional score estimator (Tsiatis and Davidian, 2001, Biometrika 88, 447-458), the parametric correction estimator (Nakamura, 1992, Biometrics 48, 829-838), and the nonparametric correction estimator (Huang and Wang, 2000, Journal of th...

Various methods to control the influence of a covariate on a response variable are compared. In particular, ANOVA with or without homogeneity of variances (HOV) of errors and Kruskal-Wallis (K-W) tests on covariate-adjusted residuals and analysis of covariance (ANCOVA) are compared. Covariate-adjusted residuals are obtained from the overall regression line fit to the entire data set ignoring th...

Stratified Cox regression models with large number of strata and small stratum size are useful in many settings, including matched case-control family studies. In the presence of measurement error in covariates and a large number of strata, we show that extensions of existing methods fail either to reduce the bias or to correct the bias under nonsymmetric distributions of the true covariate or ...