A Simulation Study Comparing Two Methods of Handling Missing Covariate Values when Fitting a Cox Proportional-Hazards Regression Model

Abstract

Missing covariate values is a common problem in a survival data research. The aim of this study is to Compaq the use of the multiple imputation (MI) and last observation carried forward (LOCF) methods for handling missing covariate values in the Cox proportional hazards (PH) regression model. The comparisons between the methods are based on simulated data. The missingness mechanism is assumed to be missing at random (MAR). Missing covariate values are generated under different missingness rates. The results from both methods are compared by assessing the bias, efficiency and coverage. The simulation results in general revealed that MI is likely to be the best under the MAR mechanism.

Keywords

• Missing covariate values
• multiple imputation (MI)
• Cox proportional hazard model
• Last observation carried forward (LOCF)
• missing at random (MAR)