Overview of Parametric Survival Analysis for Health-Economic Applications
- 1k Downloads
Health economic models rely on data from trials to project the risk of events (e.g., death) over time beyond the span of the available data. Parametric survival analysis methods can be applied to identify an appropriate statistical model for the observed data, which can then be extrapolated to derive a complete time-to-event curve. This paper describes the properties of the most commonly used statistical distributions as a basis for these models and describes an objective process of identifying the most suitable parametric distribution in a given dataset. The approach can be applied with both individual-patient data as well as with survival probabilities derived from published Kaplan–Meier curves. Both are illustrated with analyses of overall survival from the Sorafenib Hepatocellular Carcinoma Assessment Randomised Protocol trial.
KeywordsOverall Survival Sorafenib Survival Probability Individual Patient Data Accelerate Failure Time Model
This work was supported in part by a grant from Bayer HealthCare Pharmaceuticals to United BioSource Corporation. The authors are employees of United BioSource Corporation. Bayer HealthCare Pharmaceuticals reviewed and commented on this manuscript, but final editorial control was retained by the authors. KJI was responsible for the development of methods, oversaw analyses, and led the drafting and finalizing of the paper; NK carried out analyses, drafted parts of the paper, and reviewed drafts; AB and NM drafted parts of the paper and reviewed drafts.
- 1.Cuzick J, Sestak I, Baum M, et al, ATAC/LATTE investigators. Effect of anastrozole and tamoxifen as adjuvant treatment for early-stage breast cancer: 10-year analysis of the ATAC trial. Lancet Oncol. 2010;11(12):1135–1141.Google Scholar
- 4.Latimer N. NICE DSU Technical Support Document 14: undertaking survival analysis for economic evaluations alongside clinical trials—extrapolation with patient-level data. 2011. http://www.nicedsu.org.uk.
- 5.Collett D. Modelling survival data in medical research. 2nd ed. London: Chapman & Hall/CRC; 2003.Google Scholar
- 7.Kleinbaum DG, Klein M. Survival analysis: a self-learning text. Berlin: Springer; 2005.Google Scholar
- 9.Siegel S. Non-parametric statistics for the behavioral sciences. New York: McGraw-Hill; 1956. p. 75–83.Google Scholar
- 10.Cox DR. Regression models and life-tables. J R Stat Soc Ser B Stat Methodol. 1972;34(2):187–220.Google Scholar
- 13.StataCorp LP. STATA survival analysis and epidemiological tables. Reference manual, release 12. College Station: Stata Press; 2011.Google Scholar
- 17.Gray RJ. Flexible methods for analyzing survival data using splines, with applications to breast cancer prognosis. J Am Statist Assn. 1992;87(420):942–951.Google Scholar
- 19.Lambert PC, Royston P. Further development of flexible parametric models for survival analysis. Stata J. 2009;9(2):265–90.Google Scholar
- 20.Jackson CH, Sharples LD, Thompson SG. Survival models in health economic evaluations: balancing fit and parsimony to improve prediction. Int J Biostat. 2010;6(1):article 34.Google Scholar
- 27.Hind D, Tappenden P, Tumur I, et al. The use of irinotecan, oxaliplatin and raltitrexed for the treatment of advanced colorectal cancer: systematic review and economic evaluation. Health Technol Assess 2008;12(15):ii–ix, xi–162.Google Scholar
- 28.Thompson Coon J, Hoyle M, Green C, et al. Bevacizumab, sorafenib tosylate, sunitinib and temsirolimus for renal cell carcinoma: a systematic review and economic evaluation. Health Technol Assess. 2010;14(2):1–184, iii–iv.Google Scholar
- 29.Garside R, Pitt M, Anderson R, et al. The effectiveness and cost-effectiveness of carmustine implants and temozolomide for the treatment of newly diagnosed high-grade glioma: a systematic review and economic evaluation. Health Technol Assess. 2007;11(45):iii–iv, ix–221.Google Scholar