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

Decision making during a phase III randomized controlled trial

D A Berry1, M C Wolff, D Sack

  • 1Institute of Statistics and Decision Sciences, Duke University, Durham, North Carolina 27708-0251.

Controlled Clinical Trials
|October 1, 1994
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

Treatment Efficacy Score-continuous residual cancer burden-based metric to compare neoadjuvant chemotherapy efficacy between randomized trial arms in breast cancer trials.

Annals of oncology : official journal of the European Society for Medical Oncology·2022
Same author

Oral delivery of Hyperimmune bovine serum antibodies against CS6-expressing enterotoxigenic <i>Escherichia coli</i> as a prophylactic against diarrhea.

Gut microbes·2020
Same author

Effect of recent seasonal influenza vaccination on serum antibody responses to candidate pandemic influenza A/H5N1 vaccines: A meta-analysis.

Vaccine·2019
Same author

Risk of acute myeloid leukemia and myelodysplastic syndrome among older women receiving anthracycline-based adjuvant chemotherapy for breast cancer on Modern Cooperative Group Trials (Alliance A151511).

Breast cancer research and treatment·2016
Same author

Emerging innovations in clinical trial design.

Clinical pharmacology and therapeutics·2015
Same author

The Signature Program: Bringing the Protocol to the Patient.

Clinical pharmacology and therapeutics·2015
Same journal

On the generation and ownership of alpha in medical studies.

Controlled clinical trials·2004
Same journal

An analysis of the effect of funding source in randomized clinical trials of second generation antipsychotics for the treatment of schizophrenia.

Controlled clinical trials·2004
Same journal

Symptom recording in a randomised clinical trial: paper diaries vs. electronic or telephone data capture.

Controlled clinical trials·2004
Same journal

Statistical comparison of random allocation methods in cancer clinical trials.

Controlled clinical trials·2004
Same journal

Analyzing bronchodilation with emphasis on disease type, age and sex.

Controlled clinical trials·2004
Same journal

Geographic variability in patient characteristics, treatment and outcome in an International Trial of Magnesium in acute myocardial infarction.

Controlled clinical trials·2004
See all related articles

This study introduces a Bayesian dynamic programming approach for optimizing clinical trial designs. It demonstrates how to dynamically adjust trial continuation based on accumulating evidence and external factors, using a Haemophilus influenzae type b vaccine trial as an example.

Area of Science:

  • Clinical trial design
  • Biostatistics
  • Public health

Background:

  • Clinical trials require adaptive objectives and continuous assessment of accumulating data.
  • External circumstances and evolving evidence necessitate potential modifications to trial designs, including early stopping.

Purpose of the Study:

  • To develop and apply a Bayesian dynamic programming framework for sequential clinical trial decision-making.
  • To optimize trial objectives, such as minimizing disease incidence, by dynamically adjusting trial continuation.
  • To illustrate the approach using a vaccine trial for Haemophilus influenzae type b prevention in a Native American population.

Main Methods:

  • Utilized a Bayesian approach and dynamic programming to model the sequential decision problem of trial continuation.

Related Experiment Videos

  • Assessed prior probability distributions for vaccine efficacy and the probability of regulatory approval.
  • Incorporated predictive probabilities of future results to weigh the impact of continuing the trial.

    • Main Results:

    • Developed an optimal stopping policy sensitive to prior probabilities, regulatory approval likelihood, and the specified time horizon.
    • Demonstrated the application of the framework to a specific vaccine trial scenario.

    Conclusions:

    • The Bayesian dynamic programming approach provides a robust method for adaptive clinical trial design and management.
    • Dynamic adjustment of trial continuation based on data and external factors can optimize trial outcomes and resource allocation.