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Estimating Mean Survival Time: When is it Possible?

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  • Ying Ding
  • Bin Nan

Abstract

type="main" xml:id="sjos12112-abs-0001"> For right-censored survival data, it is well-known that the mean survival time can be consistently estimated when the support of the censoring time contains the support of the survival time. In practice, however, this condition can be easily violated because the follow-up of a study is usually within a finite window. In this article, we show that the mean survival time is still estimable from a linear model when the support of some covariate(s) with non-zero coefficient(s) is unbounded regardless of the length of follow-up. This implies that the mean survival time can be well estimated when the support of linear predictor is wide in practice. The theoretical finding is further verified for finite samples by simulation studies. Simulations also show that, when both models are correctly specified, the linear model yields reasonable mean square prediction errors and outperforms the Cox model, particularly with heavy censoring and short follow-up time.

Suggested Citation

  • Ying Ding & Bin Nan, 2015. "Estimating Mean Survival Time: When is it Possible?," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(2), pages 397-413, June.
    • Handle: RePEc:bla:scjsta:v:42:y:2015:i:2:p:397-413
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    File URL: http://hdl.handle.net/10.1111/sjos.12112
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    Cited by:

    1. Byungtae Seo & Sangwook Kang, 2023. "Accelerated failure time modeling via nonparametric mixtures," Biometrics, The International Biometric Society, vol. 79(1), pages 165-177, March.

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