Overview of Parametric Survival Analysis for Health-Economic Applications

• Published in: PharmacoEconomics Vol. 31; no. 8; pp. 663 - 675
• Main Authors: Ishak, K. Jack, Kreif, Noemi, Benedict, Agnes, Muszbek, Noemi
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01-08-2013; Adis International; Springer; Springer Nature B.V
• Subjects: Analysis. Health state; Antineoplastic Agents - economics; Antineoplastic Agents - therapeutic use; Biological and medical sciences; Carcinoma, Hepatocellular - drug therapy; Carcinoma, Hepatocellular - mortality; Cost control; Economic models; Epidemiology; Evaluation; General aspects; Health Administration; Health Economics; Health technology assessment; Humans; Liver cancer; Liver Neoplasms - drug therapy; Liver Neoplasms - mortality; Medical economics; Medical sciences; Medicine; Medicine & Public Health; Models, Economic; Outcome and process assessment (Health Care); Patients; Pharmacoeconomics and Health Outcomes; Probability distribution; Public Health; Public health. Hygiene; Public health. Hygiene-occupational medicine; Quality of Life Research; Random variables; Randomized Controlled Trials as Topic; Review Article; Statistical methods; Studies; Survival Analysis; Textbooks; Canada; North America; America; United Kingdom; Survival Probability; Accelerate Failure Time Model; Overall Survival; Sorafenib; Individual Patient Data; Review; Statistical distribution; Survival; Health economy; Statistical model; Public health
• ISSN: 1170-7690; 1179-2027
• DOI: 10.1007/s40273-013-0064-3

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.