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On the performance of some non-parametric estimators of the conditional survival function with interval-censored data

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  • Dehghan, Mohammad Hossein
  • Duchesne, Thierry

Abstract

Simple nonparametric estimators of the conditional distribution of a response variable given a continuous covariate are often useful in survival analysis. Since a few nonparametric estimation options are available, a comparison of the performance of these options may be of value to determine which approach to use in a given application. In this note, we compare various nonparametric estimators of the conditional survival function when the response is subject to interval- and right-censoring. The estimators considered are a generalization of Turnbull's estimator proposed by Dehghan and Duchesne (2011) and two nonparametric estimators for complete or right-censored data used in conjunction with imputation methods, namely the Nadaraya-Watson and generalized Kaplan-Meier estimators. We study the finite sample integrated mean squared error properties of all these estimators by simulation and compare them to a semi-parametric estimator. We propose a rule-of-thumb based on simple sample summary statistics to choose the most appropriate among these estimators in practice.

Suggested Citation

  • Dehghan, Mohammad Hossein & Duchesne, Thierry, 2011. "On the performance of some non-parametric estimators of the conditional survival function with interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3355-3364, December.
    • Handle: RePEc:eee:csdana:v:55:y:2011:i:12:p:3355-3364
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    References listed on IDEAS

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    1. Leconte, E. & Poiraud-Casanova, S. & Tohomas-Agnan, C., 2000. "Smooth Conditional Distribution Function and Quantiles Under Random Censorship," Papers 00-543, Toulouse - GREMAQ.
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    Cited by:

    1. Xun Xiao & Amitava Mukherjee & Min Xie, 2016. "Estimation procedures for grouped data – a comparative study," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(11), pages 2110-2130, August.

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