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

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

661
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...
661
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

309
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...
309
Censoring Survival Data01:09

Censoring Survival Data

262
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...
262
Survival Tree01:19

Survival Tree

171
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
171
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

332
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...
332
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

414
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...
414

You might also read

Related Articles

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

Sort by
Same author

Examining the Association Between Equity-Related Factors and EQ-5D-3L Health Utilities of Patients with Cancer.

Current oncology (Toronto, Ont.)·2025
Same author

Intercept Estimation of Semi-Parametric Joint Models in the Context of Longitudinal Data Subject to Irregular Observations.

Biometrical journal. Biometrische Zeitschrift·2025
Same author

Why Recommended Visit Intervals Should Be Extracted When Conducting Longitudinal Analyses Using Electronic Health Record Data: Examining Visit Mechanism and Sensitivity to Assessment Not at Random.

Statistics in medicine·2025
Same author

Immediate Death: Not So Bad If You Discount the Future but Still Worse than It Should Be.

Medical decision making : an international journal of the Society for Medical Decision Making·2025
Same author

A Bayesian latent class approach to causal inference with longitudinal data.

Statistical methods in medical research·2024
Same author

EQ-5D-5L population norms and health inequality for Trinidad and Tobago in 2022-2023 and comparison with 2012.

Health and quality of life outcomes·2024

Related Experiment Video

Updated: Sep 25, 2025

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

Variable selection in semiparametric regression models for longitudinal data with informative observation times.

Omidali Aghababaei Jazi1, Eleanor Pullenayegum2

  • 1Department of Mathematical and Computational Sciences, University of Toronto Mississauga, Mississauga, Ontario, Canada.

Statistics in Medicine
|April 25, 2022
PubMed
Summary

This study introduces a new variable selection method for longitudinal data with informative observation times, addressing limitations in current models. The procedure enhances analysis of complex datasets, like those in clinical trials for major depressive disorder.

Keywords:
informative follow-uplongitudinal datasemiparametric regression modelsvariable selection

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
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.4K

Related Experiment Videos

Last Updated: Sep 25, 2025

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
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
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.4K

Area of Science:

  • Statistics
  • Biostatistics
  • Longitudinal Data Analysis

Background:

  • Longitudinal studies often feature irregular patient visits influenced by outcomes (informative observation times).
  • Semiparametric regression models offer flexibility for analyzing longitudinal outcomes but face challenges with large covariate numbers.
  • Existing penalization methods for these models do not adequately handle informative observation times.

Purpose of the Study:

  • To develop a novel variable selection procedure for semiparametric regression models with informative observation times.
  • To address the challenge of selecting relevant covariates when the number of baseline variables is large.
  • To improve the analysis of longitudinal data where visit times are not independent of the outcome.

Main Methods:

  • Proposed a new variable selection procedure tailored for pseudo-score function estimation methods.
  • Investigated the asymptotic properties of the proposed penalized estimators.
  • Conducted simulation studies to validate the theoretical findings.

Main Results:

  • The developed procedure is suitable for semiparametric regression models with informative observation times.
  • Asymptotic properties of penalized estimators were theoretically established.
  • Simulation studies demonstrated the effectiveness of the proposed method.

Conclusions:

  • The new variable selection method effectively handles informative observation times in longitudinal data.
  • The procedure provides a robust approach for covariate selection in complex semiparametric models.
  • Applied the method to the STAR*D dataset for analyzing major depressive disorder treatment outcomes.