  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.
