Semi-parametric modelling for costs of health care technologies.
Cost data that arise in the evaluation of health care technologies usually exhibit highly skew, heavy-tailed and, possibly, multi-modal distributions. Distribution-free methods for analysing these data, such as the bootstrap, or those based on the asymptotic normality of sample means, may often lead to inefficient or misleading inferences. On the other hand, parametric models that fit the data (or a transformation of the data) equally well can produce very different answers. We consider a Bayesian approach, and model cost data with a distribution composed of a piecewise constant density up to an unknown endpoint, and a generalized Pareto distribution for the remaining tail.
Conigliani
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C
Università Roma Tre, Roma, Italy.
Tancredi
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A
D000818:Animals; D018927:Anti-Asthmatic Agents / Q000191:economics / Q000627:therapeutic use; D001249:Asthma / Q000188:drug therapy / Q000191:economics; D001499:Bayes Theorem; D017048:Health Care Costs; D006801:Humans; D008390:Markov Chains; D018803:Models, Economic; D015233:Models, Statistical; D009010:Monte Carlo Method; D015337:Multicenter Studies as Topic; D016032:Randomized Controlled Trials as Topic
D018927:Anti-Asthmatic Agents