Literature DB >> 33407710

Adapting the randomised controlled trial (RCT) for precision medicine: introducing the nested-precision RCT (npRCT).

Nils Kappelmann1,2, Bertram Müller-Myhsok3,4, Johannes Kopf-Beck5.   

Abstract

Adaptations to the gold standard randomised controlled trial (RCT) have been introduced to decrease trial costs and avoid high sample sizes. To facilitate development of precision medicine algorithms that aim to optimise treatment allocation for individual patients, we propose a new RCT adaptation termed the nested-precision RCT (npRCT). The npRCT combines a traditional RCT (intervention A versus B) with a precision RCT (stratified versus randomised allocation to A or B). This combination allows online development of a precision algorithm, thus providing an integrated platform for algorithm development and its testing. Moreover, as both the traditional and the precision RCT include participants randomised to interventions of interest, data from these participants can be jointly analysed to determine the comparative effectiveness of intervention A versus B, thus increasing statistical power. We quantify savings of the npRCT compared to two independent RCTs by highlighting sample size requirements for different target effect sizes and by introducing an open-source power calculation app. We describe important practical considerations such as blinding issues and potential biases that need to be considered when designing an npRCT. We also highlight limitations and research contexts that are less suited for an npRCT. In conclusion, we introduce the npRCT as a novel precision medicine trial design strategy which may provide one opportunity to efficiently combine traditional and precision RCTs.

Entities:  

Keywords:  Nested-precision randomised controlled trial; Precision medicine; Randomised controlled trial

Mesh:

Year:  2021        PMID: 33407710      PMCID: PMC7788903          DOI: 10.1186/s13063-020-04965-0

Source DB:  PubMed          Journal:  Trials        ISSN: 1745-6215            Impact factor:   2.279


  5 in total

1.  A demonstration of a multi-method variable selection approach for treatment selection: Recommending cognitive-behavioral versus psychodynamic therapy for mild to moderate adult depression.

Authors:  Zachary D Cohen; Thomas T Kim; Henricus L Van; Jack J M Dekker; Ellen Driessen
Journal:  Psychother Res       Date:  2019-01-11

2.  Controlled multi-arm platform design using predictive probability.

Authors:  Brian P Hobbs; Nan Chen; J Jack Lee
Journal:  Stat Methods Med Res       Date:  2016-01-12       Impact factor: 3.021

3.  The Personalized Advantage Index: translating research on prediction into individualized treatment recommendations. A demonstration.

Authors:  Robert J DeRubeis; Zachary D Cohen; Nicholas R Forand; Jay C Fournier; Lois A Gelfand; Lorenzo Lorenzo-Luaces
Journal:  PLoS One       Date:  2014-01-08       Impact factor: 3.240

Review 4.  New clinical trial designs in the era of precision medicine.

Authors:  Elena Garralda; Rodrigo Dienstmann; Alejandro Piris-Giménez; Irene Braña; Jordi Rodon; Josep Tabernero
Journal:  Mol Oncol       Date:  2019-02-22       Impact factor: 6.603

5.  A Bayesian adaptive design for biomarker trials with linked treatments.

Authors:  James M S Wason; Jean E Abraham; Richard D Baird; Ioannis Gournaris; Anne-Laure Vallier; James D Brenton; Helena M Earl; Adrian P Mander
Journal:  Br J Cancer       Date:  2015-08-11       Impact factor: 7.640

  5 in total

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