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

4.5K
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.
4.5K
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

166
Body: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...
166
Sample Size Calculation01:19

Sample Size Calculation

6.2K
Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...
6.2K
Censoring Survival Data01:09

Censoring Survival Data

507
Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
507
Randomized Experiments01:13

Randomized Experiments

8.8K
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...
8.8K
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

6.6K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
6.6K

You might also read

Related Articles

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

Sort by
Same author

CA-125 KELIM as a Potential Complementary Tool for Predicting Veliparib Benefit: An Exploratory Analysis From the VELIA/GOG-3005 Study.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology·2022
Same author

Drug response hysteresis in the concentration-QTc analysis of early clinical trials.

Journal of biopharmaceutical statistics·2021
Same author

Global Analysis of Models for Predicting Human Absorption: QSAR, <i>In Vitro</i>, and Preclinical Models.

Journal of medicinal chemistry·2021
Same author

Use of Early Clinical Trial Data to Support Thorough QT Study Waiver for Upadacitinib and Utility of Food Effect to Demonstrate ECG Assay Sensitivity.

Clinical pharmacology and therapeutics·2017
Same author

Application of Exposure-Response Analyses to Establish the Pharmacodynamic Similarity of a Once-Daily Regimen to an Approved Twice-Daily Dosing Regimen for the Treatment of HCV Infection.

The AAPS journal·2017
Same author

Pharmacokinetic and exposure-response analyses of leuprolide following administration of leuprolide acetate 3-month depot formulations to children with central precocious puberty.

Clinical drug investigation·2014

Related Experiment Video

Updated: Jan 11, 2026

Frequency and Distribution of Crossovers in Caenorhabditis elegans Meiosis by SNP Genotyping using Real-time PCR
06:18

Frequency and Distribution of Crossovers in Caenorhabditis elegans Meiosis by SNP Genotyping using Real-time PCR

Published on: July 11, 2025

777

Blinded sample size re-estimation in a crossover study.

Shaofei Zhao1, Balakrishna Hosmane2, Chen Chen3

  • 1Data and Statistical Sciences, AbbVie, North Chicago, IL, USA.

Journal of Biopharmaceutical Statistics
|November 19, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new blinded method for estimating sample size in drug bioequivalence trials using crossover designs. The approach improves statistical power by accurately estimating within-subject variance during interim analysis.

Keywords:
Bioequivalence studyblinded sample size re-estimationcrossover designwithin-subject variance

More Related Videos

Performing Permanent Distal Middle Cerebral with Common Carotid Artery Occlusion in Aged Rats to Study Cortical Ischemia with Sustained Disability
09:11

Performing Permanent Distal Middle Cerebral with Common Carotid Artery Occlusion in Aged Rats to Study Cortical Ischemia with Sustained Disability

Published on: February 23, 2016

22.9K
The Optical Fractionator Technique to Estimate Cell Numbers in a Rat Model of Electroconvulsive Therapy
07:55

The Optical Fractionator Technique to Estimate Cell Numbers in a Rat Model of Electroconvulsive Therapy

Published on: July 9, 2017

12.0K

Related Experiment Videos

Last Updated: Jan 11, 2026

Frequency and Distribution of Crossovers in Caenorhabditis elegans Meiosis by SNP Genotyping using Real-time PCR
06:18

Frequency and Distribution of Crossovers in Caenorhabditis elegans Meiosis by SNP Genotyping using Real-time PCR

Published on: July 11, 2025

777
Performing Permanent Distal Middle Cerebral with Common Carotid Artery Occlusion in Aged Rats to Study Cortical Ischemia with Sustained Disability
09:11

Performing Permanent Distal Middle Cerebral with Common Carotid Artery Occlusion in Aged Rats to Study Cortical Ischemia with Sustained Disability

Published on: February 23, 2016

22.9K
The Optical Fractionator Technique to Estimate Cell Numbers in a Rat Model of Electroconvulsive Therapy
07:55

The Optical Fractionator Technique to Estimate Cell Numbers in a Rat Model of Electroconvulsive Therapy

Published on: July 9, 2017

12.0K

Area of Science:

  • Pharmacokinetics and Drug Development
  • Biostatistics
  • Clinical Trial Design

Background:

  • Bioequivalence studies are crucial for drug development, ensuring formulation equivalence.
  • Crossover designs are preferred for within-subject comparisons and statistical power.
  • Accurate sample size determination is challenging due to unknown variance in early-stage drug development.

Purpose of the Study:

  • To propose a novel blinded method for estimating within-subject variance in crossover bioequivalence studies.
  • To enable sample size re-estimation during a trial without unblinding data.
  • To enhance the efficiency and reliability of sample size calculations in adaptive clinical trials.

Main Methods:

  • Development of a new blinded method for within-subject variance estimation at interim analysis.
  • Analytical investigation of the proposed method's statistical properties.
  • Introduction of a refined, unbiased variance estimator for improved accuracy.

Main Results:

  • The proposed method demonstrates comparable performance to existing blinded approaches.
  • The novel method offers advantages in scenarios with small treatment differences and large subject variances.
  • Simulations confirm the method's effectiveness in achieving desired statistical power.

Conclusions:

  • The developed blinded variance estimation method is a valuable tool for adaptive sample size re-estimation in crossover bioequivalence trials.
  • This approach addresses limitations of existing methods by maintaining data blinding.
  • The method enhances statistical rigor and efficiency in drug development studies.