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

11.8K
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...
11.8K
Longitudinal Studies01:26

Longitudinal Studies

81
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...
81
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

85
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...
85
Censoring Survival Data01:09

Censoring Survival Data

41
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
41
Study Design in Statistics01:15

Study Design in Statistics

7.7K
A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
7.7K
Cross-Sectional Research01:50

Cross-Sectional Research

11.1K
In cross-sectional research, a researcher compares multiple segments of the population at the same time. If they were interested in people's dietary habits, the researcher might directly compare different groups of people by age. Instead of following a group of people for 20 years to see how their dietary habits changed from decade to decade, the researcher would study a group of 20-year-old individuals and compare them to a group of 30-year-old individuals and a group of 40-year-old...
11.1K

You might also read

Related Articles

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

Sort by
Same author

The DIVINE dose-selection model in daily ART practice: effects on live birth rate and safety.

Reproductive biomedicine online·2026
Same author

Haemorrhagic Safety Update of CLEAR-PATH: 30 Day and 12 Month Antiplatelet Therapy After Peripheral Angioplasty.

EJVES vascular forum·2026
Same author

Normalization of Seasonality and Age Distribution of Pediatric RSV Infection Following the Pandemic Disruption in the Netherlands.

Open forum infectious diseases·2026
Same author

Surgical outcomes for necrotizing enterocolitis in Dutch infants born before 26 weeks' gestation.

BJS open·2025
Same author

Outcome of Recurrent Tracheoesophageal Fistula Treatment After Esophageal Atresia Repair.

Journal of pediatric surgery·2025
Same author

Does primary posterior tracheopexy prevent collapse of the trachea in newborns with oesophageal atresia and tracheomalacia? A study protocol for an international, multicentre randomised controlled trial (PORTRAIT trial).

BMJ open·2024
Same journal

Methods for incorporating test result information within the high-dimensional propensity score framework: application in UK electronic health record data.

BMC medical research methodology·2026
Same journal

Sparse multi-way DMDC for longitudinal classification in high dimension low sample size data.

BMC medical research methodology·2026
Same journal

Tree-based exploratory identification of predictive biomarkers in non-randomized data.

BMC medical research methodology·2026
Same journal

Comparative evaluation of interrupted time series analytical methods for healthcare quality improvement research: a Monte Carlo simulation study.

BMC medical research methodology·2026
Same journal

Methodological advances in claims-based dementia algorithms: integrating medication and clinical data for medicare populations.

BMC medical research methodology·2026
Same journal

An interpretable XGboost algorithm for predicting 30-day mortality in acute pancreatitis using routine biomarkers.

BMC medical research methodology·2026
See all related articles

Related Experiment Video

Updated: May 11, 2025

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

6.2K

Cohort data with dropout: a simulation study comparing five longitudinal analysis methods.

Rebecca K Stellato1, Rutger M van den Bor2, Maria Schipper2

  • 1Department of Data Science and Biostatistics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, GA, 3508, The Netherlands. r.k.stellato@umcutrecht.nl.

BMC Medical Research Methodology
|April 17, 2025
PubMed
Summary
This summary is machine-generated.

Linear mixed effects (LME) and covariance pattern (CP) models are superior for longitudinal studies with missing data. Repeated measures ANOVA (RMA) and t-tests (TT) show bias and poor coverage when data are missing at random (MAR).

Keywords:
Cohort studiesDropoutLongitudinalMissing dataSimulation

More Related Videos

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

3.3K
Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

8.2K

Related Experiment Videos

Last Updated: May 11, 2025

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

6.2K
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

3.3K
Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

8.2K

Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Clinical Research Methods

Background:

  • Longitudinal cohort studies often face challenges with missing data due to participant dropout.
  • Traditional statistical methods like repeated measures ANOVA (RMA) and t-tests (TT) may yield biased results in the presence of missing data.
  • Advanced models such as linear mixed effects (LME), covariance pattern (CP), and generalized estimating equations (GEE) are proposed as alternatives.

Purpose of the Study:

  • To compare the performance of LME, CP, and GEE models against RMA and TT in longitudinal studies with missing data.
  • To visually demonstrate the impact of missing data (MCAR and MAR) on the accuracy and reliability of different statistical analyses.
  • To evaluate bias, confidence interval coverage, and statistical power across various analytical methods under different dropout scenarios.

Main Methods:

  • Simulated data for a health-related quality of life (HRQoL) study in children post-surgery.
  • Two dropout scenarios were generated: missing completely at random (MCAR) at 4-10% and missing at random (MAR) at 10-40%.
  • Five analysis methods (LME, CP, GEE, RMA, TT) were applied to assess bias, confidence interval coverage, and power for within- and between-group comparisons.

Main Results:

  • All methods performed well under MCAR conditions with negligible bias and good coverage.
  • Under MAR conditions, RMA and TT showed increasing bias, reduced coverage, and lower power with higher dropout rates.
  • LME and CP models consistently provided unbiased estimates and maintained ~95% coverage, even with 40% MAR data, outperforming GEE, RMA, and TT.

Conclusions:

  • LME and CP models are the most robust and reliable methods for analyzing longitudinal data with missing at random (MAR) dropout.
  • RMA and paired t-tests (TT) are unsuitable for longitudinal data with MAR dropout due to significant bias and poor precision.
  • Researchers should prioritize LME or CP models for longitudinal studies experiencing MAR data to ensure valid and accurate findings.