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 Experiment Videos

Sample size determination for logistic regression revisited.

Eugene Demidenko1

  • 1Dartmouth Medical School, Hanover, NH 03755, USA. eugened@dartmouth.edu

Statistics in Medicine
|December 7, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Metabolic Response to CDK4/6 Inhibition in ER+ Breast Cancer Creates a Therapeutic Vulnerability in Drug-Tolerant Persister Cells.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology·2026
Same author

OxyTrack: A Novel Needle Sensor for In Situ Oximetry.

Magnetic resonance in medicine·2025
Same author

Laplace's law of succession estimator and M-statistics.

The American statistician·2025
Same author

Oxidative Phosphorylation Is a Metabolic Vulnerability of Endocrine Therapy-Tolerant Persister Cells in ER+ Breast Cancer.

Cancer research·2025
Same author

Endocrine persistence in ER+ breast cancer is accompanied by metabolic vulnerability in oxidative phosphorylation.

bioRxiv : the preprint server for biology·2024
Same author

Differential Toxicity of Arsenic in Daphnia pulex Under Phosphorus and Food Limitation.

Environmental toxicology and chemistry·2024
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
Same journal

A Mixture of Distributed Lag Non-Linear Models to Account for Spatially Heterogeneous Exposure-Lag-Response Associations.

Statistics in medicine·2026
Same journal

Practical Considerations for Gaussian Process Modeling for Causal Inference in Quasi-Experimental Studies With Panel Data.

Statistics in medicine·2026
Same journal

Covariate Adjustment for Wilcoxon Two Sample Statistic and Test.

Statistics in medicine·2026
See all related articles

Calculating statistical power and sample size for logistic regression is debated. This study advocates the Wald test, providing general formulas and an online calculator for optimized case-control studies, simplifying sample size determination.

Area of Science:

  • Biostatistics
  • Epidemiology
  • Statistical Modeling

Background:

  • No consensus exists on computing power and sample size for logistic regression models.
  • Existing methods include likelihood ratio tests, proportion tests, and various approximations.
  • Contradictions arise between historical null-variance formulas and modern maximum likelihood estimation (MLE) software.

Purpose of the Study:

  • To advocate for the use of the Wald test in logistic regression power and sample size calculations.
  • To derive general Wald-based formulas for power and sample size in logistic regression.
  • To apply these formulas to optimize sample size in case-control studies.

Main Methods:

  • Derivation of general Wald-based power and sample size formulas for logistic regression.

Related Experiment Videos

  • Application of formulas to binary exposure and confounder scenarios, yielding a closed-form expression.
  • Optimization of the control-to-case ratio in case-control studies to minimize total sample size for a given power.

    • Main Results:

    • General Wald-based power and sample size formulas for logistic regression were derived.
    • A closed-form expression was obtained for binary exposure and confounder.
    • The optimal number of controls per case is approximately the square root of the alternative odds ratio.

    Conclusions:

    • The Wald test provides a robust and practical approach for logistic regression power and sample size calculations.
    • The derived formulas offer a method to minimize sample size in case-control studies.
    • An online calculator is available for practical application of these sample size and power calculations.