Evidence map›Paper›PMID 39325832›Full record

ArticlePloS one2024

Applications of the prediction of satisfaction design for monitoring single-arm phase II trials.

Zohra Djeridi, Ahlem Ghouar, Hamid Boulares, Mohamed Bouye

Abstract readComment
In one paragraph

Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Zohra DjeridiDepartment of Mathematics, University of Jijel, Jijel, Algeria.ORCID 0000-0003-0572-2398
Ahlem GhouarPreparatory Department, Higher School of Management Sciences-Annaba, Annaba, Algeria.
Hamid BoularesACED Laboratory University of 8 May 1945 Guelma, Guelma, Algeria.
Mohamed BouyeDepartment of Mathematics, College of Science, King Khalid University, Abha, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Prediction of satisfaction design, with binary endpoints, is an innovative strategy for phase II trials. We explain this hybrid frequentist-Bayesian strategy with an adept statistical plan and thorough findings, incorporating a description of study design features such as the sample size and the beta prior distribution, to simplify the Bayesian design. We also provide a set of tables and figures ranging from the stopping boundary for futility to the prediction of satisfaction, performance (type I error, power, and the probability of early termination PET), and sensitivity analysis for prediction of satisfaction. The statistical plan includes the operating characteristics through simulation study. Several trial examples from phase II lung cancer studies demonstrate the approach's practical use. The prediction of satisfaction design presents a flexible method in clinical study. This statistical study adds value to the application by broadening its scope.

Indexed as

Bayes TheoremClinical Trials, Phase II as TopicResearch DesignHumansLung NeoplasmsSample Size

Identifiers

PMID39325832
PMCPMC11426476

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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.