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Related Concept Videos

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...
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,...
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...
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 squares (OLS)...
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...

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Updated: May 19, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Published on: July 3, 2020

A Bayesian Location-Scale Joint Model for Time-To-Event and Multivariate Longitudinal Data With Association Based on

Marco Palma1,2, Omar El Makkaoui1,3, Ruth H Keogh4

  • 1MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.

Statistics in Medicine
|May 18, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel joint model to analyze how changes in individual health markers over time affect disease progression and mortality risk. The new method accurately quanties within-individual variability, improving predictions for time-to-event outcomes.

Keywords:
cystic fibrosisjoint modelmixed‐effects location‐scale model (MELSM)within‐individual variability

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Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Survival Analysis

Background:

  • Within-individual variability in health indicators is crucial for understanding disease progression.
  • Traditional methods using summary statistics for longitudinal data can lead to biased results in survival models.
  • Existing joint models often assume constant variance, failing to capture dynamic changes.

Purpose of the Study:

  • To develop and validate a novel joint model that incorporates within-individual variability of longitudinal markers.
  • To accurately estimate the association between dynamic health marker variability and time-to-event outcomes.
  • To address regression dilution bias inherent in standard survival analysis methods.

Main Methods:

  • A mixed-effect location-scale model was employed to analyze longitudinal biomarkers, their within-individual variability, and correlations.
  • A proportional hazard regression model with a flexible baseline hazard was used for time-to-event outcomes.
  • The proposed joint model shares information from longitudinal biomarkers as a function of random effects.

Main Results:

  • The novel joint model demonstrated superior performance compared to standard joint models with constant variance in simulation studies.
  • The model effectively quantifies within-individual variability for longitudinal markers.
  • The model successfully evaluated the association between lung function, malnutrition, and mortality in cystic fibrosis patients.

Conclusions:

  • The developed joint model provides a robust framework for analyzing longitudinal data and time-to-event outcomes, accounting for within-individual variability.
  • This approach offers improved accuracy in estimating hazard ratios and understanding disease progression.
  • The findings have significant implications for clinical research and patient management, particularly in chronic diseases like cystic fibrosis.