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Unpublished Paper
Matching methods for biomarker evaluation: a mapping with causal inference
Technical Report, Department of Biostatistics and Informatics, University of Colorado (2015)
  • Debashis Ghosh, university of colorado denver
  • Michael S Sabel
Abstract

In many medical settings, there is interest in evaluating the predictive ability of a candidate biomarker while adjusting appropriately for confounding factors. Recently, Janes and Pepe (2008, {\it Biometrics} 64: 1 -- 9) evaluated the effects of matching on classification accuracy for biomarkers. In this article, we note an analogy between the use of matching in causal inference with its role in the biomarker evaluation problem. This leads us to be able to import much of the literature on matching from causal inferential settings to the biomarker evaluation problem. This leads to a theoretical characterization of the bias properties of matching using a modification of the concept equal percent bias reduction that has been previously developed in the literature. In addition, we can develop an approach to matching with multiple confounders using a `reverse propensity score.' Assumptions relevant to proper causal inference are adapted to the biomarker problem, and various tree-based regression modelling diagnostics are developed. The methodology is illustrated with application to evaluating the role of mitotic rate for the discrimination of sentinel lymph node (SLN) positive cases in melanoma.

Keywords
  • Bias reduction; causal effect; confounding; full matching; oncology; optimal matching.
Publication Date
2015
Citation Information
Debashis Ghosh and Michael S Sabel. "Matching methods for biomarker evaluation: a mapping with causal inference" Technical Report, Department of Biostatistics and Informatics, University of Colorado (2015)
Available at: http://works.bepress.com/debashis_ghosh/74/