Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Comparing the Survival Analysis of Two or More Groups

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...

Crossover Experiments

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 Designs

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...

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

Bioequivalence Experimental Study Designs: Completely Randomized and Randomized Block Designs

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...

Group Design

Group Design

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 the two are due to...

Study Designs in Epidemiology

Study Designs in Epidemiology

Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and case-control studies.

You might also read

Related Articles

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

Sort by

Same author

Lactate dynamics in Parkinson's disease: striatal circuit vulnerability, biomarker potential, and therapeutic windows.

Molecular medicine (Cambridge, Mass.)·2026

Same author

Aerobic Exercise Improves Motor Performance by Reducing Total ERK Expression and Hyperexcitability of Striatal D2-MSNs in MPTP-Induced Parkinsonian Mice.

Molecular neurobiology·2026

Same author

Multiomics Analysis Reveals Coordinated Molecular and Physiological Changes during Light-Induced Primordium Initiation in <i>Lentinula edodes</i>.

Journal of agricultural and food chemistry·2026

Same author

TLCD1 correlates with malignant progression of hepatocellular carcinoma and activation of the ERK signaling cascade.

Scientific reports·2026

Same author

Comparative Analysis of the In Vitro and In Vivo Antioxidant and Anti-Inflammatory Capacities of Lycopene Extracts from Different Sources.

Foods (Basel, Switzerland)·2026

Same author

New Rare-Earth-Based Hybrid Glass for Fine-Tuning of the Magneto-Optical Faraday Rotation.

Langmuir : the ACS journal of surfaces and colloids·2026

Search research articles

Related Experiment Video

Updated: Jun 14, 2026

High Throughput Sequential ELISA for Validation of Biomarkers of Acute Graft-Versus-Host Disease

High Throughput Sequential ELISA for Validation of Biomarkers of Acute Graft-Versus-Host Disease

Published on: October 31, 2012

Testing a primary and a secondary endpoint in a group sequential design.

Ajit C Tamhane¹, Cyrus R Mehta, Lingyun Liu

¹Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illnois 60208, USA. atamhane@northwestern.edu

|March 27, 2010

Summary

This study develops methods for controlling statistical errors in two-stage clinical trials with primary and secondary endpoints. It identifies optimal boundaries for group sequential procedures to maintain familywise error rate control.

Related Experiment Videos

Last Updated: Jun 14, 2026

High Throughput Sequential ELISA for Validation of Biomarkers of Acute Graft-Versus-Host Disease

High Throughput Sequential ELISA for Validation of Biomarkers of Acute Graft-Versus-Host Disease

Published on: October 31, 2012

Area of Science:

Clinical Trial Design
Biostatistics
Statistical Inference

Background:

Clinical trials often involve multiple endpoints, necessitating careful statistical planning.
Group sequential methods allow early trial stopping for efficacy or futility, impacting error rates.
Controlling the familywise error rate (FWER) is crucial when testing multiple endpoints sequentially.

Purpose of the Study:

To determine optimal group sequential boundaries for primary and secondary endpoints in two-stage clinical trials.
To control the familywise error rate (FWER) at a nominal level alpha.
To investigate the impact of correlation between endpoints on FWER control.

Main Methods:

Utilized a two-stage group sequential procedure for clinical trials.
Analyzed and numerically computed familywise error rates (FWER) for various boundary combinations.
Investigated the influence of endpoint correlation (ρ) on statistical power and error rates.

Main Results:

FWER is maximized when the correlation coefficient (ρ) between endpoints is 1.
Computed critical constants for O'Brien-Fleming and Pocock boundary combinations across different ρ values.
O'Brien-Fleming (primary) and Pocock (secondary) boundaries generally offer the best power performance.

Conclusions:

The study provides methods for selecting appropriate group sequential boundaries to control FWER in two-stage trials.
An ad hoc boundary for the secondary endpoint is proposed as an alternative to the Pocock boundary with minimal power loss.
Recommended O'Brien-Fleming for primary and Pocock for secondary endpoints for optimal power, with an alternative ad hoc boundary.