Comparison of missing data methods in clustered survival data using Bayesian adaptive B-Spline estimation

In many epidemiological studies, missing values in the outcome arise due to censoring. Such censoring is what makes survival analysis special and differentiated from other analytical methods. There are many methods that deal with censored data in survival analysis. However, few studies have dealt with missing covariates in survival data. Furthermore, studies dealing with missing covariates are rare when data are clustered. In this paper, we conducted a simulation study to compare results of several missing data methods when data had clustered multi-structured type with missing covariates. In this study, we modeled unknown baseline hazard and frailty with Bayesian B-Spline to obtain more smooth and accurate estimates. We also used prior information to achieve more accurate results. We assumed the missing mechanism as MAR. We compared the performance of five different missing data techniques and compared these results through simulation studies. We also presented results from a Multi-Center study of Korean IBD patients with Crohn's disease.