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

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  • Gurprit Grover
  • Vinay K. Gupta

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).

Suggested Citation

  • Gurprit Grover & Vinay K. Gupta, 2015. "Multiple imputation of censored survival data in the presence of missing covariates using restricted mean survival time," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(4), pages 817-827, April.
    • Handle: RePEc:taf:japsta:v:42:y:2015:i:4:p:817-827
    DOI: 10.1080/02664763.2014.986439
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    References listed on IDEAS

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    1. Koehler, Elizabeth & Brown, Elizabeth & Haneuse, Sebastien J.-P. A., 2009. "On the Assessment of Monte Carlo Error in Simulation-Based Statistical Analyses," The American Statistician, American Statistical Association, vol. 63(2), pages 155-162.
    2. Rubin, Donald B, 1986. "Statistical Matching Using File Concatenation with Adjusted Weights and Multiple Imputations," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 87-94, January.
    3. Wei Pan, 2000. "A Multiple Imputation Approach to Cox Regression with Interval-Censored Data," Biometrics, The International Biometric Society, vol. 56(1), pages 199-203, March.
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    1. Speidel, Matthias & Drechsler, Jörg & Jolani, Shahab, 2018. "R package hmi: a convenient tool for hierarchical multiple imputation and beyond," IAB-Discussion Paper 201816, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].

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