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Related Concept Videos

Longitudinal Studies01:26

Longitudinal Studies

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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...
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Truncation in Survival Analysis01:09

Truncation in Survival Analysis

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Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
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Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

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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.
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Longitudinal Research02:20

Longitudinal Research

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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...
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Multicompartment Models: Overview01:14

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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.
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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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...
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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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A transition copula model for analyzing multivariate longitudinal data with missing responses.

A Ahmadi1, T Baghfalaki1, M Ganjali2

  • 1Department of Statistics, Faculty of Mathematical Sciences, Tarbiat Modares University, Tehran, Iran.

Journal of Applied Statistics
|August 29, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel copula-based approach for analyzing complex longitudinal data with multiple outcomes. The method effectively models associations between outcomes and over time, demonstrating its utility in real-world obesity research.

Keywords:
Copula functionlongitudinal datamissingnessmixed outcomestransition models

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Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Multivariate longitudinal studies involve repeated measurements of multiple outcomes over time.
  • These studies present challenges in modeling both cross-sectional and time-dependent associations.
  • Existing methods may not adequately capture the complex dependencies inherent in such data.

Purpose of the Study:

  • To develop a flexible statistical framework for joint modeling of multivariate longitudinal outcomes.
  • To account for both the association between different outcomes at a specific time point and the association of repeated measurements over time for a single outcome.
  • To address the issue of incomplete data in longitudinal studies.

Main Methods:

  • A copula-based approach is employed for joint modeling of multivariate outcomes at each time point.
  • A transition model is utilized to capture the association of longitudinal measurements over time.
  • The missingness mechanism is assumed to be ignorable.
  • Simulation studies are conducted using Gaussian, t, and Archimedean copulas.
  • Akaike Information Criterion (AIC) is used for copula selection.

Main Results:

  • The proposed method demonstrates flexibility in handling various marginal distributions.
  • Simulation results show the performance of the approach under different scenarios.
  • The Akaike Information Criterion effectively aids in selecting the most appropriate copula function.
  • The approach is successfully applied to a real-world obesity dataset.

Conclusions:

  • The copula-based joint modeling with a transition component provides a robust framework for multivariate longitudinal data.
  • The method effectively handles complex associations and incomplete data.
  • The approach offers a valuable tool for analyzing complex health-related datasets, such as obesity data.