Jackson Christopher H, Sharples Linda D, Thompson Simon G
MRC Biostatistics Unit, Institute of Public Health, Forvie Site, Robinson Way, Cambridge CB2 0SR, UK.
Int J Biostat. 2010;6(1):Article 34. doi: 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.
健康经济决策模型长期比较不同干预措施的成本和健康效果，通常会纳入生存数据。由于生存情况常常是在数据范围之外进行推断，模型设定不准确可能导致截然不同的政策决策。然而，在这一领域，很少考虑灵活的生存模型，也很少考虑模型的不确定性。在本文中，各种生存分布被应用于口腔癌筛查的决策模型中。将灵活的参数模型与贝叶斯半参数模型进行比较，在贝叶斯半参数模型中，基线风险函数可以变得任意复杂，同时仍能进行生存情况的推断。所有模型都使用完全贝叶斯框架，以便在长期成本和效果估计中轻松纳入不确定性。使用偏差信息准则比较参数模型和半参数模型的拟合度和预测能力，以便在成本效益分析中考虑模型的不确定性。在贝叶斯半参数模型下，需要对风险函数进行一些平滑处理，以获得足够的预测能力，并避免对先验选择的敏感性。我们确定，在口腔癌示例中，一种灵活的参数生存模型的拟合效果明显优于其他考虑的模型。
