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

Sample Size Calculation01:19

Sample Size Calculation

3.3K
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...
3.3K
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

2.4K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.4K
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

3.3K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
3.3K
Testing a Claim about Mean: Unknown Population SD01:21

Testing a Claim about Mean: Unknown Population SD

3.4K
A complete procedure of testing a hypothesis about a population mean when the population standard deviation is unknown is explained here.
Estimating a population mean requires the samples to be approximately normally distributed. The data should be collected from the randomly selected samples having no sampling bias. There is no specific requirement for sample size. But if the sample size is less than 30, and we don't know the population standard deviation, a different approach is used;...
3.4K
Wald-Wolfowitz Runs Test II01:17

Wald-Wolfowitz Runs Test II

227
The Wald-Wolfowitz runs test, commonly referred to as the runs test, is a nonparametric test used to assess the randomness of ordered data. The test evaluates the number of runs, which are consecutive sequences of similar elements within the data. If the number of runs is significantly higher or lower than expected, the data is considered non-random, indicating a detectable pattern or structure.
For binary data, runs are identified using symbols such as + and −, or equivalently, 1s and...
227
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

129
The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in...
129

You might also read

Related Articles

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

Sort by
Same author

Weighted generalized score test for comparing predictive values in the presence of verification bias.

Statistics in medicine·2022
Same author

Joint comparison of the predictive values of multiple binary diagnostic tests: an extension of McNemar's test.

Journal of biopharmaceutical statistics·2022
Same author

Weighted McNemar's test for the comparison of two screening tests in the presence of verification bias.

Statistics in medicine·2022
Same author

Nonparametric inference of the area under ROC curve under two-phase cluster sampling.

Journal of biopharmaceutical statistics·2021
Same author

Assess predictive values of a binary diagnostic test under a nested case-control design.

Journal of biopharmaceutical statistics·2021
Same author

Simple confidence interval and region formulas for comparing diagnostic likelihood ratios under a paired design.

Biometrical journal. Biometrische Zeitschrift·2021
Same journal

Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.

Statistics in medicine·2026
Same journal

Adaptive Sequential Multiple Hypotheses Testing for Concomitant Vaccine Safety Surveillance.

Statistics in medicine·2026
Same journal

Novel Distance Regression for Repeated Outcomes With Missing Data: Applications to Longitudinal and Crossover Studies of Microbiome Beta-Diversity.

Statistics in medicine·2026
Same journal

Optimal Weighted Tests for Replication Studies and the 'Two-Trials Rule' With Multiple Hypotheses.

Statistics in medicine·2026
Same journal

Identifiable Copula-Double-Cox Models: A Fully Parametric Framework for Dependent Right-Censored Survival Data.

Statistics in medicine·2026
Same journal

Moving From Individualized Risk-Based Prevention to Benefit-Based Prevention: Estimating Individualized Life-Years Gained From Prevention Services as a Basis for Eligibility.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Jun 26, 2025

Pooled CRISPR-Based Genetic Screens in Mammalian Cells
00:09

Pooled CRISPR-Based Genetic Screens in Mammalian Cells

Published on: September 4, 2019

21.9K

Sample size calculation for comparing two screening tests when the gold standard is missing at random.

Yougui Wu1

  • 1Department of Epidemiology and Biostatistics, College of Public Health, University of South Florida, Tampa, Florida, USA.

Statistics in Medicine
|May 15, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces simpler sample size formulas for comparing diagnostic test accuracy, addressing verification bias in paired designs. The new methods are easier to use and yield similar results to complex existing ones.

Keywords:
McNemar's testWald testssample size calculationverification biasweighted McNemar's test

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K
MEDUSA for Identifying Death Regulatory Genes in Chemo-genetic Profiling Data
07:17

MEDUSA for Identifying Death Regulatory Genes in Chemo-genetic Profiling Data

Published on: February 7, 2025

426

Related Experiment Videos

Last Updated: Jun 26, 2025

Pooled CRISPR-Based Genetic Screens in Mammalian Cells
00:09

Pooled CRISPR-Based Genetic Screens in Mammalian Cells

Published on: September 4, 2019

21.9K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K
MEDUSA for Identifying Death Regulatory Genes in Chemo-genetic Profiling Data
07:17

MEDUSA for Identifying Death Regulatory Genes in Chemo-genetic Profiling Data

Published on: February 7, 2025

426

Area of Science:

  • Biostatistics
  • Medical Diagnostics
  • Clinical Trials

Background:

  • Comparing diagnostic test accuracy is crucial, especially with verification bias.
  • Existing sample size formulas for paired designs are complex and difficult to implement.
  • Paired designs are common in studies evaluating diagnostic test performance.

Purpose of the Study:

  • To propose simplified and intuitive sample size formulas for comparing two sensitivities or specificities.
  • To provide practical alternatives to existing complicated formulas for paired designs with verification bias.
  • To compare the efficiency of different statistical tests in sample size calculations.

Main Methods:

  • Development of alternative sample size formulas for two Wald tests and one weighted McNemar's test.
  • Comparison of the proposed formulas with existing complex methods.
  • Analysis of sample size requirements across different tests, considering both discordant and accordant pairs.

Main Results:

  • The proposed sample size formulas are simpler and more intuitive than existing ones.
  • All three considered tests (two Wald, one weighted McNemar's) result in similar sample sizes.
  • The weighted McNemar's test, using only discordant pairs, shows comparable sample size needs to Wald tests using accordant and discordant pairs.

Conclusions:

  • Simplified sample size calculations are now available for paired diagnostic accuracy studies with verification bias.
  • The choice of test (Wald vs. weighted McNemar's) does not significantly impact sample size requirements.
  • These findings facilitate more accessible and efficient study design in diagnostic test evaluation.