Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...

Randomized Experiments

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...

Parametric Survival Analysis: Weibull and Exponential Methods

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...

Assumptions of Survival Analysis

Assumptions of Survival Analysis

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.

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 Baseline Sleep as a Potential Moderator of Weight Loss Intervention in Breast Cancer Survivors: Results From the POWER-Remote Trial.

Obesity (Silver Spring, Md.)·2025

Same author

Comparison of continuous, binary, and ordinal endpoints.

Journal of biopharmaceutical statistics·2025

Same author

A Bias-Corrected Bayesian Nonparametric Model for Combining Studies With Varying Quality in Meta-Analysis.

Biometrical journal. Biometrische Zeitschrift·2025

Same author

A Bayesian decision-theoretic design for simultaneous biomarker-based subgroup selection and efficacy evaluation.

Statistics in biopharmaceutical research·2023

Same author

Bayesian modelling of response to therapy and drug-sensitivity in acute lymphoblastic leukemia.

Research square·2023

Same author

A Phase 1 Trial of Durvalumab in Combination with Bacillus Calmette-Guerin (BCG) or External Beam Radiation Therapy in Patients with BCG-unresponsive Non-muscle-Invasive Bladder Cancer: The Hoosier Cancer Research Network GU16-243 ADAPT-BLADDER Study.

European urology·2023

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

Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026

Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026

Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 25, 2025

A Tactile Automated Passive-Finger Stimulator TAPS

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

A Bayesian model with application for adaptive platform trials having temporal changes.

Chenguang Wang¹, Min Lin², Gary L Rosner¹

¹Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA.

|April 27, 2022

Summary

This summary is machine-generated.

Temporal changes can impact clinical trial results, especially in adaptive platform trials. This study introduces a Bayesian method using hidden Markov models to mitigate these temporal effects, improving trial analysis accuracy.

Keywords:

adaptive platform trial clinical trial design complex innovative design temporal change temporal effect-adjusted prior

More Related Videos

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

Related Experiment Videos

Last Updated: Sep 25, 2025

A Tactile Automated Passive-Finger Stimulator TAPS

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments

Published on: January 23, 2017

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

Area of Science:

Biostatistics
Clinical Trial Design
Epidemiology

Background:

Temporal changes in patient populations and trial conduct can affect clinical trial outcomes.
Randomization in standard trials mitigates temporal effects, but complex designs like adaptive platform trials require explicit methods.
Adaptive platform trials are increasingly used in medical product development, necessitating robust analytical approaches.

Purpose of the Study:

To introduce a novel Bayesian robust prior method for mitigating temporal effects in clinical trials.
To propose a particle filtering algorithm for the computation of the proposed Bayesian method.
To evaluate the performance of the new method through simulation studies and real-world examples.

Main Methods:

Development of a Bayesian robust prior based on a hidden Markov model to account for temporal variations.
Implementation of a particle filtering algorithm for efficient computation of the Bayesian model.
Simulation studies to assess the method's effectiveness under various temporal change scenarios.

Main Results:

The proposed Bayesian method effectively mitigates temporal effects in clinical trial data.
Simulation studies demonstrated the robustness and accuracy of the hidden Markov model-based approach.
Illustrative examples showed successful application in Ebola virus disease therapeutics and vascular surgery hemostat trials.

Conclusions:

The Bayesian robust prior method offers a powerful tool for addressing temporal effects in complex clinical trial designs.
The proposed particle filtering algorithm provides a computationally efficient solution for applying this method.
This approach enhances the reliability of clinical trial analysis, particularly for adaptive platform trials.