Comparative Effectiveness of Biomarkers to Target Cancer Treatment

Jeanette K. Birnbaum, Foluso O. Ademuyiwa, Josh J. Carlson, Leslie Mallinger, Mark W. Mason and Ruth Etzioni

Medical Decision Making, 2016, vol. 36, issue 5, 594-603

Abstract: Background. Biomarkers used at the time of diagnosis to tailor treatment decisions may diffuse into clinical practice before data become available on whether biomarker testing reduces cancer mortality. In the interim, quantitative estimates of the mortality impact of testing are needed to assess the value of these diagnostic biomarkers. These estimates are typically generated by customized models that are resource intensive to build and apply. Methods. We developed a user-friendly system of models for Cancer Translation of Comparative Effectiveness Research (CANTRANce) to model the mortality impact of cancer interventions. The Diagnostic Biomarker module of this system projects the mortality impact of testing for a diagnostic biomarker, given data on how testing affects treatment recommendations. Costs and quality-of-life outcomes may also be modeled. We applied the Diagnostic Biomarker module to 2 case studies to demonstrate its capabilities. Results. The user interface ( http://www.fhcrc.org/cantrance ) allows comparative effectiveness researchers to use the Diagnostic Biomarker module of CANTRANce. Our case studies indicate that the model produces estimates on par with those generated by customized models and is a strong tool for quickly generating novel projections. Limitations. The simple structure that makes CANTRANce user-friendly also constrains the complexity with which cancer progression can be modeled. The quality of the results rests on the quality of the input data, which may pertain to small or dissimilar populations or suffer from informative censoring. Conclusions. The Diagnostic Biomarker module of CANTRANce is a novel public resource that can provide timely insights into the expected mortality impact of testing for diagnostic biomarkers. The model projections should be useful for understanding the long-term potential of emerging diagnostic biomarkers.

Keywords: decision analysis; breast cancer; outcomes research; comparative effectiveness; simulation methods (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:36:y:2016:i:5:p:594-603

DOI: 10.1177/0272989X15601998

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