ArticleBiometrics2012
Bayesian enrichment strategies for randomized discontinuation trials.
Article in Biometrics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Application of Bayesian methods to accelerate rare disease drug development: scopes and hurdles.Orphanet journal of rare diseases · 2022Review
- Designing and conducting adaptive trials to evaluate interventions in health services and implementation research: practical considerations.BMJ medicine · 2022Article
- Adaptive Enrichment Designs in Clinical Trials.Annual review of statistics and its application · 2021Article
- Decision-theoretic designs for small trials and pilot studies: A review.Statistical methods in medical research · 2016Review
- Combining progression-free survival and overall survival as a novel composite endpoint for glioblastoma trials.Neuro-oncology · 2015Article
- Estimation of Progression-Free Survival for All Treated Patients in the Randomized Discontinuation Trial Design.The American statisticianArticle
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Authors and funding
3 authors.
Funding
Abstract
We propose optimal choice of the design parameters for random discontinuation designs (RDD) using a Bayesian decision-theoretic approach. We consider applications of RDDs to oncology phase II studies evaluating activity of cytostatic agents. The design consists of two stages. The preliminary open-label stage treats all patients with the new agent and identifies a possibly sensitive subpopulation. The subsequent second stage randomizes, treats, follows, and compares outcomes among patients in the identified subgroup, with randomization to either the new or a control treatment. Several tuning parameters characterize the design: the number of patients in the trial, the duration of the preliminary stage, and the duration of follow-up after randomization. We define a probability model for tumor growth, specify a suitable utility function, and develop a computational procedure for selecting the optimal tuning parameters.
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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.