ArticleStatistics in medicine2016
Utility-based designs for randomized comparative trials with categorical outcomes.
Article in Statistics in medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed, 26 citations in OpenAlex.
- Bayesian Safety and Futility Monitoring in Phase II Trials Using One Utility-Based Rule.Statistics in medicine · 2024Article
- Bayesian multilevel multivariate logistic regression for superiority decision-making under observable treatment heterogeneity.BMC medical research methodology · 2023Article
- Personalized Risk-Based Screening Design for Comparative Two-Arm Group Sequential Clinical Trials.Journal of personalized medicine · 2022Article
- Estimation and Optimization of Composite Outcomes.Journal of machine learning research : JMLR · 2021Article
- Decision-making with multiple correlated binary outcomes in clinical trials.Statistical methods in medical research · 2020Article
- Precision medicine: Statistical methods for estimating adaptive treatment strategies.Bone marrow transplantation · 2020Article
- Optimizing natural killer cell doses for heterogeneous cancer patients on the basis of multiple event times.Journal of the Royal Statistical Society. Series C, Applied statistics · 2019Article
- Bayesian Utility-Based Designs for Subgroup-Specific Treatment Comparison and Early-Phase Dose Optimization in Oncology Clinical Trials.JCO precision oncology · 2019Review
- A utility-based design for randomized comparative trials with ordinal outcomes and prognostic subgroups.Biometrics · 2018Article
- Bayesian Multivariate Logistic Regression for Superiority and Inferiority Decision-Making under Observable Treatment Heterogeneity.Multivariate behavioral researchArticle
- Clinical Trial Design as a Decision Problem.Applied stochastic models in business and industryArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
A general utility-based testing methodology for design and conduct of randomized comparative clinical trials with categorical outcomes is presented. Numerical utilities of all elementary events are elicited to quantify their desirabilities. These numerical values are used to map the categorical outcome probability vector of each treatment to a mean utility, which is used as a one-dimensional criterion for constructing comparative tests. Bayesian tests are presented, including fixed sample and group sequential procedures, assuming Dirichlet-multinomial models for the priors and likelihoods. Guidelines are provided for establishing priors, eliciting utilities, and specifying hypotheses. Efficient posterior computation is discussed, and algorithms are provided for jointly calibrating test cutoffs and sample size to control overall type I error and achieve specified power. Asymptotic approximations for the power curve are used to initialize the algorithms. The methodology is applied to re-design a completed trial that compared two chemotherapy regimens for chronic lymphocytic leukemia, in which an ordinal efficacy outcome was dichotomized, and toxicity was ignored to construct the trial's design. The Bayesian tests also are illustrated by several types of categorical outcomes arising in common clinical settings. Freely available computer software for implementation is provided. Copyright © 2016 John Wiley & Sons, Ltd.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.