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

Censoring Survival Data01:09

Censoring Survival Data

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 reasons...
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...
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...
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)...
Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
The...
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.

You might also read

Related Articles

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

Sort by
Same author

Identification of an early-formed HIV-1 circulating recombinant form (CRF202_BC) in the southern border region of Yunnan, China.

The Journal of infection·2026
Same author

Identification of a Complex HIV-1 Circulating Recombinant Form (CRF203_cpx) Originating from Three Main Circulating Recombinant Forms in China.

AIDS research and human retroviruses·2026
Same author

Interpretable LightGBM model for predicting postoperative gastrointestinal hemorrhage in elderly hip fracture patients: leveraging systemic inflammation and medication exposures for personalized risk stratification.

BMC geriatrics·2026
Same author

Linking neuroinflammation and neurodegeneration to cognitive decline in HIV.

Brain, behavior, & immunity - health·2026
Same author

Model-based clustering of multiple images incorporating covariates.

Statistical methods in medical research·2026
Same author

Identification of a novel HIV-1 circulating recombinant form (CRF161_0107) from CRF01_AE-C5 lineage among men who have sex with men in southwestern China.

Virologica Sinica·2026

Related Experiment Video

Updated: Jun 4, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

A longitudinal Model for repeated interval-observed data with informative dropouts.

Huichao Chen1, Amita K Manatunga

  • 1Center for Biostatistics in AIDS Research Department of Biostatistics Harvard School of Public Health 651 Huntington Avenue, FXB-607 Boston, MA 02115, USA FAX: (617) 432-3163 hchen@sdac.harvard.edu.

Statistics & Probability Letters
|February 15, 2011
PubMed
Summary
This summary is machine-generated.

This study addresses missing data in longitudinal studies with informative dropouts. We developed a new statistical method to accurately analyze repeated measures data, improving understanding of complex health trends.

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

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

Related Experiment Videos

Last Updated: Jun 4, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

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

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

Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Survival Analysis

Background:

  • Repeated measures data often suffer from informative dropouts, where the reason for missing data is related to the outcome.
  • Existing methods may provide biased results when dropouts are informative.

Purpose of the Study:

  • To develop a statistical model for analyzing interval-observed repeated measures data with informative dropouts.
  • To derive a closed-form marginal likelihood for this model.
  • To evaluate the performance of the proposed estimation method.

Main Methods:

  • Modeling repeated outcomes using an unobserved random intercept.
  • Linking dropout probability to the random intercept via a complementary log-log model.
  • Assuming a power variance function (PVF) family for the random effect.
  • Deriving the marginal likelihood in closed form.
  • Using maximum likelihood estimation (MLE).

Main Results:

  • The proposed method provides a closed-form marginal likelihood, simplifying analysis.
  • Simulation studies demonstrate the performance of the maximum likelihood estimation.
  • The method was successfully applied to a real-world dataset.

Conclusions:

  • The developed statistical approach effectively handles informative dropouts in repeated measures data.
  • This method offers a robust tool for analyzing complex longitudinal data where missingness is not random.
  • The findings have implications for various fields relying on longitudinal data analysis.