Sequential knockoffs for continuous and categorical predictors: with application to a large Psoriatic Arthritis clinical trial pool

TL;DR

This paper introduces a sequential knockoff algorithm for mixed data types, validated through simulations and applied to identify prognostic factors in a large psoriatic arthritis clinical trial dataset.

Contribution

It presents a novel sequential algorithm for generating knockoffs for mixed continuous and categorical data, along with a heuristic approach for robustness assessment.

Findings

Validated the method through extensive simulations.

Successfully identified prognostic factors in a large clinical trial dataset.

Abstract

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 data set. 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 $2,000$ patients with psoriatic arthritis evaluated in 4 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 data sets in medical practice and other fields where variable selection is of particular interest.