Related Experiment Video

Updated: Aug 7, 2025

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K

A Bayesian joint model for multivariate longitudinal and time-to-event data with application to ALL maintenance

Damitri Kundu1, Partha Sarkar2, Manash Pratim Gogoi3

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

Journal of Biopharmaceutical Statistics
|March 8, 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 in ALL patients.

Area of Science:

  • Pediatric Oncology
  • Biostatistics
  • Translational Cancer Research

Background:

  • Acute Lymphocytic Leukemia (ALL) is the most common childhood cancer.
  • Standard treatments involve drugs like 6-mercaptopurine (6MP) and methotrexate (MTx).
  • Identifying biomarkers for relapse prediction and treatment effectiveness is crucial.

Purpose of the Study:

  • To identify longitudinal biomarkers associated with time-to-relapse in pediatric ALL patients.
  • To assess the effectiveness of 6MP and MTx treatments in relation to relapse.
  • To develop and validate a statistical model for analyzing longitudinal biomarkers and time-to-relapse.

Main Methods:

  • A Bayesian joint model was developed, integrating a linear mixed model for longitudinal biomarkers (white blood cell, neutrophil, platelet counts) and a semi-parametric proportional hazards model for time-to-relapse.
  • The model analyzed data from 236 pediatric ALL patients treated at TTCRC, Kolkata.
  • The model's ability to assess covariate effects and impute missing data was evaluated.

Main Results:

  • Neutrophil and platelet counts were significantly associated with time-to-relapse in ALL patients.
  • White blood cell count showed no significant association with time-to-relapse.
  • Lower 6MP doses and higher MTx doses were linked to a reduced probability of relapse.
  • Patients classified as "high-risk" at presentation exhibited the lowest relapse probability.

Conclusions:

  • The developed Bayesian joint model effectively identifies biomarkers predicting relapse in pediatric ALL.
  • Neutrophil and platelet counts are critical prognostic indicators for ALL relapse.
  • Treatment strategies involving adjusted 6MP and MTx dosages may influence relapse rates, with potential implications for high-risk groups.

Keywords:
Acute lymphocytic leukemia (ALL)MCMCjoint modellongitudinal outcomesposterior predictive distribution

More Related Videos

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.4K

Related Concept Videos

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
512
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
167
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
320
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
104
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
205
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
71