Evidence map›Paper›PMID 38847351›Full record

ArticleJournal of biopharmaceutical statistics2025

Implementation of statistical features of a Bayesian two-armed responsive adaptive randomization trial with post hoc analysis of time trend drift.

Elena Shergina, Kimber P Richter, Chuanwu Zhang, Laura Mussulman, Niaman Nazir, Byron J Gajewski

Abstract read
In one paragraph

Article in Journal of biopharmaceutical statistics, 2025. 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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0cells of the map it votes in
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

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

6 authors.

Elena SherginaDepartment of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA.
Kimber P RichterDepartment of Population Health, University of Kansas Medical Center, Kansas City, Kansas, USA.
Chuanwu ZhangDepartment of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA.
Laura MussulmanClinical and Translational Science Unit Fairway, University of Kansas Medical Center, Kansas City, Kansas, USA.
Niaman NazirDepartment of Population Health, University of Kansas Medical Center, Kansas City, Kansas, USA.
Byron J GajewskiDepartment of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA.

Funding

Transgenic & Gene-Targeting Shared ResourceP30CA168524 · NCI · UNIVERSITY OF KANSAS MEDICAL CENTER · PI ROY A. JENSEN · 2012 to 2026
$40.1M
Changing the Default for Tobacco TreatmentR01HL131512 · NHLBI · UNIVERSITY OF KANSAS MEDICAL CENTER · PI RICHTER, KIMBER P · 2016 to 2020
$3.7M
NCI NIH HHS P30 CA168524NHLBI NIH HHS R01 HL131512
6 · The paper itself

Abstract

Bayesian adaptive designs with response adaptive randomization (RAR) have the potential to benefit more participants in a clinical trial. While there are many papers that describe RAR designs and results, there is a scarcity of works reporting the details of RAR implementation from a statistical point exclusively. In this paper, we introduce the statistical methodology and implementation of the trial Changing the Default (CTD). CTD is a single-center prospective RAR comparative effectiveness trial to compare opt-in to opt-out tobacco treatment approaches for hospitalized patients. The design assumed an uninformative prior, conservative initial allocation ratio, and a higher threshold for stopping for success to protect results from statistical bias. A particular emerging concern of RAR designs is the possibility that time trends will occur during the implementation of a trial. If there is a time trend and the analytic plan does not prespecify an appropriate model, this could lead to a biased trial. Adjustment for time trend was not pre-specified in CTD, but post hoc time-adjusted analysis showed no presence of influential drift. This trial was an example of a successful two-armed confirmatory trial with a Bayesian adaptive design using response adaptive randomization.

Indexed as

Adaptive Clinical Trials as TopicRandomized Controlled Trials as TopicResearch DesignBayes TheoremData Interpretation, StatisticalHumansModels, StatisticalProspective StudiesTime FactorsBayesian adaptive designscomparative effectiveness trialDrift analysis

Identifiers

PMID38847351
PMCPMC11624317

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Registered trials

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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.