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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/18649

Title: Impact of selection bias on the evaluation of clusters of chemical compounds in the drug discovery process.
Authors: Milanzi, Elasma
Alonso Abad, Ariel
Molenberghs, Geert
Buyck, Christophe
Bijnens, Luc
Issue Date: 2015
Citation: Pharmaceutical statistics, 14 (2), p. 129-138
Abstract: Expert opinion plays an important role when selecting promising clusters of chemical compounds in the drug discovery process. Indeed, experts can qualitatively assess the potential of each cluster, and with appropriate statistical methods, these qualitative assessments can be quantified into a success probability for each of them. However, one crucial element often overlooked is the procedure by which the clusters are assigned to/selected by the experts for evaluation. In the present work, the impact such a procedure may have on the statistical analysis and the entire evaluation process is studied. It has been shown that some implementations of the selection procedure may seriously compromise the validity of the evaluation even when the rating and selection processes are independent. Consequently, the fully random allocation of the clusters to the experts is strongly advocated.
Notes: Correspondence to: Ariel Alonso, Interuniversity Institute for Biostatistics and statistical Bioinformatics, Katholieke Universiteit Leuven, B-3000 Leuven, Belgium. E-mail: Ariel.AlonsoAbad@kuleuven.be
URI: http://hdl.handle.net/1942/18649
DOI: 10.1002/pst.1665
ISI #: 000351527100007
ISSN: 1539-1604
Category: A1
Type: Journal Contribution
Validation: ecoom, 2016
Appears in Collections: Research publications

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