Jump to ContentJump to Main Navigation
Show Summary Details
In This Section

The International Journal of Biostatistics

Ed. by Chambaz, Antoine / Hubbard, Alan E. / van der Laan, Mark J.

2 Issues per year


IMPACT FACTOR 2015: 0.667
5-year IMPACT FACTOR: 1.188

SCImago Journal Rank (SJR) 2015: 0.495
Source Normalized Impact per Paper (SNIP) 2015: 0.180
Impact per Publication (IPP) 2015: 0.319

Mathematical Citation Quotient (MCQ) 2015: 0.04

Online
ISSN
1557-4679
See all formats and pricing
In This Section

Survival Models in Health Economic Evaluations: Balancing Fit and Parsimony to Improve Prediction

Christopher H Jackson
  • MRC Biostatistics Unit
/ Linda D Sharples
  • MRC Biostatistics Unit
/ Simon G Thompson
  • MRC Biostatistics Unit
Published Online: 2010-10-27 | DOI: https://doi.org/10.2202/1557-4679.1269

Health economic decision models compare costs and health effects of different interventions over the long term and usually incorporate survival data. Since survival is often extrapolated beyond the range of the data, inaccurate model specification can result in very different policy decisions. However, in this area, flexible survival models are rarely considered, and model uncertainty is rarely accounted for. In this article, various survival distributions are applied in a decision model for oral cancer screening. Flexible parametric models are compared with Bayesian semiparametric models, in which the baseline hazard can be made arbitrarily complex while still enabling survival to be extrapolated. A fully Bayesian framework is used for all models so that uncertainties can be easily incorporated in estimates of long-term costs and effects. The fit and predictive ability of both parametric and semiparametric models are compared using the deviance information criterion in order to account for model uncertainty in the cost-effectiveness analysis. Under the Bayesian semiparametric models, some smoothing of the hazard function is required to obtain adequate predictive ability and avoid sensitivity to the choice of prior. We determine that one flexible parametric survival model fits substantially better than the others considered in the oral cancer example.

Keywords: cost-effectiveness; model uncertainty; model comparison; Bayesian semiparametric; generalized F

About the article

Published Online: 2010-10-27



Citation Information: The International Journal of Biostatistics, ISSN (Online) 1557-4679, DOI: https://doi.org/10.2202/1557-4679.1269. Export Citation

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

[1]
Andrew Davies, Andrew Briggs, John Schneider, Adrian Levy, Omar Ebeid, Samuel Wagner, Srividya Kotapati, and Scott Ramsey
Health Outcomes Research in Medicine, 2012, Volume 3, Number 1, Page e25
[3]
Tatiana Benaglia, Christopher H. Jackson, and Linda D. Sharples
Statistics in Medicine, 2015, Volume 34, Number 5, Page 796
[5]
Patricia Guyot, Nicky J. Welton, Mario J.N.M. Ouwens, and A.E. Ades
Value in Health, 2011, Volume 14, Number 5, Page 640
[6]
Kirk A. Olson, Elise A. Larsen, Thomas Mueller, Peter Leimgruber, Todd K. Fuller, George B. Schaller, and William F. Fagan
The Journal of Wildlife Management, 2014, Volume 78, Number 1, Page 35
[7]
K. Jack Ishak, Noemi Kreif, Agnes Benedict, and Noemi Muszbek
PharmacoEconomics, 2013, Volume 31, Number 8, Page 663

Comments (0)

Please log in or register to comment.
Log in