Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Longitudinal Research02:20

Longitudinal Research

12.5K
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...
12.5K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

615
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
615
Longitudinal Studies01:26

Longitudinal Studies

248
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...
248
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

400
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...
400
Sample Size Calculation01:19

Sample Size Calculation

3.8K
Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
3.8K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

289
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...
289

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Optimal group sizes for testing group mean differences using the Bayes factor.

Journal of applied statistics·2026
Same author

Design and analysis of behavioral intervention studies: A Bayesian approach.

PloS one·2026
Same author

The relationship between changes in emotional intensity and treatment outcome in PTSD patients in EMDR therapy.

European journal of psychotraumatology·2025
Same author

Optimal design of cluster randomized crossover trials with a continuous outcome: Optimal number of time periods and treatment switches under a fixed number of clusters or fixed budget.

Behavior research methods·2024
Same author

Bayesian sequential designs in studies with multilevel data.

Behavior research methods·2023
Same author

The effectiveness, efficiency, and acceptability of EMDR vs. EMDR 2.0 vs. the Flash technique in the treatment of patients with PTSD: study protocol for the ENHANCE randomized controlled trial.

Frontiers in psychiatry·2023
Same journal

Planned missingness in intensive longitudinal studies: Extensions and comparisons of multiform designs.

Behavior research methods·2026
Same journal

A validity-guided workflow for robust large language model research in psychology.

Behavior research methods·2026
Same journal

Are 7-point Likert scales preferable to 5-point scales in language research?

Behavior research methods·2026
Same journal

Generative psychometrics via AI-GENIE: Automatic item generation and validation with network-integrated evaluation.

Behavior research methods·2026
Same journal

Exploring psychological tradeoffs: Developing and demonstrating an R Shiny app for Pareto optimization.

Behavior research methods·2026
Same journal

The performance of Bayesian fit measures in detecting misspecified multilevel structural equation modeling.

Behavior research methods·2026
See all related articles

Related Experiment Video

Updated: Sep 13, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

Bayesian sample size determination for longitudinal intervention studies with linear and log-linear growth.

Ulrich Lösener1, Mirjam Moerbeek2

  • 1Department of Methodology and Statistics, Utrecht University, Utrecht, Netherlands. u.c.losener1@uu.nl.

Behavior Research Methods
|July 28, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for sample size determination (SSD) in Bayesian hypothesis testing for longitudinal studies. It provides an R function for SSD in multilevel models, crucial for accurate trial design.

Keywords:
Bayes factorLog-linearLongitudinal dataMonte Carlo simulationMultilevel modelPowerSample size determination

More Related Videos

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

4.1K
Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems
07:41

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

Published on: July 30, 2019

7.6K

Related Experiment Videos

Last Updated: Sep 13, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K
Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
10:26

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities

Published on: September 11, 2021

4.1K
Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems
07:41

Modeling the Size Spectrum for Macroinvertebrates and Fishes in Stream Ecosystems

Published on: July 30, 2019

7.6K

Area of Science:

  • Statistics
  • Biostatistics
  • Psychometrics

Background:

  • Sample size determination (SSD) is critical for efficient, powered studies and is often required by ethics committees and funding agencies.
  • Null hypothesis significance testing (NHST) based SSD has faced criticism; Bayesian hypothesis evaluation using Bayes factors offers an alternative.
  • Current Bayesian SSD tools are limited to simple models, excluding complex longitudinal data where observations are nested within individuals.

Purpose of the Study:

  • To provide a tool for sample size determination (SSD) in Bayesian hypothesis testing for multilevel models with longitudinal data.
  • To offer the necessary theoretical background and practical examples for implementing Bayesian SSD in complex study designs.
  • To address the limitations of existing software by enabling SSD for nested data structures.

Main Methods:

  • The study proposes a simulation-based approach for SSD within a Bayesian framework.
  • It focuses on the application of multilevel models to handle nested data structures inherent in longitudinal experiments.
  • An open-source R function is developed to facilitate custom SSD simulations for researchers.

Main Results:

  • The developed R function allows researchers to perform SSD for multilevel models in a Bayesian context.
  • The tool supports longitudinal data analysis where observations are not independent.
  • This provides a practical solution for designing studies with complex data structures.

Conclusions:

  • This work offers a valuable resource for researchers conducting longitudinal studies requiring Bayesian hypothesis testing.
  • The provided R function simplifies the process of sample size determination for complex multilevel models.
  • Accurate SSD using this tool enhances the rigor and efficiency of research designs involving longitudinal data.