Brief. Bioinformatics - Adjusting confounders in ranking biomarkers: a model-based ROC approach.

Tópicos

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Resumo

High-throughput studies have been extensively conducted in the research of complex human diseases. As a representative example, consider gene-expression studies where thousands of genes are profiled at the same time. An important objective of such studies is to rank the diagnostic accuracy of biomarkers (e.g. gene expressions) for predicting outcome variables while properly adjusting for confounding effects from low-dimensional clinical risk factors and environmental exposures. Existing approaches are often fully based on parametric or semi-parametric models and target evaluating estimation significance as opposed to diagnostic accuracy. Receiver operating characteristic (ROC) approaches can be employed to tackle this problem. However, existing ROC ranking methods focus on biomarkers only and ignore effects of confounders. In this article, we propose a model-based approach which ranks the diagnostic accuracy of biomarkers using ROC measures with a proper adjustment of confounding effects. To this end, three different methods for constructing the underlying regression models are investigated. Simulation study shows that the proposed methods can accurately identify biomarkers with additional diagnostic power beyond confounders. Analysis of two cancer gene-expression studies demonstrates that adjusting for confounders can lead to substantially different rankings of genes.

Resumo Limpo

highthroughput studi extens conduct research complex human diseas repres exampl consid geneexpress studi thousand gene profil time import object studi rank diagnost accuraci biomark eg gene express predict outcom variabl proper adjust confound effect lowdimension clinic risk factor environment exposur exist approach often fulli base parametr semiparametr model target evalu estim signific oppos diagnost accuraci receiv oper characterist roc approach can employ tackl problem howev exist roc rank method focus biomark ignor effect confound articl propos modelbas approach rank diagnost accuraci biomark use roc measur proper adjust confound effect end three differ method construct under regress model investig simul studi show propos method can accur identifi biomark addit diagnost power beyond confound analysi two cancer geneexpress studi demonstr adjust confound can lead substanti differ rank gene

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