A Modified Risk Set Approach to Biomarker Evaluation Studies

Debashis Ghosh ()
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Debashis Ghosh: Colorado School of Public Health

Statistics in Biosciences, 2016, vol. 8, issue 2, No 13, 395-406

Abstract: Abstract There is tremendous scientific and medical interest in the use of biomarkers to better facilitate medical decision making. In this article, we present a simple framework for assessing the predictive ability of a biomarker. The methodology requires use of techniques from a subfield of survival analysis termed semi-competing risks; results are presented to make the article self-contained. As we show in the article, one natural interpretation of semi-competing risks model is in terms of modifying the classical risk set approach to survival analysis that is more germane to medical decision making. A crucial parameter for evaluating biomarkers is the predictive hazard ratio, which is different from the usual hazard ratio from Cox regression models for right-censored data. This quantity will be defined; its estimation, inference, and adjustment for covariates will be discussed. Aspects of causal inference related to these procedures will also be described. The methodology is illustrated with an evaluation of serum albumin in terms of predicting death in patients with primary biliary cirrhosis.

Keywords: Association; Causal effect; Copula; Cross-ratio; Dependence; Diagnostics (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s12561-016-9166-8

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