Reduction of recruitment costs in preclinical AD trials: validation of automatic pre-screening algorithm for brain amyloidosis.
We propose a method for recruiting asymptomatic Amyloid positive individuals in clinical trials, using a two-step process. We first select during a pre-screening phase a subset of individuals which are more likely to be amyloid positive based on the automatic analysis of data acquired during routine...
| Publicado en: | Statistical Methods in Medical Research Vol. 29; no. 1; pp. 151 - 165 |
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| Autores principales: | , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Sage Publications Inc.
Jan2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=141396002&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141396002 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09622802 31F jtl: Statistical Methods in Medical Research issn: 09622802 maglogo: Y pubinfo: dt: Jan2020 vid: 29 iid: 1 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 141396002 141396002 NLM30698081 141396002 10.1177/0962280218823036 NLM30698081 141396002 ppf: 151 ppct: 14 formats: tig: atl: Reduction of recruitment costs in preclinical AD trials: validation of automatic pre-screening algorithm for brain amyloidosis. aug: au: Ansart, Manon Epelbaum, Stéphane Gagliardi, Geoffroy Colliot, Olivier Dormont, Didier Dubois, Bruno Hampel, Harald Durrleman, Stanley affil: Institut du Cerveau et de la Moelle épinière, ICM, Inserm, U 1127, CNRS, UMR 7225, Sorbonne Université, Paris, France sug: subj: Patient Selection Alzheimer's Disease Tomography, Emission-Computed Algorithms Health Screening Economics Amyloidosis Prospective Studies Human Clinical Trials France Disease Progression Models, Statistical Comparative Studies Multicenter Studies Evaluation Research Validation Studies ab: We propose a method for recruiting asymptomatic Amyloid positive individuals in clinical trials, using a two-step process. We first select during a pre-screening phase a subset of individuals which are more likely to be amyloid positive based on the automatic analysis of data acquired during routine clinical practice, before doing a confirmatory PET-scan to these selected individuals only. This method leads to an increased number of recruitments and to a reduced number of PET-scans, resulting in a decrease in overall recruitment costs. We validate our method on three different cohorts, and consider five different classification algorithms for the pre-screening phase. We show that the best results are obtained using solely cognitive, genetic and socio-demographic features, as the slight increased performance when using MRI or longitudinal data is balanced by the cost increase they induce. We show that the proposed method generalizes well when tested on an independent cohort, and that the characteristics of the selected set of individuals are identical to the characteristics of a population selected in a standard way. The proposed approach shows how Machine Learning can be used effectively in practice to optimize recruitment costs in clinical trials. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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