Bayesian estimation of multilevel item response model with missing data

Xiao Su, The University of Texas School of Public Health


This study proposed a novel statistical method that modeled the multiple outcomes and missing data process jointly using item response theory. This method follows the "intent-to-treat" principle in clinical trials and accounts for the correlation between outcomes and missing data process. This method may provide a good solution to chronic mental disorder study. The simulation study demonstrated that if the true model is the proposed model with moderate or strong correlation, ignoring the within correlation may lead to overestimate of the treatment effect and result in more type I error than specified level. Even if the within correlation is small, the performance of proposed model is as good as naïve response model. Thus, the proposed model is robust for different correlation settings if the data is generated by the proposed model.

Subject Area


Recommended Citation

Su, Xiao, "Bayesian estimation of multilevel item response model with missing data" (2012). Texas Medical Center Dissertations (via ProQuest). AAI1519597.