Misclassification Simulation Extrapolation Procedure for Interval-Censored Log-Logistic Accelerated Failure Time Model
Contribution to Book
Emerging Topics in Modeling Interval-Censored Survival Data
Misclassification of binary covariates often occurs in survival data and any survival data analysis ignoring such misclassification will result in estimation bias. To handle such misclassification, the misclassification simulation extrapolation (MC-SIMEX) procedure is a flexible method proposed in survival data analysis, which has been investigated extensively for right-censored survival data. However, the performance of the MC-SIMEX method has not been explored enough for interval-censored survival data. This chapter is aimed to investigate the performance of the MC-SIMEX procedure to interval-censored survival data through Monte-Carlo simulations and real data analysis. This investigation focuses on the log-logistic accelerated failure time (AFT) model since the log-logistic distribution plays an important role in evaluating non-monotonic hazards for survival data.
Sevilimedu, Varadan, Lili Yu, Ding-Geng Chen, Y. L. Lio.
"Misclassification Simulation Extrapolation Procedure for Interval-Censored Log-Logistic Accelerated Failure Time Model."
Emerging Topics in Modeling Interval-Censored Survival Data, Jianguo Sun and Ding-Geng Chen (Ed.): 295-308: Springer.