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

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

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

358
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...
358
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

373
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...
373
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
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
Study Design in Statistics01:15

Study Design in Statistics

10.2K
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...
10.2K
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

696
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...
696

You might also read

Related Articles

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

Sort by
Same author

Treatment of Progressive Multifocal Leukoencephalopathy with Third-Party Allogeneic BK Virus T Cells.

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America·2026
Same author

Model-Assisted Bayesian Estimators of Transparent Population Level Summary Measures for Ordinal Outcomes in Randomized Controlled Trials.

Statistics in medicine·2026
Same author

BAR12: Bayesian Autoregressive Phase 1-2 Design for Cell Therapy Trials With Manufacturing Changes.

Statistics in medicine·2026
Same author

Exercise priming to enhance therapeutic bond and behavioral activation in CBT for MDD: a randomized controlled target-engagement trial with remission signal.

Journal of affective disorders·2026
Same author

mHealth Intervention to Improve Hypertension Care in High-Risk Patients.

Hypertension (Dallas, Tex. : 1979)·2026
Same author

Tipping point analysis in network meta-analysis.

Research synthesis methods·2026

Related Experiment Video

Updated: Mar 21, 2026

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

15.5K

Utility-based designs for randomized comparative trials with categorical outcomes.

Thomas A Murray1, Peter F Thall2, Ying Yuan2

  • 1Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Unit 1411, P.O. Box 301402, Houston, TX, 77030-1402, U.S.A.. tamurray@mdanderson.org.

Statistics in Medicine
|May 19, 2016
PubMed
Summary

This study introduces a utility-based testing method for clinical trials with categorical outcomes. It uses Bayesian tests and numerical utilities to create a one-dimensional criterion for treatment comparison, enhancing trial design and analysis.

Keywords:
Bayesian methodsDirichlet-multinomialmultiple outcomesoncologyrandomized comparative trialsutility elicitation

More Related Videos

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
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

11.0K

Related Experiment Videos

Last Updated: Mar 21, 2026

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

15.5K
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
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

11.0K

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Decision Analysis

Background:

  • Randomized comparative clinical trials often involve categorical outcomes, posing challenges for standard statistical testing.
  • Existing methodologies may not fully incorporate patient preferences or the multifaceted nature of treatment outcomes, including efficacy and toxicity.
  • A unified framework is needed to handle complex categorical outcomes and optimize trial design.

Purpose of the Study:

  • To present a general utility-based testing methodology for designing and conducting randomized comparative clinical trials with categorical outcomes.
  • To develop Bayesian statistical tests that incorporate elicited numerical utilities to quantify outcome desirabilities.
  • To provide practical guidelines and computational tools for implementing this methodology in clinical research.

Main Methods:

  • Elicitation of numerical utilities for all elementary events to quantify outcome desirability.
  • Mapping categorical outcome probability vectors to a mean utility for a one-dimensional comparative criterion.
  • Development of Bayesian fixed-sample and group sequential tests using Dirichlet-multinomial models.
  • Algorithms for jointly calibrating test cutoffs and sample size to control Type I error and achieve desired power.

Main Results:

  • A novel utility-based framework for comparative clinical trials with categorical outcomes.
  • Demonstration of Bayesian tests, including sequential procedures, with practical guidelines for prior specification and utility elicitation.
  • Successful application to re-design a chronic lymphocytic leukemia trial, incorporating efficacy and toxicity considerations.
  • Availability of free computer software for implementing the proposed methodology.

Conclusions:

  • The proposed utility-based Bayesian testing methodology offers a robust framework for clinical trials with categorical outcomes.
  • This approach allows for a more comprehensive evaluation of treatments by integrating patient-relevant utilities.
  • The methodology and provided software facilitate improved clinical trial design, analysis, and decision-making, particularly for complex outcomes.