Biomarker Panel Development Using Logic Regression in the Presence of Missing Data
Volume 2, Issue 1 (2024), pp. 3–14
Pub. online: 31 January 2024
Type: Methodology Article
Open Access
Open Access
Area: Biomedical Research
Accepted
20 December 2023
20 December 2023
Published
31 January 2024
31 January 2024
Abstract
We consider the problem of developing flexible and parsimonious biomarker combinations for cancer early detection in the presence of variable missingness at random. Motivated by the need to develop biomarker panels in a cross-institute pancreatic cyst biomarker validation study, we propose logic-regression based methods for feature selection and construction of logic rules under a multiple imputation framework. We generate ensemble trees for classification decision, and further select a single decision tree for simplicity and interpretability. We demonstrate superior performance of the proposed methods compared to alternative methods based on complete-case data or single imputation. The methods are applied to the pancreatic cyst data to estimate biomarker panels for pancreatic cysts subtype classification and malignant potential prediction.
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