Related Experiment Video
Updated: Jun 22, 2026
07:35
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
A nonlinear latent class model for joint analysis of multivariate longitudinal data and a binary outcome
Cécile Proust-Lima1, Luc Letenneur, Hélène Jacqmin-Gadda
1Institut National de la Santé et de la Recherche Médicale, Equipe de Biostatistique E0338, 33076 Bordeaux, France. cecile.proust@isped.u-bordeaux2.fr
Statistics in Medicine
|August 11, 2006
Summary
This study introduces a new joint model to link longitudinal markers with clinical events. The model aids in early detection and prognosis by identifying distinct patient trajectories and associated risks.
Area of Science:
- Biostatistics
- Longitudinal Data Analysis
- Clinical Event Prediction
Background:
- Understanding the relationship between multiple longitudinal markers and clinical events is crucial for disease management.
- Existing models often struggle to account for complex marker interdependencies and non-Gaussian data.
Purpose of the Study:
- To develop a flexible joint model integrating nonlinear growth mixture models and logistic regression.
- To analyze associations between correlated longitudinal markers and a clinical event, accommodating non-Gaussian data.
- To provide tools for early detection and prognosis of clinical events.
Main Methods:
- A nonlinear growth mixture model was employed to capture latent classes of longitudinal marker evolution.
- Logistic regression modeled the probability of a clinical event based on latent classes.
- Flexible nonlinear transformations were used to link markers to the latent process, handling non-Gaussian outcomes.
Main Results:
- The model successfully described latent profiles of evolution associated with the clinical event, incorporating covariate effects.
- It demonstrated the utility of derived diagnostic and prognostic tools for early clinical event detection.
- The application on cognitive aging highlighted the model's ability to handle complex longitudinal data.
Conclusions:
- The proposed joint model offers a robust framework for analyzing longitudinal markers and clinical events.
- It provides valuable insights into disease progression and enables early risk assessment.
- The model facilitates the development of predictive tools for clinical decision-making.
Related Concept Videos
01:11Statistical Analysis: Overview
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
01:14Multicompartment 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,...
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
01:21Friedman Two-way Analysis of Variance by Ranks
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...
01:25Statistical 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:
01:26Longitudinal Studies
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
01:20Comparing the Survival Analysis of Two or More Groups
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...