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

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 co...

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Bibliographic Details
Published inStatistika (Prague, Czech Republic) Vol. 94; no. 1; pp. 64 - 72
Main Author Ali Satty
Format Journal Article
LanguageEnglish
Published Czech Statistical Office 01.01.2014
Subjects
Cox proportional hazard model
Last observation carried forward (LOCF)
missing at random (MAR)
Missing covariate values
multiple imputation (MI)
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Summary: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.
ISSN:1804-8765
1804-8765