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

arXiv: https://arxiv.org/abs/2010.14026 · HTML: https://mlsanity.com/paper/2010.14026/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2020.

## Built on by (most cited first, 1 of 1)

- [Conditional Feature Importance for Mixed Data](https://mlsanity.com/paper/2210.03047/) (2022)

## Abstract (arXiv)

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.