Title |
Oncology Modeling for Fun and Profit! Key Steps for Busy Analysts in Health Technology Assessment
|
---|---|
Published in |
PharmacoEconomics, November 2017
|
DOI | 10.1007/s40273-017-0583-4 |
Pubmed ID | |
Authors |
Jaclyn Beca, Don Husereau, Kelvin K. W. Chan, Neil Hawkins, Jeffrey S. Hoch |
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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 4 | 18% |
Australia | 4 | 18% |
United States | 3 | 14% |
New Zealand | 3 | 14% |
Canada | 3 | 14% |
India | 1 | 5% |
Chile | 1 | 5% |
Unknown | 3 | 14% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 9 | 41% |
Scientists | 7 | 32% |
Science communicators (journalists, bloggers, editors) | 3 | 14% |
Practitioners (doctors, other healthcare professionals) | 2 | 9% |
Unknown | 1 | 5% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 31 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 5 | 16% |
Researcher | 4 | 13% |
Student > Ph. D. Student | 3 | 10% |
Other | 3 | 10% |
Student > Postgraduate | 2 | 6% |
Other | 1 | 3% |
Unknown | 13 | 42% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 8 | 26% |
Pharmacology, Toxicology and Pharmaceutical Science | 3 | 10% |
Economics, Econometrics and Finance | 2 | 6% |
Mathematics | 1 | 3% |
Business, Management and Accounting | 1 | 3% |
Other | 3 | 10% |
Unknown | 13 | 42% |