ArticleThe New England Journal of Statistics in Data Science2024
Biomarker Panel Development Using Logic Regression in the Presence of Missing Data.
Article in The New England Journal of Statistics in Data Science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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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