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Variance Inflation Due to Censoring in Survival Probability Estimates

Author

Listed:
  • Szilard Nemes

    (AstraZeneca)

  • Andreas Gustavsson

    (AstraZeneca)

  • Ziad Taib

    (Atlasbiostat)

Abstract

One of the most obvious features of time-to-event data is the occurrence of censoring. Rarely, if ever, studies are conducted until all participants experience the event of interest. Some participants survive beyond the end of follow-up time, some drop out from the studies for various non-study related reasons. During research planning it is paramount to consider the effect of censoring the follow-up times on the estimates. Herein, we look into the possibility of assessing the loss of information, as measured by the variability of the survival probability estimates under right censoring. We provide the researchers with an easy to use formula to assess the magnitude of variance inflation due to censoring. Additionally, we conducted simulation studies assuming various survival distributions. We conclude that the provided variance inflation estimator can be an accurate practical tool for applied statisticians.

Suggested Citation

  • Szilard Nemes & Andreas Gustavsson & Ziad Taib, 2020. "Variance Inflation Due to Censoring in Survival Probability Estimates," Statistica, Department of Statistics, University of Bologna, vol. 80(4), pages 395-412.
    • Handle: RePEc:bot:rivsta:v:80:y:2020:i:4:p:395-412
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    Cited by:

    1. Szilárd Nemes, 2023. "Asymptotic Relative Efficiency of Parametric and Nonparametric Survival Estimators," Stats, MDPI, vol. 6(4), pages 1-13, October.

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