Oncology Modeling for Fun and Profit! Key Steps for Busy Analysts in Health Technology Assessment

Jaclyn Beca, Don Husereau, Kelvin K. W. Chan, Neil Hawkins and Jeffrey Hoch
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Jaclyn Beca: Cancer Care Ontario
Kelvin K. W. Chan: University of Toronto
Neil Hawkins: The University of Glasgow

PharmacoEconomics, 2018, vol. 36, issue 1, No 3, 7-15

Abstract: Abstract In evaluating new oncology medicines, two common modeling approaches are state transition (e.g., Markov and semi-Markov) and partitioned survival. Partitioned survival models have become more prominent in oncology health technology assessment processes in recent years. Our experience in conducting and evaluating models for economic evaluation has highlighted many important and practical pitfalls. As there is little guidance available on best practices for those who wish to conduct them, we provide guidance in the form of ‘Key steps for busy analysts,’ who may have very little time and require highly favorable results. Our guidance highlights the continued need for rigorous conduct and transparent reporting of economic evaluations regardless of the modeling approach taken, and the importance of modeling that better reflects reality, which includes better approaches to considering plausibility, estimating relative treatment effects, dealing with post-progression effects, and appropriate characterization of the uncertainty from modeling itself.

Date: 2018
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DOI: 10.1007/s40273-017-0583-4

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