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

Group Design02:01

Group Design

8.9K
The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
8.9K
Crossover Experiments01:16

Crossover Experiments

2.7K
Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
Crossover designs are performed even with smaller sample sizes since the samples can act as their controls. These are better than simple randomized trials since patients are exposed to all the treatments.
2.7K
Behavioral Genetics and Its Designs01:23

Behavioral Genetics and Its Designs

325
Behavior genetics explores how genetic inheritance influences human behavior. It focuses on how genes, passed from parents to offspring, contribute to the development of behavioral traits and tendencies. This branch of genetics seeks to understand the complex interplay between inherited genetic factors and environmental influences in shaping our behaviors.
The primary methodologies used in behavior genetics include family studies, twin studies, and adoption studies, each providing unique...
325
Study Design in Statistics01:15

Study Design in Statistics

7.8K
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...
7.8K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

27
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...
27
Experimental Designs01:16

Experimental Designs

11.1K
An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...
11.1K

You might also read

Related Articles

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

Sort by
Same author

Implementing Project Optimus in Oncology Dosage Optimization: Where are We Now?

Therapeutic innovation & regulatory science·2026
Same author

Correlation between tumor growth rate and survival in patients with metastatic breast cancer treated with trastuzumab deruxtecan.

The oncologist·2025
Same author

Parent Quality of Life After Disclosure of Pediatric Oncology Germline Sequencing Results.

JCO precision oncology·2023
Same author

Pyruvate Kinase Deficiency Causing Priapism.

Case reports in hematology·2023
Same author

Tumor Mutational Burden as a Predictor of Survival with Durvalumab and/or Tremelimumab Treatment in Recurrent or Metastatic Head and Neck Squamous Cell Carcinoma.

Clinical cancer research : an official journal of the American Association for Cancer Research·2023
Same author

Decreasing Burnout and Improving Work Environment: The Impact of <i>Firgun</i> on a Pediatric Hematopoietic Cell Transplant Team.

JCO oncology practice·2022
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

Related Experiment Video

Updated: Jun 4, 2025

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

3.3K

A Bayesian Hybrid Design With Borrowing From Historical Study.

Zhaohua Lu1, John Toso2, Girma Ayele1

  • 1Biostatistics, Daiichi Sankyo Inc, Basking Ridge, USA.

Pharmaceutical Statistics
|December 28, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian dynamic power prior framework for early-phase cancer clinical trials. This hybrid design enhances decision-making by borrowing historical data, improving assessment of combination therapy efficacy.

Keywords:
Bayesian hybrid designdynamic borrowingnonconcurrent controlpower prior

More Related Videos

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.3K
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

11.3K

Related Experiment Videos

Last Updated: Jun 4, 2025

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

3.3K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.3K
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

11.3K

Area of Science:

  • Oncology
  • Biostatistics
  • Clinical Trial Design

Background:

  • Early-phase drug development for combination therapy often uses uncontrolled single-arm trials.
  • Assessing additive activity of novel agents with established monotherapy presents challenges due to feasibility and lack of randomization.
  • Traditional trial designs struggle with robust decision-making for proceeding to later phases.

Purpose of the Study:

  • To introduce a novel hybrid clinical trial design for early-phase combination therapy assessment.
  • To enhance study efficiency and improve decision-making by leveraging historical data.
  • To address the challenge of information borrowing in hybrid designs.

Main Methods:

  • Development of a Bayesian dynamic power prior (DPP) framework.
  • Incorporation of three components for controlling the amount of dynamic borrowing.
  • Utilizing a closed-form posterior distribution for computational efficiency.

Main Results:

  • The DPP framework offers flexible study design with interpretable borrowing.
  • Demonstrated enhanced statistical power through borrowing external historical data.
  • Simulations and a case study confirmed the framework's utility and efficiency.

Conclusions:

  • The proposed Bayesian DPP framework provides a robust and efficient hybrid design for early-phase cancer trials.
  • This approach facilitates more informed decisions regarding combination therapy development.
  • The framework allows for customizable borrowing strategies and computational advantages.