Evidence map›Paper›PMID 29200977›Full record

ArticleApplied stochastic models in business and industry

Clinical Trial Design as a Decision Problem.

Peter Müller, Yanxun Xu, Peter F Thall

Abstract read
In one paragraph

Article in Applied stochastic models in business and industry. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Bayesian Methods in Regulatory Science.Statistics in biopharmaceutical research · 2020
    Article
  4. Review
  5. Applying Probabilistic Decision Models to Clinical Trial Design.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2018
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Peter MüllerDept. of Mathematics, University of Texas at Austin.
Yanxun XuDept. of Applied Mathematics and Statistics, Johns Hopkins University.
Peter F ThallDept. of Biostatistics, University of Texas, M.D. Anderson Cancer Center.

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Scott Kopetz · 1985 to 2026
$290.8M
Bayesian models for cancer prognosis by integrating diverse types of dataR01CA132897 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI JI, YUAN, MUELLER, PETER · 2008 to 2019
$2.1M
NCI NIH HHS P30 CA016672NCI NIH HHS R01 CA132897
6 · The paper itself

Abstract

The intent of this discussion is to highlight opportunities and limitations of utility-based and decision theoretic arguments in clinical trial design. The discussion is based on a specific case study, but the arguments and principles remain valid in general. The example concerns the design of a randomized clinical trial to compare a gel sealant versus standard care for resolving air leaks after pulmonary resection. The design follows a principled approach to optimal decision making, including a probability model for the unknown distributions of time to resolution of air leaks under the two treatment arms, and an explicit utility function that quantifies clinical preferences for alternative outcomes. As is typical for any real application, the final implementation includes some compromises from the initial principled setup. In particular, we use the formal decision problem only for the final decision, but use reasonable

Indexed as

Bayesian decision problem Bayes rulenonparametric Bayesoptimal designsequential stopping

Identifiers

PMID29200977
PMCPMC5705102

What OpenQuestion holds

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Registered trials

None linked

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.