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

Selection designs for pilot studies based on survival

P Y Liu1, S Dahlberg, J Crowley

  • 1Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, Washington 98104.

Biometrics
|June 1, 1993
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

[Efficacy of three hearing intervention strategies in microtia patients with concomitant ossicular malformation].

Zhonghua yi xue za zhi·2026
Same author

Urgent Virtual Care: Impact on GP, Emergency, and Scheduled Care Pathways.

Irish medical journal·2026
Same author

[Ongoing pregnancy rate with intervention and expectant therapy in patients with unexplained recurrent spontaneous abortion: a prospective multicenter cohort study].

Zhonghua fu chan ke za zhi·2025
Same author

Breeding for sustainability: Development of an index to reduce greenhouse gas in dairy cattle.

Animal : an international journal of animal bioscience·2025
Same author

Implementing Faecal Immunochemical Testing in Primary Care.

Irish medical journal·2024
Same author

A Video Analysis of Helmet Area Injuries and Helmet Type Worn in Hurling and Camogie Over 2 Seasons.

Irish medical journal·2024
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 a new Cox regression model design for cancer clinical trials to efficiently select the best treatment regimen before large-scale randomized comparisons. This approach optimizes resource allocation and accelerates the identification of effective cancer therapies.

Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Oncology Research

Background:

  • Cancer clinical trials typically test new regimens in advanced disease patients, then compare promising ones to standard treatments.
  • Comparing multiple promising regimens against a control can be infeasible due to sample size and study duration constraints.

Purpose of the Study:

  • To propose a novel clinical trial design utilizing the Cox regression model for selecting the optimal treatment regimen based on survival data prior to randomized comparison.
  • To provide sample size calculations for achieving a .90 selection probability with Weibull survival distributions.

Main Methods:

  • Application of the Cox regression model to survival data for treatment selection.
  • Development of sample size guidelines for Weibull survival distributions.

Related Experiment Videos

  • Monte Carlo simulations to verify asymptotic approximations and assess robustness.

    • Main Results:

    • Asymptotic approximations for correct selection probabilities are satisfactory.
    • The proposed design is robust to violations of the proportional hazards assumption.
    • The design integrates seamlessly with sequential cancer trial phases, potentially using different patient populations.

    Conclusions:

    • The proposed Cox regression-based design offers an efficient alternative for selecting the best cancer treatment regimen in clinical trials.
    • This method addresses limitations of traditional multi-regimen comparisons by optimizing sample size and study length.
    • The design provides a flexible framework for advancing cancer treatment research.