-- KathrynStoltzfus - 04 Apr 2018

An efficient procedure to combine biomarkers with limits of detection for risk prediction

Much research seeks biomarkers for diagnosing disease and understanding disease etiology.

As high-throughput technologies allow measuring multiple markers simultaneously, and particularly if no single marker is highly discriminating, strategies for combining markers are needed. In collaborations on studies of inflammation markers and cancer risk I faced the challenge of correlated markers with appreciable percentages of values outside the limits of detection of the assays. Only a few procedures have been proposed so far that address how to combine information from multiple correlated markers that are also left and/or right censored due to lower or upper limits of detection. We extend dimension reduction approaches, specifically likelihood-based sufficient dimension reduction (LDR) to regression or classification with censored predictors. These methods apply generally to any type of outcome, including continuous and categorical outcomes. Using an expectation maximization (EM) algorithm, we find linear combinations that contain all the information contained in correlated markers for modeling and prediction of an outcome variable, while accounting for left and right censoring due to detection limits. We also allow for selection of important variables through penalization. We assess the performance of our methods extensively in simulations and apply them to data from a study conducted to assess associations of 47 inflammatory markers and lung cancer risk and build prediction models.

This is joint work with Diego Tomassi, Liliana Forzani and Efstathia Bura E
Topic revision: r1 - 04 Apr 2018, KathrynStoltzfus

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