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Multiple imputation of censored bivariate event-times via inverse transform and nonparametric Gibbs sampling

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  • Angulo, Daniela
  • Murray, Susan

Abstract

Bivariate time-to-event data, subject to right censoring, frequently arise in medical research. This paper introduces a novel nonparametric multiple imputation (MI) procedure for analyzing censored bivariate time-to-event data. Our methodology offers a straightforward, easy-to-implement inverse transform MI method that effectively captures the joint distribution of bivariate random variables through the imputation of censored event-times.

Suggested Citation

  • Angulo, Daniela & Murray, Susan, 2026. "Multiple imputation of censored bivariate event-times via inverse transform and nonparametric Gibbs sampling," Statistics & Probability Letters, Elsevier, vol. 228(C).
    • Handle: RePEc:eee:stapro:v:228:y:2026:i:c:s0167715225002093
    DOI: 10.1016/j.spl.2025.110564
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    References listed on IDEAS

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