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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...
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

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 observed.
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.
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)...
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...

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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

Missing data methods in longitudinal studies: a review.

Joseph G Ibrahim1, Geert Molenberghs

  • 1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, USA ibrahim@bios.unc.edu.

Test (Madrid, Spain)
|September 28, 2011
PubMed
Summary
This summary is machine-generated.

Handling incomplete research data is crucial, especially in longitudinal studies. This work details missing data concepts, methods, and tools, using breast cancer and childhood obesity case studies.

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Last Updated: Jun 5, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Published on: January 8, 2020

Area of Science:

  • Biostatistics
  • Epidemiology
  • Health Services Research

Background:

  • Incomplete data are prevalent in biomedical and longitudinal research.
  • Significant advancements in missing-data methodologies have occurred over the past three decades.
  • A comprehensive taxonomy of missing-data concepts, issues, and methods has emerged.

Purpose of the Study:

  • To provide a detailed overview of missing-data concepts, issues, and methods.
  • To present a variety of concrete data-analytic tools for handling missing data.
  • To illustrate the application of these methods through real-world case studies.

Main Methods:

  • Description of missing data patterns, mechanisms, and modeling frameworks.
  • Explanation of inferential paradigms for incomplete datasets.
  • Presentation of sensitivity analysis frameworks.
  • Illustrative application of various data-analytic tools.

Main Results:

  • The study details a rich taxonomy of missing-data concepts and methods.
  • Various concrete modeling devices and data-analytic tools are presented.
  • Case studies demonstrate practical applications in quality of life research and childhood obesity studies.

Conclusions:

  • Effective management of incomplete data is essential for robust research findings.
  • The presented taxonomy and tools offer a framework for addressing missing data challenges.
  • The case studies highlight the utility of advanced methods in diverse research areas.