Multiple imputation of censored survival data in the presence of missing covariates using restricted mean survival time

Abstract

Missing covariates data with censored outcomes put a challenge in the analysis of clinical data especially in small sample settings. Multiple imputation (MI) techniques are popularly used to impute missing covariates and the data are then analyzed through methods that can handle censoring. However, techniques based on MI are available to impute censored data also but they are not much in practice. In the present study, we applied a method based on multiple imputation by chained equations to impute missing values of covariates and also to impute censored outcomes using restricted survival time in small sample settings. The complete data were then analyzed using linear regression models. Simulation studies and a real example of CHD data show that the present method produced better estimates and lower standard errors when applied on the data having missing covariate values and censored outcomes than the analysis of the data having censored outcome but excluding cases with missing covariates or the analysis when cases with missing covariate values and censored outcomes were excluded from the data (complete case analysis).

Keywords:

• multiple imputation
• censored outcomes
• missing covariates
• restricted mean survival time
• small sample size

Disclosure statement

No potential conflict of interest was reported by the authors.

Funding

This work was supported by grant to the first author under research and development program of University of Delhi, India.