Flexible variable selection in the presence of missing data

TL;DR

This paper introduces a nonparametric variable selection method with multiple imputation to effectively identify predictive feature panels in datasets with missing-at-random data, outperforming traditional penalized regression when models are misspecified.

Contribution

It proposes a novel nonparametric algorithm combined with multiple imputation for flexible variable selection under missing data, addressing limitations of model-based approaches.

Findings

Demonstrates improved classification performance over penalized regression methods.

Achieves control of error rates in variable selection.

Successfully applied to biomarker panel development for pancreatic cysts.

Abstract

In many applications, it is of interest to identify a parsimonious set of features, or panel, from multiple candidates that achieves a desired level of performance in predicting a response. This task is often complicated in practice by missing data arising from the sampling design or other random mechanisms. Most recent work on variable selection in missing data contexts relies in some part on a finite-dimensional statistical model, e.g., a generalized or penalized linear model. In cases where this model is misspecified, the selected variables may not all be truly scientifically relevant and can result in panels with suboptimal classification performance. To address this limitation, we propose a nonparametric variable selection algorithm combined with multiple imputation to develop flexible panels in the presence of missing-at-random data. We outline strategies based on the proposed algorithm that achieve control of commonly used error rates. Through simulations, we show that our proposal has good operating characteristics and results in panels with higher classification and variable selection performance compared to several existing penalized regression approaches in cases where a generalized linear model is misspecified. Finally, we use the proposed method to develop biomarker panels for separating pancreatic cysts with differing malignancy potential in a setting where complicated missingness in the biomarkers arose due to limited specimen volumes.