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

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

500
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
500
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

460
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.
460
Longitudinal Studies01:26

Longitudinal Studies

569
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...
569
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

891
Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
891
Study Design in Statistics01:15

Study Design in Statistics

10.1K
A study design is a set of techniques that allow a researcher to collect and analyze data from different variables defined for a specific research problem. Statistics is commonly for effective study design and more robust experiments,
Does aspirin reduce the risk of heart attacks? Is one brand of fertilizer more effective at growing roses than another? Is fatigue as dangerous to a driver as the influence of alcohol? Questions like these are answered using randomized experiments with proper...
10.1K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

658
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...
658

You might also read

Related Articles

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

Sort by
Same author

Lung- and diaphragm-protective mechanical ventilation in acute respiratory distress syndrome.

Intensive care medicine·2026
Same author

A statistical evaluation of decision-making methods and the efficiency of Bayesian multi-arm multi-stage trials.

Clinical trials (London, England)·2026
Same author

Airway Occlusions to Measure Inspiratory Effort, Respiratory Drive, and Lung Mechanics During Noninvasive Ventilation.

American journal of respiratory and critical care medicine·2026
Same author

Remote, bivariate prior elicitation for a Bayesian non-inferiority randomized controlled trial.

Trials·2026
Same author

Technology-Enhanced Strategies to Optimize Positive End-Expiratory Pressure in Patients Receiving Invasive Mechanical Ventilation: A Systematic Review and Meta-Analysis.

Critical care medicine·2026
Same author

Reply to Fishler et al.

Journal of applied physiology (Bethesda, Md. : 1985)·2026

Related Experiment Video

Updated: Feb 25, 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

11.2K

Adjusting for intercurrent events using Bayesian joint models for longitudinal outcomes in clinical trials.

Wen Teng1, Yongdong Ouyang2, Jose Dianti3

  • 1Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada.

Contemporary Clinical Trials Communications
|February 24, 2026
PubMed
Summary

Terminal intercurrent events in clinical trials can bias results. A Bayesian joint modeling approach effectively handles these events, improving treatment effect estimation and increasing statistical power by approximately 15%.

Keywords:
Bayesian joint modelingClinical trialCompeting risksIntercurrent events

More Related Videos

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

15.4K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.7K

Related Experiment Videos

Last Updated: Feb 25, 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

11.2K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

15.4K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.7K

Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Longitudinal Data Analysis

Background:

  • Intercurrent events complicate clinical trial endpoint interpretation and measurement.
  • Terminal events can preclude complete longitudinal outcome assessment, leading to biased estimates if not properly handled.
  • A robust methodology is essential for managing outcome-related terminal intercurrent events.

Purpose of the Study:

  • To propose and evaluate a Bayesian joint modeling approach for handling terminal intercurrent events in clinical trials.
  • To improve the accuracy and reliability of treatment effect estimation in the presence of incomplete outcome data.
  • To provide a principled methodology for both the design and analysis phases of clinical trials.

Main Methods:

  • Developed a Bayesian joint model analyzing longitudinal outcomes and terminal events simultaneously using shared random effects.
  • Employed multiple discrete-time survival submodels to accommodate diverse event types.
  • Conducted extensive simulations mimicking clinical trials with competing risks (e.g., recovery and death).

Main Results:

  • The proposed Bayesian joint modeling approach demonstrated superior statistical power compared to methods ignoring intercurrent events.
  • Power increased by approximately 15% in scenarios with substantial bias from intercurrent events.
  • Joint modeling effectively reduced bias caused by terminal intercurrent events.

Conclusions:

  • The Bayesian joint modeling approach is effective for addressing terminal intercurrent events in clinical trial design and analysis.
  • Explicitly accounting for event-related truncation of longitudinal follow-up enhances precision and reliability of treatment effect estimates.
  • This methodology improves the interpretation of clinical trial outcomes when measurements are incomplete due to terminal events.