Application of the Misclassification Simulation Extrapolation (Mc-Simex) Procedure to Log-Logistic Accelerated Failure Time (Aft) Models In Survival Analysis

Survival analysis is the study of time to event outcomes. Accelerated Failure Time models (AFT) serve as a useful tool in survival analysis to study the time of occurrence of an event and its relation to the covariates of interest. The accuracy of estimation of parameters in a model depends upon the correct measurement of covariates. Considering that perfect measurement of covariates is highly unlikely, it is imperative that the performance of the existing bias-correction methods be analyzed in AFT models. However, certain areas of bias-correction in AFT models still remain unexplored. One of these unexplored areas, is a situation where the survival times follow a log-logistic distribution. In this dissertation, we evaluate the performance of the Misclassification simulation extrapolation (MC-SIMEX) procedure, a well-known procedure for bias-correction due to misclassification, in AFT models where the survival times follow a standard log-logistic distribution. In addition, a modified version of the MC-SIMEX procedure is also proposed, that provides an advantage in situations where the sensitivity and specificity of classification are unknown. Lastly, the performance of the original MC-SIMEX procedure in lung cancer data provided by the North Central Cancer Treatment Group (NCCTG), is also evaluated.