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

Optimal conditional error functions for the control of conditional power.

Werner Brannath1, Peter Bauer

  • 1Department of Medical Statistics, Medical University of Vienna, Schwarzspaniesrstr. 17, Vienna, Austria. werner.brannath@meduniwien.ac.at

Biometrics
|September 2, 2004
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

The GBA1 p.E427K (p.E388K) Variant Is a Risk Factor for Synucleinopathies: A Meta-Analysis.

Movement disorders : official journal of the Movement Disorder Society·2026
Same author

Glucosylsphingosine (Lyso-Gb1) Dynamics in Untreated States in Gaucher Disease.

International journal of molecular sciences·2026
Same author

Moyamoya syndrome in a patient with pathogenic heterozygous variant in the <i>SETD5</i> gene: A case report.

Brain circulation·2026
Same author

Genetic Testing by Age at Onset in Parkinson Disease.

JAMA neurology·2026
Same author

Variants in the proteasome regulator PSMF1 cause a phenotypic spectrum from parkinsonism to perinatal lethality.

Nature communications·2026
Same author

DRD1-driven infantile dystonia: towards a mechanism-informed framework for GPCR receptoropathies.

Brain : a journal of neurology·2026
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
Same journal

Discussion on "INTACT: a method for integration of longitudinal physical activity data from multiple sources" by Jingru Zhang, Erjia Cui, Hongzhe Li, and Haochang Shou.

Biometrics·2026
See all related articles

This study introduces optimal two-stage clinical trial designs that maintain high conditional power and control overall type I error rates. These designs minimize expected sample size, offering a powerful alternative to traditional group sequential methods.

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Power

Background:

  • Clinical trials require sufficient statistical power to detect treatment effects.
  • Interim analyses in trials often necessitate high conditional power for continuation decisions.
  • Classical group sequential designs may offer insufficient conditional power.

Purpose of the Study:

  • To construct and optimize two-stage clinical trial designs with controlled overall and conditional power.
  • To minimize the expected sample size in clinical trials while maintaining desired power levels.
  • To provide an alternative to group sequential designs that often yield low conditional power.

Main Methods:

  • Development of two-stage designs incorporating interim sample size recalculation.

Related Experiment Videos

  • Optimization criteria focused on minimizing expected sample size under various alternative hypotheses.
  • Consideration of constraints like minimal second-stage sample size and interim hypothesis testing.

    • Main Results:

    • Optimal designs are derived for both point and mixed alternative hypotheses.
    • The proposed designs effectively control overall type I error rate and maintain prespecified overall and conditional power.
    • The method accounts for interim hypothesis testing and sample size constraints.

    Conclusions:

    • Two-stage designs with controlled conditional power offer an efficient alternative to group sequential designs.
    • These optimized designs minimize expected sample size, improving resource allocation in clinical trials.
    • The presented methodology provides a flexible framework for robust clinical trial planning.