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

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...
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
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...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

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 Cox...
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

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 number is...

You might also read

Related Articles

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

Sort by
Same author

The impact of affective symptoms and mood instability on sexual desire and sexual distress in newly diagnosed bipolar disorder: a longitudinal study.

Journal of psychiatric research·2026
Same author

Superior frontal and hippocampal structures associated with onset versus recurrence of mood disorders in monozygotic twins.

Molecular psychiatry·2026
Same author

Variation in sleep and instability in mood in patients with bipolar disorder and the association between these - an exploratory post hoc study on daily smartphone-based data from two separate randomised controlled trials.

Journal of affective disorders·2026
Same author

Recommendations of the ECNP Digital Network for the evaluation of mental health care apps: A Delphi consensus.

European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology·2026
Same author

Bipolar disorder and the risk of developing dementia - a systematic review and meta-analysis.

Current opinion in psychiatry·2026
Same author

Retrospective Assessment of Food Noise Changes After Initiation of Injectable Semaglutide for Weight Management in the USA: The INFORM Survey.

Advances in therapy·2026

Related Experiment Video

Updated: Jul 10, 2026

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

Non-parametric estimation and model checking procedures for marginal gap time distributions for recurrent events.

Kajsa Kvist1, Mette Gerster, Per Kragh Andersen

  • 1Department of Biostatistics, Institute of Public Health, University of Copenhagen, Copenhagen, Denmark. kakv@biostat.ku.dk

Statistics in Medicine
|November 13, 2007
PubMed
Summary

This study adapts a model-checking procedure for recurrent event data to detect bias from misspecified frailty distributions. The method is effective only for common recurrent events and weak intra-individual associations.

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

Related Experiment Videos

Last Updated: Jul 10, 2026

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

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

Area of Science:

  • Biostatistics
  • Epidemiology
  • Survival Analysis

Background:

  • Misspecification of frailty distribution in recurrent event models can lead to significant bias in regression coefficient estimation.
  • Existing model-checking procedures for parallel data require adaptation for the complexities of recurrent events.

Purpose of the Study:

  • To adapt a model-checking procedure for gamma frailty to recurrent event data.
  • To investigate the performance of a non-parametric estimator for joint gap time distributions within this model-checking framework.
  • To assess the applicability of the adapted procedure using simulations and real-world registry data.

Main Methods:

  • Adaptation of a parallel data procedure for checking gamma frailty to recurrent events.
  • Application of a non-parametric estimator based on inverse probability of censoring weights for joint gap time distributions.
  • Performance evaluation through simulations and analysis of Danish registry data, comparing with Kaplan-Meier and marginalized estimators.

Main Results:

  • The adapted model-checking procedure demonstrates effectiveness under specific conditions.
  • Successful application requires the recurrent event to be common.
  • The intra-individual association between gap times must be weak for the procedure to be reliable.

Conclusions:

  • The proposed model-checking procedure for recurrent events is sensitive to the frequency of events and the degree of dependence between them.
  • The method's utility is limited to scenarios with common recurrent events and weak intra-individual associations.
  • Further research may be needed to refine the procedure for broader applicability in survival analysis.