A PERMUTATIONAL-SPLITTING SAMPLE PROCEDURE TO QUANTIFY EXPERT OPINION ON CLUSTERS OF CHEMICAL COMPOUNDS USING HIGH-DIMENSIONAL DATA

• 成果类型: Article
• 署名作者: Milanzi, Elasma; Alonso, Ariel; Buyck, Christophe; Molenberghs, Geert; Bijnens, Luc
• 署名单位: Hasselt University; Maastricht University; Johnson & Johnson; Janssen Pharmaceuticals; Johson & Johnson Belgium; KU Leuven
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/14-AOAS772
• 发表日期: 2014
• 页码: 2319-2335
• 关键词: regression; models

摘要

Expert opinion plays an important role when selecting promising clusters of chemical compounds in the drug discovery process. We propose a method to quantify these qualitative assessments using hierarchical models. However, with the most commonly available computing resources, the high dimensionality of the vectors of fixed effects and correlated responses renders maximum likelihood unfeasible in this scenario. We devise a reliable procedure to tackle this problem and show, using theoretical arguments and simulations, that the new methodology compares favorably with maximum likelihood, when the latter option is available. The approach was motivated by a case study, which we present and analyze.