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Related Concept Videos

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...
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...
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...
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...
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.
Crossover Experiments01:16

Crossover Experiments

Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.

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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

On outcome-dependent sampling designs for longitudinal binary response data with time-varying covariates.

Jonathan S Schildcrout1, Patrick J Heagerty

  • 1Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37232-2158, USA. jonathan.schildcrout@vanderbilt.edu

Biostatistics (Oxford, England)
|March 29, 2008
PubMed
Summary

Longitudinal studies can improve efficiency by selectively collecting covariate data based on health outcomes. This approach, using outcome-dependent sampling, is now supported by advanced statistical methods for valid inference.

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Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Epidemiology

Background:

  • Traditional longitudinal studies collect repeated measures of health outcomes and covariates.
  • Time-dependent covariates, especially those requiring retrospective ascertainment (e.g., from biological specimens), pose challenges.
  • Existing outcome-dependent sampling designs are not widely adopted, potentially due to limited analysis methods.

Purpose of the Study:

  • To promote the use of outcome-dependent longitudinal sampling designs.
  • To outline and evaluate likelihood-based analysis methods for these designs.
  • To enable resource-efficient collection of covariate information in longitudinal studies.

Main Methods:

  • Utilizes generalized linear mixed models and marginalized models for longitudinal data.
  • Focuses on conditional likelihood analysis for statistical inference.
  • Evaluates the application of these methods to outcome-dependent sampling designs.

Main Results:

  • Recent advancements in statistical modeling provide a flexible framework for longitudinal data.
  • Likelihood-based methods are sufficiently developed to handle complex longitudinal correlations.
  • Conditional likelihood analysis allows for valid statistical inference in outcome-dependent designs.

Conclusions:

  • Outcome-dependent longitudinal sampling designs offer a resource-efficient alternative to traditional methods.
  • The availability of advanced statistical analysis methods makes these designs more feasible.
  • This work advocates for the broader consideration and application of these efficient study designs.