Impact of selection bias on the evaluation of clusters of chemical compounds in the drug discovery process |
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| Authors: | Ariel Alonso Elasma Milanzi Geert Molenberghs Christophe Buyck Luc Bijnens |
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| Affiliation: | 1. Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Katholieke Universiteit Leuven, Leuven, Belgium;2. Interuniversity Institute for Biostatistics and statistical Bioinformatics, Universiteit Hasselt, Diepenbeek, Belgium;3. Janssen Pharmaceutica, Beerse, Belgium |
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| 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. Copyright © 2014 John Wiley & Sons, Ltd. |
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| Keywords: | drug discovery missing data sensitivity analysis hierarchical models |
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