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

Crossover Experiments01:16

Crossover Experiments

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.
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

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

Comparing the Survival Analysis of Two or More Groups

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 Cox...
McNemar's Test01:23

McNemar's Test

McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...

You might also read

Related Articles

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

Sort by
Same author

Response-adaptive randomization with imperfect intermediate endpoints.

Clinical trials (London, England)·2026
Same author

Treatment of indolent systemic mastocytosis with sarilumab is not supported in a randomized trial.

The journal of allergy and clinical immunology. Global·2025
Same author

Futility Monitoring in Clinical Trials.

Statistics in medicine·2025
Same author

Does Remdesivir Lower COVID-19 Mortality? A Subgroup Analysis of Hospitalized Adults Receiving Supplemental Oxygen.

Statistics in medicine·2024
Same author

Changing interim monitoring in response to internal clinical trial data.

Biometrics·2024
Same author

Genetically defined individual reference ranges for tryptase limit unnecessary procedures and unmask myeloid neoplasms.

Blood advances·2022
Same journal

A statistical evaluation of decision-making methods and the efficiency of Bayesian multi-arm multi-stage trials.

Clinical trials (London, England)·2026
Same journal

Accounting for non-adherence: A re-analysis of the Liraglutide Effect and Action in Diabetes: Evaluation of Cardiovascular Outcome Results trial.

Clinical trials (London, England)·2026
Same journal

Phase I design for partially ordered groups with late-onset toxicity.

Clinical trials (London, England)·2026
Same journal

Trial informed consent forms, the Declaration of Helsinki and the SPIRIT 2025 statement.

Clinical trials (London, England)·2026
Same journal

17th Annual University of Pennsylvania Conference on statistical issues in clinical trials - Covariate adjustment in randomized clinical trials: New methods and applications (Morning panel discussion).

Clinical trials (London, England)·2026
Same journal

17th Annual University of Pennsylvania Conference on statistical issues in clinical trials - Covariate adjustment in randomized clinical trials: New methods and applications (Afternoon panel discussion).

Clinical trials (London, England)·2026
See all related articles

Related Experiment Video

Updated: Jun 2, 2026

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
07:05

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents

Published on: September 10, 2018

6.5K

Event-driven planning of two-armed trials with a binary endpoint.

Erica H Brittain1, Raphaël N Morsomme1, Michael A Proschan1

  • 1Office of Biostatistics Research, NIAID, NIH, Bethesda, MD, USA.

Clinical Trials (London, England)
|February 16, 2026
PubMed
Summary
This summary is machine-generated.

For clinical trials with low event probabilities, the number of events is more stable than sample size. This stability can enhance adaptive trial designs and enable simple event-driven strategies for binary endpoints.

Keywords:
Clinical trialbinary dataevent-driven trialodds ratiorisk differencerisk ratiosample size calculation

More Related Videos

Three Laboratory Procedures for Assessing Different Manifestations of Impulsivity in Rats
09:12

Three Laboratory Procedures for Assessing Different Manifestations of Impulsivity in Rats

Published on: March 17, 2019

10.1K
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

3.4K

Related Experiment Videos

Last Updated: Jun 2, 2026

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents
07:05

Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents

Published on: September 10, 2018

6.5K
Three Laboratory Procedures for Assessing Different Manifestations of Impulsivity in Rats
09:12

Three Laboratory Procedures for Assessing Different Manifestations of Impulsivity in Rats

Published on: March 17, 2019

10.1K
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

3.4K

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Power Analysis

Background:

  • Sample size calculation for clinical trials with binary endpoints often relies on event probability, which can be uncertain.
  • Survival trials are powered by the number of events, which is less sensitive to unknown parameters than sample size.
  • This study investigates the relative stability of event counts versus sample size in two-armed randomized trials with binary outcomes.

Purpose of the Study:

  • To quantify the relative stability of the number of events compared to sample size for binary endpoint trials.
  • To explore the enhancement of adaptive trial designs using this relative stability.
  • To evaluate the potential benefits of a simple event-driven strategy in such settings.

Main Methods:

  • Utilized sample size formulas to assess the stability of event numbers versus sample size for relative risk, odds ratio, and risk difference.
  • Conducted simulations to evaluate an event-driven design under conditions of relative stability.
  • Assessed type I error rate and power using various analysis methods and trial halting strategies.

Main Results:

  • The number of events is at least three times more stable than sample size for relative risk (event probability < 1/3) and odds ratio (event probability < 0.20).
  • This stability is independent of error rates and treatment effect magnitude.
  • Simulations of event-driven designs showed that while asymptotic methods may inflate type I error, other approaches demonstrate favorable operating characteristics.

Conclusions:

  • In trials with moderately low event probabilities, focusing on the number of events can aid trial planning and futility assessment.
  • This approach may facilitate the development of simple, feasible, and appealing event-driven designs for binary endpoints.
  • Adopting an event-centric perspective can streamline clinical trial planning and execution.