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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

323
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
323
Randomized Experiments01:13

Randomized Experiments

9.3K
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...
9.3K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

533
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,...
533
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

705
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...
705
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

311
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...
311
Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

368
Bioequivalence experimental study designs are crucial methodologies used in evaluating and comparing the bioavailability of different drug products. These designs are categorized into various types: completely randomized, randomized block, repeated measures, cross and carry-over, and Latin square designs.Completely randomized designs involve randomly allocating treatments to all subjects participating in the experiment. This allocation is achieved by assigning unique random numbers to subjects...
368

You might also read

Related Articles

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

Sort by
Same author

Coded Caching Scheme for Multiaccess Cache-Assisted Partially Connected Linear Network via Multi-Antenna Placement Delivery Array.

Entropy (Basel, Switzerland)·2026
Same author

Avian Pathogenic Escherichia coli T6SS Effector Protein Hcp2 Induces Mitochondrial Dysfunction and Activates Mitophagy in Chicken Tracheal Mucosal Epithelial Cells.

Transboundary and emerging diseases·2026
Same author

p53 overrides <i>METTL5</i> loss-induced tumor suppression via mitochondrial respiration.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Solving the Hubbard model with neural quantum states.

Nature communications·2026
Same author

A novel ferritin-based nanoparticle vaccine targeting lgE confers protection against chronic allergic asthma in a murine model.

Vaccine·2026
Same author

In situ spatiotemporal tracking of endogenous protein dynamics with allosteric genetically encoded biosensor in living cells.

Biosensors & bioelectronics·2026
Same journal

Impact of Information Leakage in Platform Trials With Survival Endpoints on Type I Error Control.

Pharmaceutical statistics·2026
Same journal

Harmonic Fowlkes-Mallows Index for Medical Diagnostics Tests and Optimal Cut-Off Point Selection of Binary Diseases.

Pharmaceutical statistics·2026
Same journal

Early Phase Dose-Finding Designs for CAR-T Cell Therapies.

Pharmaceutical statistics·2026
Same journal

Optimizing Randomization Ratios in Clinical Trials With Survival Endpoints.

Pharmaceutical statistics·2026
Same journal

CUI-MET: A Clinical Utility Index Based Analysis and Decision Framework for Dose Optimization in Multiple-Dose, Multiple-Outcome Randomized Trials.

Pharmaceutical statistics·2026
Same journal

Will the Pharmaceutical Industry Need Statisticians in an AI World?

Pharmaceutical statistics·2026
See all related articles
  1. Home
  3. A Constrained Hierarchical Bayesian Model Considering Latent Biomarker Subgroups For Time-to-event Endpoints In Randomized Phase Ii Trials.
  1. Home
  3. A Constrained Hierarchical Bayesian Model Considering Latent Biomarker Subgroups For Time-to-event Endpoints In Randomized Phase Ii Trials.

Related Experiment Video

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K

A Constrained Hierarchical Bayesian Model Considering Latent Biomarker Subgroups for Time-To-Event Endpoints in

Yifei Huang1, Kentaro Takeda1, Yongyun Zhao1

  • 1Quantitative Science and Evidence Generation, Astellas Pharma Global Development, Inc., Northbrook, Illinois, USA.

Pharmaceutical Statistics
|March 29, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a new Bayesian model for Phase II oncology trials. It uses biomarker data to better predict long-term survival benefits and improve treatment effect estimates.

Keywords:
Bayesian adaptive designbiomarkerconstrained hierarchical Bayesian modelheterogeneitylatent subgroup

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.5K
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.8K

Related Experiment Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.5K
A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.8K

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Oncology Research

Background:

  • Phase II oncology trials often use short-term tumor response as a surrogate for long-term survival, increasing Phase III failure rates.
  • Biomarker data can predict treatment response, necessitating biomarker-based trial enrichment strategies.
  • Current methods may not fully capture treatment effect heterogeneity across biomarker subgroups.

Purpose of the Study:

  • To propose a constrained hierarchical Bayesian model with latent biomarker subgroups (CHBM-LS) for Phase II randomized trials.
  • To improve the accuracy of long-term time-to-event endpoint evaluation in early-phase oncology studies.
  • To enhance the detection of true treatment effects by accounting for biomarker-driven heterogeneity.

Main Methods:

  • Developed a constrained hierarchical Bayesian model (CHBM-LS) to analyze time-to-event endpoints.
  • Incorporated latent biomarker subgroups to model heterogeneity in treatment effects.
  • Compared CHBM-LS performance against existing approaches in simulations or analyses.

    • Main Results:

    • CHBM-LS improves the accuracy of hazard ratio estimation compared to other methods.
    • The proposed model increases statistical power to detect true treatment effects.
    • CHBM-LS maintains control over Type I error rates while optimizing participant selection.

    Conclusions:

    • CHBM-LS offers a robust framework for biomarker-guided Phase II oncology trial design.
    • This approach enhances the reliability of early-phase trial results for predicting survival benefits.
    • Biomarker enrichment using CHBM-LS can lead to more successful progression to Phase III trials.