Test for Incremental Value of New Biomarkers Based on OR Rules

TL;DR

This paper develops statistical methods to evaluate whether adding a new biomarker to existing ones improves disease detection performance using OR-based rules, addressing challenges in inference due to non-regularity.

Contribution

It introduces novel procedures for testing the incremental value of new biomarkers within OR rule frameworks, including bootstrap, cross-validation, and fuzzy p-value methods.

Findings

New methods effectively assess biomarker added value.

Simulation studies demonstrate method robustness.

Application to pancreatic cancer data shows practical utility.

Abstract

In early detection of disease, a single biomarker often has inadequate classification performance, making it important to identify new biomarkers to combine with the existing marker for improved performance. A biologically natural method to combine biomarkers is to use logic rules, e.g. the OR/AND rules. In our motivating example of early detection of pancreatic cancer, the established biomarker CA19-9 is only present in a subclass of cancer; it is of interest to identify new biomarkers present in the other subclasses and declare disease when either marker is positive. While there has been research on developing biomarker combinations using the OR/AND rules, the inference regarding the incremental value of the new marker within this framework is lacking and challenging due to a statistical non-regularity. In this paper, we aim to answer the inferential question of whether combining the new biomarker achieves better classification performance than using the existing biomarker alone, based on a nonparametrically estimated OR rule that maximizes the weighted average of sensitivity and specificity. We propose and compare various procedures for testing the incremental value of the new biomarker and constructing its confidence interval, using bootstrap, cross-validation, and a novel fuzzy p-value-based technique. We compare the performance of different methods via extensive simulation studies and apply them to the pancreatic cancer example.