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

Study Design in Statistics01:15

Study Design in Statistics

10.3K
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...
10.3K
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs01:15

Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs

383
Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
383
Experimental Designs01:16

Experimental Designs

18.5K
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...
18.5K
Crossover Experiments01:16

Crossover Experiments

4.7K
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.
4.7K
Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs01:20

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

366
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...
366
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

486
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.
486

You might also read

Related Articles

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

Sort by
Same author

Rethinking Probability of Success as Bayes Utility.

Biometrical journal. Biometrische Zeitschrift·2025
Same author

Hybrid classical-Bayesian approach to sample size determination for two-arm superiority clinical trials.

The international journal of biostatistics·2024
Same author

On the distribution of the power function for the scale parameter of exponential families.

Statistics in medicine·2024
Same author

A dynamic power prior approach to non-inferiority trials for normal means.

Pharmaceutical statistics·2023
Same author

Exact sample size determination for a single Poisson random sample.

Biometrical journal. Biometrische Zeitschrift·2023
Same author

Borrowing historical information for non-inferiority trials on Covid-19 vaccines.

The international journal of biostatistics·2022

Related Experiment Video

Updated: Mar 25, 2026

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

11.7K

Continuous endpoints in Bayesian two-stage designs.

Pierpaolo Brutti1, Fulvio De Santis1, Stefania Gubbiotti1

  • 1a Dipartimento di Scienze Statistiche , Sapienza Università di Roma , Rome , Italy.

Journal of Biopharmaceutical Statistics
|February 20, 2016
PubMed
Summary

This study introduces a novel Bayesian two-stage clinical trial design for phase II trials. It enhances treatment efficacy assessment by directly using continuous endpoints, preserving valuable information.

Keywords:
Analysis priorDesign priorSample size determinationSkew normal distribution

More Related Videos

A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

11.6K
RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

3.5K

Related Experiment Videos

Last Updated: Mar 25, 2026

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

11.7K
A Two-interval Forced-choice Task for Multisensory Comparisons
07:13

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

11.6K
RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans
11:09

RBDT: A Computerized Task System based in Transposition for the Continuous Analysis of Relational Behavior Dynamics in Humans

Published on: July 17, 2021

3.5K

Area of Science:

  • Clinical Trial Design
  • Biostatistics
  • Oncology

Background:

  • Phase II clinical trials often use binary endpoints derived from continuous measures, leading to information loss.
  • Traditional designs may not fully leverage the available data for treatment efficacy assessment.

Purpose of the Study:

  • To propose an improved two-stage clinical trial design for phase II studies.
  • To directly utilize continuous endpoints, avoiding information loss associated with dichotomization.
  • To apply a Bayesian predictive approach for enhanced analysis.

Main Methods:

  • Development of a single-arm, two-stage clinical trial design.
  • Incorporation of a Bayesian predictive framework.
  • Direct use of continuous efficacy endpoints, such as tumor shrinkage.

Main Results:

  • The proposed Bayesian design effectively utilizes continuous data, preserving information.
  • Numerical results demonstrate the design's applicability in phase II cancer trials.
  • The approach offers a more sensitive assessment of treatment effects compared to binary endpoints.

Conclusions:

  • The Bayesian predictive two-stage design offers a statistically robust alternative for phase II trials.
  • This method enhances the evaluation of experimental treatments by preserving data integrity.
  • Recommended for oncology trials assessing continuous efficacy measures like tumor response.