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

251
Body: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...
251
Stratified Sampling Method01:16

Stratified Sampling Method

15.5K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
15.5K
Random Sampling Method01:09

Random Sampling Method

14.9K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
14.9K
Sample Size Calculation01:19

Sample Size Calculation

6.7K
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...
6.7K
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

6.8K
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:
6.8K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

4.2K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
4.2K

You might also read

Related Articles

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

Sort by
Same author

Longitudinal magnetic resonance spectroscopy study of metabolite changes over 2 years in relapsing and primary progressive multiple sclerosis treated with ocrelizumab.

Multiple sclerosis (Houndmills, Basingstoke, England)·2026
Same author

Body mass index and chemotherapy completion among patients with newly diagnosed ovarian cancer.

JNCI cancer spectrum·2025
Same author

Macular Atrophy-Related Observations in Eyes Treated with the Port Delivery System with Ranibizumab in the Archway Trial.

Ophthalmology. Retina·2025
Same author

Does Comorbid Food Allergy Affect Response to Omalizumab in Patients with Asthma?

Journal of asthma and allergy·2024
Same author

Emerging Cerebrospinal Fluid Biomarkers of Disease Activity and Progression in Multiple Sclerosis.

JAMA neurology·2024
Same author

Association between diet quality and ovarian cancer risk and survival.

Journal of the National Cancer Institute·2024

Related Experiment Video

Updated: Feb 11, 2026

Rendering SiO2/Si Surfaces Omniphobic by Carving Gas-Entrapping Microtextures Comprising Reentrant and Doubly Reentrant Cavities or Pillars
08:02

Rendering SiO2/Si Surfaces Omniphobic by Carving Gas-Entrapping Microtextures Comprising Reentrant and Doubly Reentrant Cavities or Pillars

Published on: February 11, 2020

9.4K

Sample size and power for a stratified doubly randomized preference design.

Briana Cameron1, Denise A Esserman1

  • 1Department of Biostatistics, Yale School of Public Health, USA.

Statistical Methods in Medical Research
|November 23, 2016
PubMed
Summary

This study introduces a stratified two-stage randomized trial design to better understand patient preferences and treatment effects. The new method improves upon existing designs by accounting for varying preference rates and effect sizes in different patient groups.

Keywords:
Sample sizepatient preferencepowerstratifiedtwo-stage trial

More Related Videos

Design and Analysis of Temperature Preference Behavior and its Circadian Rhythm in Drosophila
09:09

Design and Analysis of Temperature Preference Behavior and its Circadian Rhythm in Drosophila

Published on: January 13, 2014

8.5K
Differential Imaging of Biological Structures with Doubly-resonant Coherent Anti-stokes Raman Scattering CARS
12:56

Differential Imaging of Biological Structures with Doubly-resonant Coherent Anti-stokes Raman Scattering CARS

Published on: October 17, 2010

14.1K

Related Experiment Videos

Last Updated: Feb 11, 2026

Rendering SiO2/Si Surfaces Omniphobic by Carving Gas-Entrapping Microtextures Comprising Reentrant and Doubly Reentrant Cavities or Pillars
08:02

Rendering SiO2/Si Surfaces Omniphobic by Carving Gas-Entrapping Microtextures Comprising Reentrant and Doubly Reentrant Cavities or Pillars

Published on: February 11, 2020

9.4K
Design and Analysis of Temperature Preference Behavior and its Circadian Rhythm in Drosophila
09:09

Design and Analysis of Temperature Preference Behavior and its Circadian Rhythm in Drosophila

Published on: January 13, 2014

8.5K
Differential Imaging of Biological Structures with Doubly-resonant Coherent Anti-stokes Raman Scattering CARS
12:56

Differential Imaging of Biological Structures with Doubly-resonant Coherent Anti-stokes Raman Scattering CARS

Published on: October 17, 2010

14.1K

Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Health Services Research

Background:

  • Two-stage randomized preference trials help separate patient preference effects from treatment effects.
  • Existing designs assume uniform preference rates and effect sizes, limiting their applicability.
  • This limitation can lead to inaccurate estimations of treatment selection and preference effects.

Purpose of the Study:

  • To propose a stratified two-stage randomized trial design to overcome limitations of current methods.
  • To enable accurate estimation of treatment, preference, and selection effects in diverse populations.
  • To develop a sample size formula for the stratified design.

Main Methods:

  • Derivation of stratified test statistics for treatment, preference, and selection effects.
  • Development of a sample size formula tailored for the stratified design.
  • Validation through simulation studies and application to a Hepatitis C treatment study.

Main Results:

  • The stratified design effectively addresses limitations of unstratified approaches.
  • Stratification by patient characteristics (e.g., alcohol/drug use) improves efficiency.
  • The design more accurately captures the distribution of patient preferences and effects.

Conclusions:

  • The proposed stratified two-stage randomized preference trial design offers a more robust and efficient approach.
  • This methodology enhances the ability to disentangle patient preference from treatment response.
  • The design is particularly valuable when patient preferences and effect sizes vary across subgroups.