• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Value in health: the journal of the International Society for Pharmacoeconomics and Outcomes Research >A New Statistical Method to Determine the Degree of Validity of Health Economic Model Outcomes against Empirical Data
【24h】

A New Statistical Method to Determine the Degree of Validity of Health Economic Model Outcomes against Empirical Data

机译：A New Statistical Method to Determine the Degree of Validity of Health Economic Model Outcomes against Empirical Data

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相关主题

摘要

Abstract Background The validation of health economic (HE) model outcomes against empirical data is of key importance. Although statistical testing seems applicable, guidelines for the validation of HE models lack guidance on statistical validation, and actual validation efforts often present subjective judgment of graphs and point estimates. Objectives To discuss the applicability of existing validation techniques and to present a new method for quantifying the degrees of validity statistically, which is useful for decision makers. Methods A new Bayesian method is proposed to determine how well HE model outcomes compare with empirical data. Validity is based on a pre-established accuracy interval in which the model outcomes should fall. The method uses the outcomes of a probabilistic sensitivity analysis and results in a posterior distribution around the probability that HE model outcomes can be regarded as valid. Results We use a published diabetes model (Modelling Integrated Care for Diabetes based on Observational data) to validate the outcome “number of patients who are on dialysis or with end-stage renal disease.” Results indicate that a high probability of a valid outcome is associated with relatively wide accuracy intervals. In particular, 25% deviation from the observed outcome implied approximately 60% expected validity. Conclusions Current practice in HE model validation can be improved by using an alternative method based on assessing whether the model outcomes fit to empirical data at a predefined level of accuracy. This method has the advantage of assessing both model bias and parameter uncertainty and resulting in a quantitative measure of the degree of validity that penalizes models predicting the mean of an outcome correctly but with overly wide credible intervals.

著录项

获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号