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RETRACTED ARTICLE: A Bayesian joint model for multivariate longitudinal and time-to-event data with application to

Damitri Kundu1, Partha Sarkar1,2, Kiranmoy Das1

  • 1Applied Statistics Division, Indian Statistical Institute, Kolkata, India.

Journal of Biopharmaceutical Statistics
|February 10, 2023
PubMed

Insights

This study on childhood acute lymphocytic leukemia (ALL) found that neutrophil and platelet counts, not white blood cell counts, predict relapse. Lower 6MP and higher MTx doses reduced relapse probability.

Area of Science:

  • Pediatric Oncology
  • Biostatistics
  • Translational Cancer Research

Background:

  • Acute lymphocytic leukemia (ALL) is the most common childhood cancer.
  • Longitudinal biomarkers and drug effectiveness in ALL relapse prediction require further investigation.

Purpose of the Study:

  • To identify longitudinal biomarkers associated with time-to-relapse in pediatric ALL patients.
  • To assess the effectiveness of 6-mercaptopurine (6MP) and methotrexate (MTx) in relation to relapse probability.
  • To develop and validate a Bayesian joint model for analyzing longitudinal biomarkers and time-to-relapse.

Main Methods:

  • A Bayesian joint model was developed, integrating a linear mixed model for longitudinal biomarker data (white blood cell, neutrophil, platelet counts) and a semi-parametric proportional hazards model for time-to-relapse.
  • The model was applied to data from 236 pediatric ALL patients treated at TTCRC, Kolkata.
  • Extensive simulation studies were conducted to assess the model's effectiveness.

Main Results:

  • Neutrophil count and platelet count were significantly associated with time-to-relapse, while white blood cell count was not.
  • A lower dose of 6MP and a higher dose of MTx were associated with a lower relapse probability.
  • Patients classified as 'high-risk' at presentation exhibited the lowest relapse probability.

Conclusions:

  • The developed Bayesian joint model effectively assesses the impact of covariates and biomarkers on ALL progression and relapse.
  • The model efficiently imputes missing longitudinal biomarker data.
  • Findings provide insights into pediatric ALL relapse prediction and potential therapeutic strategies.

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