Sequential knockoffs for continuous and categorical predictors: With application to a large psoriatic arthritis clinical trial pool

Knockoffs provide a general framework for controlling the false discovery rate when performing variable selection. Much of the Knockoffs literature focuses on theoretical challenges and we recognize a need for bringing some of the current ideas into practice. In this paper we propose a sequential al...

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Bibliographic Details
Published in:Statistics in medicine 2021-06, Vol.40 (14), p.3313-3328
Main Authors: Kormaksson, Matthias, Kelly, Luke J., Zhu, Xuan, Haemmerle, Sibylle, Pricop, Luminita, Ohlssen, David
Format: Article
Language:English
Subjects:
Algorithms
Antibodies, Monoclonal, Humanized - therapeutic use
Arthritis
Arthritis, Psoriatic - drug therapy
Clinical Trials as Topic
Data Analysis
false discovery rate
Feature selection
Humans
knockoff filter
Psoriatic arthritis
sequential knockoffs
variable selection
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Summary:Knockoffs provide a general framework for controlling the false discovery rate when performing variable selection. Much of the Knockoffs literature focuses on theoretical challenges and we recognize a need for bringing some of the current ideas into practice. In this paper we propose a sequential algorithm for generating knockoffs when underlying data consists of both continuous and categorical (factor) variables. Further, we present a heuristic multiple knockoffs approach that offers a practical assessment of how robust the knockoff selection process is for a given dataset. We conduct extensive simulations to validate performance of the proposed methodology. Finally, we demonstrate the utility of the methods on a large clinical data pool of more than 2000 patients with psoriatic arthritis evaluated in four clinical trials with an IL‐17A inhibitor, secukinumab (Cosentyx), where we determine prognostic factors of a well established clinical outcome. The analyses presented in this paper could provide a wide range of applications to commonly encountered datasets in medical practice and other fields where variable selection is of particular interest.
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.8955