Overview of Parametric Survival Analysis for Health-Economic Applications
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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.
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