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Related Concept Videos

Longitudinal Studies01:26

Longitudinal 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...
Longitudinal Research02:20

Longitudinal Research

Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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 until a...
Multiple Regression01:25

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
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.
Comparing the Survival Analysis of Two or More Groups01:20

Comparing 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...

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Related Experiment Video

Updated: Jul 6, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

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

Published on: September 17, 2019

Discriminant analysis for longitudinal data with multiple continuous responses and possibly missing data.

Guillermo Marshall1, Rolando De la Cruz-Mesía, Fernando A Quintana

  • 1Departamento de Estadística, Facultad de Matemáticas, Pontificia Universidad Católica de Chile, Casilla 306, Correo 22, Santiago, Chile.

Biometrics
|March 28, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces statistical models for classifying subjects using repeated measurements on multiple outcomes over time. The methods accurately predict group differences, aiding in understanding complex biological processes and health outcomes.

Related Experiment Videos

Last Updated: Jul 6, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

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

Published on: September 17, 2019

Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Longitudinal Data Analysis

Background:

  • Characterizing effects often requires analyzing multiple outcomes.
  • Repeated measurements over time are crucial for understanding dynamic processes.
  • Classifying units into groups based on longitudinal data presents statistical challenges.

Purpose of the Study:

  • To present model-based statistical methods for classifying units into groups using multivariate longitudinal data.
  • To describe evolutions in different groups using multivariate nonlinear mixed-effects models.
  • To estimate parameters for a discriminant model using a random-effects approach for joint modeling.

Main Methods:

  • Multivariate nonlinear mixed-effects models for joint modeling of multiple outcomes.
  • Random-effects approach for discriminant model parameter estimation.
  • Expectation-maximization algorithm with a linear approximation step for parameter estimation.
  • Simulation study to assess the impact of linear approximation on classification.

Main Results:

  • The proposed random-effects approach effectively models multiple outcomes over time.
  • The expectation-maximization algorithm with linear approximation provides parameter estimates for classification.
  • Simulation results indicate the influence of linear approximation on classification accuracy.
  • The methodology is applied to predict normal versus abnormal pregnancy outcomes in a real-world study.

Conclusions:

  • Model-based statistical methods offer a flexible framework for classifying units with longitudinal multivariate data.
  • The random-effects approach facilitates joint modeling and discriminant analysis.
  • Accurate parameter estimation is crucial for reliable classification, and the impact of approximations should be considered.
  • The developed methods have practical applications in health sciences, such as pregnancy outcome prediction.