Sequential knockoffs for continuous and categorical predictors: With application to a large psoriatic arthritis clinical trial pool.
false discovery rate
knockoff filter
psoriatic arthritis
sequential knockoffs
variable selection
Journal
Statistics in medicine
ISSN: 1097-0258
Titre abrégé: Stat Med
Pays: England
ID NLM: 8215016
Informations de publication
Date de publication:
30 06 2021
30 06 2021
Historique:
revised:
22
02
2021
received:
20
10
2020
accepted:
01
03
2021
pubmed:
27
4
2021
medline:
30
6
2021
entrez:
26
4
2021
Statut:
ppublish
Résumé
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.
Substances chimiques
Antibodies, Monoclonal, Humanized
0
secukinumab
DLG4EML025
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
3313-3328Informations de copyright
© 2021 John Wiley & Sons Ltd.
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