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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/20858

Title: Validating predictors of therapeutic success: A causal inference approach
Authors: Alonso Abad, Ariel
Van der Elst, Wim
Molenberghs, Geert
Issue Date: 2015
Citation: Statistical modelling 15(6), p. 619-636
Abstract: In personalized medicine medical decisions, practices and/or products are tailored to the individual patient. The idea is to provide the right patient with the right drug at the right dose at the right time. However, our current lack of ability to predict an individual patient’s treatment success for most diseases and conditions is a major challenge to achieve the goal of personalized medicine. In the present work, we argue that many of the techniques often used to evaluate predictors of therapeutic success may not be able to answer the relevant scientific questions and we propose a new validation strategy based on causal inference. The methodology is illustrated using data from a clinical trial in opiate/heroin addiction. The user-friendly R library EffectTreat is provided to carry out the necessary calculations.
Notes: Address for correspondence: Ariel Abad Alonso, I-BioStat, Kapucijnenvoer 35, blok D, bus 7001, 3000 Leuven, Belgium. E-mail: Ariel.AlonsoAbad@kuleuven.be
URI: http://hdl.handle.net/1942/20858
DOI: 10.1177/1471082X15586286
ISI #: 000369699200006
ISSN: 1471-082X
Category: A1
Type: Journal Contribution
Validation: ecoom, 2017
Appears in Collections: Research publications

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