Evidence map›Paper›PMID 29359450›Full record

ArticleBiometrics2018

A multi-source adaptive platform design for testing sequential combinatorial therapeutic strategies.

Alexander M Kaizer, Brian P Hobbs, Joseph S Koopmeiners

Open access · greenAbstract read
In one paragraph

Article in Biometrics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
1.9field-weighted citation impact, top 14% of its field
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

24 citing papers in PubMed, 51 citations in OpenAlex.

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  9. A practical guide to adopting Bayesian analyses in clinical research.Journal of clinical and translational science · 2024
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  18. Calibrated dynamic borrowing using capping priors.Journal of biopharmaceutical statistics · 2021
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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 at 2 institutions in 1 country.

Alexander M KaizerDivision of Biostatistics, University of Minnesota, Minneapolis, Minnesota, U.S.A.
Brian P HobbsThe University of Texas MD Anderson Cancer Center, Houston, Texas, U.S.A.
Joseph S KoopmeinersDivision of Biostatistics, University of Minnesota, Minneapolis, Minnesota, U.S.A.ORCID 0000-0001-6147-3168
University of Minnesota · USThe University of Texas MD Anderson Cancer Center · US

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Scott Kopetz · 1985 to 2026
$290.8M
Women's CancerP30CA077598 · NCI · UNIVERSITY OF MINNESOTA TWIN CITIES · PI Douglas Yee · 1998 to 2026
$100.4M
NCI NIH HHS P30 CA016672NCI NIH HHS P30 CA077598
6 · The paper itself

Abstract

Traditional paradigms for clinical translation are challenged in settings where multiple contemporaneous therapeutic strategies have been identified as potentially beneficial. Platform trials have emerged as an approach for sequentially comparing multiple trials using a single protocol. The Ebola virus disease outbreak in West Africa represents one recent example which utilized a platform design. Specifically, the PREVAIL II master protocol sequentially tested new combinations of therapies against the concurrent, optimal standard of care (oSOC) strategy. Once a treatment demonstrated sufficient evidence of benefit, the treatment was added to the oSOC for all future comparisons (denoted as segments throughout the manuscript). In the interest of avoiding bias stemming from population drift, PREVAIL II considered only within-segment comparisons between the oSOC and novel treatments and failed to leverage data from oSOC patients in prior segments. This article describes adaptive design methodology aimed at boosting statistical power through Bayesian modeling and adaptive randomization. Specifically, the design uses multi-source exchangeability models to combine data from multiple segments and adaptive randomization to achieve information balance within a segment. When compared to the PREVAIL II design, we demonstrate that our proposed adaptive platform design improves power by as much as 51% with limited type-I error inflation. Further, the adaptive platform effectuates more balance with respect to the distribution of acquired information among study arms, with more patients randomized to experimental regimens.

Indexed as

Adaptive Clinical Trials as TopicAfrica, WesternBayes TheoremClinical ProtocolsComputer SimulationDisease OutbreaksHemorrhagic Fever, EbolaHumansModels, StatisticalResearch DesignTherapeuticsAdaptive randomizationEbola virus diseaseEmerging infectious diseasesMulti-source smoothingPlatform design

Identifiers

PMID29359450
PMCPMC6560363
OpenAlexW2791402324

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

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.