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

Truncation in Survival Analysis01:09

Truncation in Survival Analysis

680
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...
680
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

473
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.
473
Longitudinal Studies01:26

Longitudinal Studies

615
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...
615
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.2K
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...
1.2K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

318
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...
318
Clearance Models: Noncompartmental Models01:17

Clearance Models: Noncompartmental Models

337
Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...
337

You might also read

Related Articles

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

Sort by
Same author

Integrative learning of individualized treatment rules from multiple studies with partially overlapping treatments.

Biometrics·2026
Same author

SEMIPARAMETRIC ANALYSIS OF INTERVAL-CENSORED DATA SUBJECT TO INACCURATE DIAGNOSES WITH A TERMINAL EVENT.

The annals of applied statistics·2026
Same author

DYNAMIC CLASSIFICATION OF LATENT DISEASE PROGRESSION WITH AUXILIARY SURROGATE LABELS.

The annals of applied statistics·2026
Same author

Asymptotic Inference for Multi-Stage Stationary Treatment Policy with Variable Selection.

Journal of machine learning research : JMLR·2026
Same author

Data fusion methods for the heterogeneity of treatment effect and confounding function.

Bernoulli : official journal of the Bernoulli Society for Mathematical Statistics and Probability·2026
Same author

Leveraging precision medicine analytics to optimize inflammation reduction and enhance physical function in older adults.

The journals of gerontology. Series A, Biological sciences and medical sciences·2026
Same journal

Statistical analysis of disease onset during lifespan with left truncation.

Biometrics·2026
Same journal

Interim analysis in sequential multiple assignment randomized trials for survival outcomes.

Biometrics·2026
Same journal

Acknowledgment of Referees 2025.

Biometrics·2026
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
See all related articles

Related Experiment Video

Updated: Mar 17, 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

6.8K

Joint partially linear model for longitudinal data with informative drop-outs.

Sehee Kim1, Donglin Zeng2, Jeremy M G Taylor1

  • 1Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, U.S.A.

Biometrics
|August 2, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a joint partially linear model to accurately estimate biomarker trajectories in longitudinal studies, accounting for informative patient drop-outs. The novel approach improves data analysis by modeling both biomarker changes and dropout risks simultaneously.

Keywords:
Joint modelsLongitudinal dataNonparametric maximum likelihoodPartially linear modelRandom effectsSieve maximum likelihoodTransformation models

More Related Videos

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

11.2K
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

3.8K

Related Experiment Videos

Last Updated: Mar 17, 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

6.8K
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

11.2K
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

3.8K

Area of Science:

  • Biostatistics
  • Longitudinal Data Analysis
  • Biomedical Research

Background:

  • Longitudinal biomarkers can indicate disease progression, leading to informative patient drop-outs.
  • Ignoring informative drop-outs introduces bias in longitudinal model estimations.
  • Standard models may fail to capture complex biomarker patterns with drop-outs.

Purpose of the Study:

  • To develop a joint partially linear model for analyzing longitudinal biomarker data with informative drop-outs.
  • To accurately estimate the trajectory of longitudinal biomarkers.
  • To provide a flexible semiparametric approach for modeling drop-out mechanisms.

Main Methods:

  • Developed a joint partially linear model incorporating an arbitrary function of time and linear covariate effects for biomarkers.
  • Employed a flexible semiparametric transformation model for the drop-out mechanism.
  • Utilized a sieve maximum likelihood estimation procedure with the EM algorithm and AIC/BIC for model selection.

Main Results:

  • The proposed semiparametric joint modeling approach offers easier interpretation than nonparametric models.
  • This method effectively controls for shared prognostic factors influencing both biomarker trajectories and drop-out.
  • Asymptotic properties of the estimators were demonstrated through empirical process theory.

Conclusions:

  • The developed joint partially linear model is effective for analyzing longitudinal data with informative drop-outs.
  • This approach enhances the accuracy of biomarker trajectory estimation in biomedical studies.
  • The method was validated through simulation studies and applied to prostate cancer data.