Evidence map›Paper›PMID 36690722›Full record

ArticleBritish journal of cancer2023

Confirmatory prediction-driven RCTs in comparative effectiveness settings for cancer treatment.

Adam Brand, Michael C Sachs, Arvid Sjölander, Erin E Gabriel

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Article in British journal of cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

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

4 authors.

Adam BrandDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden. adambrand22@gmail.com.ORCID 0000-0003-1300-5292
Michael C SachsDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden.ORCID 0000-0002-1279-8676
Arvid SjölanderDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden.
Erin E GabrielDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Solna, Sweden.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMedical advances in the treatment of cancer have allowed the development of multiple approved treatments and prognostic and predictive biomarkers for many types of cancer. Identifying improved treatment strategies among approved treatment options, the study of which is termed comparative effectiveness, using predictive biomarkers is becoming more common. RCTs that incorporate predictive biomarkers into the study design, called prediction-driven RCTs, are needed to rigorously evaluate these treatment strategies. Although researched extensively in the experimental treatment setting, literature is lacking in providing guidance about prediction-driven RCTs in the comparative effectiveness setting.

methodsRealistic simulations with time-to-event endpoints are used to compare contrasts of clinical utility and provide examples of simulated prediction-driven RCTs in the comparative effectiveness setting.

resultsOur proposed contrast for clinical utility accurately estimates the true clinical utility in the comparative effectiveness setting while in some scenarios, the contrast used in current literature does not. DISCUSSION: It is important to properly define contrasts of interest according to the treatment setting. Realistic simulations should be used to choose and evaluate the RCT design(s) able to directly estimate that contrast. In the comparative effectiveness setting, our proposed contrast for clinical utility should be used.

Indexed as

NeoplasmsResearch DesignHumans

Identifiers

PMID36690722
PMCPMC10050232

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LicenceCC BY
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table measurements read7
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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.