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

Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

2.3K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
2.3K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

321
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...
321
Longitudinal Studies01:26

Longitudinal Studies

265
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...
265
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

336
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...
336
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

319
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
319
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

681
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...
681

You might also read

Related Articles

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

Sort by
Same author

Variable selection-combined causal mediation analysis for continuous treatments with application to large-dimensional biomedical data.

PLoS computational biology·2026
Same author

Structured Exercise Program After Adjuvant Chemotherapy for Colon Cancer: A Cost-Utility Analysis of the CHALLENGE Trial.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2026
Same author

Clinical and molecular correlates of circulating tumor fraction in patients with metastatic pancreatic ductal adenocarcinoma.

Clinical cancer research : an official journal of the American Association for Cancer Research·2026
Same author

DNA Repair gene alterations and efficacy from gemcitabine and nab-paclitaxel with/without durvalumab and tremelimumab in metastatic pancreatic ductal adenocarcinoma.

Nature communications·2026
Same author

A Tobit Partly Linear Mixed and Mixture Cure Model for the Joint Analysis of Interval-Bounded Longitudinal Measurements and Survival Times With Cure Proportion.

Pharmaceutical statistics·2026
Same author

Transfer learning reveals the mediating mechanisms of cross-ethnic lipid metabolic pathways in the association between APOE gene and Alzheimer's disease.

Briefings in bioinformatics·2025

Related Experiment Video

Updated: Sep 28, 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

6.4K

Multiply robust subgroup analysis based on a single-index threshold linear marginal model for longitudinal data with

Kecheng Wei1, Huichen Zhu2, Guoyou Qin1

  • 1Department of Biostatistics, School of Public Health, Fudan University, Shanghai, China.

Statistics in Medicine
|March 29, 2022
PubMed
Summary

This study introduces a new statistical model for precision medicine, identifying patient subgroups sensitive to treatments using longitudinal data with dropouts. The method enhances subgroup analysis for personalized treatment strategies.

Keywords:
dropoutsmultiply robustpenalized smoothed generalized estimating equationsingle-index threshold regressionsubgroup analysis

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

3.4K
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

10.8K

Related Experiment Videos

Last Updated: Sep 28, 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

6.4K
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
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

10.8K

Area of Science:

  • Biostatistics
  • Medical Research Methodology
  • Precision Medicine

Background:

  • Identifying patient subpopulations for targeted treatments is crucial for precision medicine.
  • Longitudinal data analysis with missing values (dropouts) presents challenges for subgroup identification.
  • Current subgroup analysis methods are limited for complex longitudinal data.

Purpose of the Study:

  • To develop a statistical model for identifying treatment-sensitive subgroups in longitudinal data with dropouts.
  • To simultaneously estimate treatment effects and test for differential effects across subgroups.
  • To address selection bias due to missing data in subgroup analysis.

Main Methods:

  • A single-index threshold linear marginal model is proposed.
  • Penalized smoothed generalized estimating equations are used for parameter estimation.
  • Multiply robust weighting matrices correct for missingness bias, allowing for multiple missingness models.

Main Results:

  • The proposed method consistently estimates treatment effects even if only one missingness model is correct.
  • Asymptotic normality of the estimator is established under regularity conditions.
  • Simulation studies demonstrate favorable finite-sample performance.

Conclusions:

  • The developed statistical framework effectively identifies patient subgroups with differential treatment effects.
  • The method provides a robust approach for analyzing longitudinal data with dropouts in clinical research.
  • Application to pancreatic cancer trial data showcases practical utility.