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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

Biomarker validation with an imperfect reference: Issues and bounds.

Sarah C Emerson¹, Sushrut S Waikar², Claudio Fuentes¹

¹1 Department of Statistics, Oregon State University, Corvallis, OR, USA.

Statistical Methods in Medical Research

|February 8, 2017

Summary

This summary is machine-generated.

Evaluating new biomarkers for acute kidney injury is challenging without a gold standard. This study shows reference test errors bias new biomarker assessments, but bounds can still offer useful performance insights.

Keywords:

Biomarkers conditional independence diagnostic tests imperfect reference sensitivity specificity

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Area of Science:

Biomarker discovery and validation
Diagnostic test evaluation
Medical statistics

Background:

Assessing new biomarkers for acute kidney injury (AKI) is crucial but difficult without a definitive gold standard.
Existing methods often rely on imperfect reference tests, introducing potential bias in performance evaluation.
Latent class analysis and similar techniques use strong, often unverifiable assumptions.

Purpose of the Study:

To investigate the impact of reference test errors on new biomarker performance assessment.
To analyze the conditional independence assumption in biomarker evaluation.
To establish bounds for the true sensitivity and specificity of a new biomarker.

Main Methods:

Exploration of the conditional independence assumption in statistical models for biomarker assessment.
Analysis of information content derived from comparing a new biomarker with a reference test.
Derivation of bounds for sensitivity and specificity using known reference test operating characteristics.

Main Results:

Conditional independence is only feasible within a limited range of disease prevalence values for observed data.
Bounds for the new biomarker's sensitivity and specificity can be derived.
The utility of these bounds varies, being tight and informative in some scenarios and wide in others.

Conclusions:

Reference test inaccuracies significantly bias new biomarker evaluations.
While bounds for sensitivity and specificity can be informative, their width depends on the data and reference test performance.
Careful consideration of assumptions and potential biases is necessary when evaluating biomarkers without a gold standard.