Dynamic lifetime prediction using a Weibull-based bivariate failure time model: a meta-analysis of individual-patient data

Sayaka Shinohara*, Yuan Hsin Lin, Hirofumi Michimae, Takeshi Emura

*Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

9 Scopus citations

Abstract

Predicting time-to-death for patients is one of the most important issues in survival analysis. A dynamic prediction method using a bivariate failure time model allows one to build a prediction formula based on tumor progression status observed during the follow-up. However, the existing spline models for the baseline hazard functions are not convenient for predicting long-term survival probability exceeding the largest follow-up time. Therefore, we proposed a parametric method based on the Weibull model to achieve long-term prediction. The present study aims to develop a prediction formula based on a Weibull-based bivariate failure time model, which is designed for individual patient data meta-analysis. We also consider prediction of residual life expectancy that is not possible by the nonparametric models. We conducted Monte Carlo simulations to compare the performance of the proposed model with the spline model. In addition, we illustrate the proposed methods through the analysis of breast cancer patients.

Original languageEnglish
Pages (from-to)349-368
Number of pages20
JournalCommunications in Statistics: Simulation and Computation
Volume52
Issue number2
DOIs
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© 2020 Taylor & Francis Group, LLC.

Keywords

  • Copula
  • Increasing hazard rate
  • Residual life
  • Semi-competing risk
  • Weibull probability plot

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