Methods for expected value of information analysis in complex health economic models: developments on the health economics of interferon-beta and glatiramer acetate for multiple sclerosis.
Methods for expected value of information analysis in complex health economic models: developments on the health economics of interferon-beta and glatiramer acetate for multiple sclerosis.
复制标题
复杂健康经济模型中信息分析预期值的方法：干扰素-β和醋酸格拉替雷治疗多发性硬化症的健康经济学进展。
DOI：
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发表时间：
2004
影响因子：
3.6
通讯作者：
C. McCabe
中科院分区：
文献类型：
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作者：
P. Tappenden;James B Chilcott;S. Eggington;J. Oakley;C. McCabe
目标
开发用于在计算昂贵的模型中进行完全信息期望值（EVPI）分析的方法，并报告使用该方法框架管理多发性硬化症（MS）中干扰素β和醋酸格拉替雷的卫生经济学发展。
数据源
电子数据库和互联网资源，相关文章的参考清单。
复习方法
开发了一个方法框架，用于对复杂模型进行EVPI分析。该框架确定了EVPI可以用数值计算的条件，其中一级算法充分近似两级算法，并且元建模技术可以准确地近似原始仿真模型。元建模技术，包括线性回归，神经网络和高斯过程（GP），进行了系统的审查和批判性的评价。线性回归元建模，GP元建模和一级EVPI近似被用来估计部分EVPI使用ScHARR MS成本效益模型。
结果
对元建模方法的审查表明，一般来说，线性回归等更简单的技术可能更容易实现，因为它们只需要很少的专业知识，尽管可能只提供有限的预测准确性。更复杂的方法，如高斯过程元建模和神经网络往往使用较少的限制性假设模型输入和净效益之间的关系，因此可能允许更高的准确性，估计经济绩效指标。假设独立治疗有效性，ScHARR MS模型中所有不确定性参数的“每例患者”EVPI为8855英镑。这导致人口EVPI为86，208，936英镑，这是10年来总体EVPI的上限估计。假设所有治疗效果完全相关，每位患者的总体EVPI为4271英镑。这导致人口EVPI为41，581，273英镑，这是10年来整体EVPI的较低估计数。使用线性回归元模型和高斯过程元模型进行的部分EVPI分析清楚地表明，需要进一步研究这些治疗对疾病进展的长期影响，患者放弃治疗的比例以及EDSS，生活质量和护理费用之间的关系。
结论
所应用的方法指向使用更复杂的元建模方法，以获得更高的准确性，在EVPI估计。在选择元建模技术时，应考虑编程需求、软件可用性和统计准确性。更简单、更容易获得的技术可能会产生更大的预测误差，而复杂的方法可能会提高非线性模型的准确性，但实施起来要困难得多，可能需要专业知识。这些技术仅在有限数量的情况下应用，因此尚未证明其适用于EVPI分析。一些需要进一步研究的领域已得到强调。进一步的临床研究是必要的关于EDSS之间的关系，护理和健康结果的成本，在患者脱落治疗，特别是疾病修饰疗法对MS的进展的影响率。进一步的方法学研究表明，关于纳入流行病学人口参数的敏感性分析;选择一个元建模方法的标准的发展;元建模技术在卫生经济模型中的应用以及在经济脆弱性指数分析中的具体应用;以及元建模在经济脆弱性指数和环境净资产分析中的应用。
OBJECTIVES
To develop methods for performing expected value of perfect information (EVPI) analysis in computationally expensive models and to report on the developments on the health economics of interferon-beta and glatiramer acetate in the management of multiple sclerosis (MS) using this methodological framework.
DATA SOURCES
Electronic databases and Internet resources, reference lists of relevant articles.
REVIEW METHODS
A methodological framework was developed for undertaking EVPI analysis for complex models. The framework identifies conditions whereby EVPI may be calculated numerically, where the one-level algorithm sufficiently approximates the two-level algorithm, and whereby metamodelling techniques may accurately approximate the original simulation model. Metamodelling techniques, including linear regression, neural networks and Gaussian processes (GP), were systematically reviewed and critically appraised. Linear regression metamodelling, GP metamodelling and the one-level EVPI approximation were used to estimate partial EVPIs using the ScHARR MS cost-effectiveness model.
RESULTS
The review of metamodelling approaches suggested that in general the simpler techniques such as linear regression may be easier to implement, as they require little specialist expertise although may provide only limited predictive accuracy. More complex methods such as Gaussian process metamodelling and neural networks tend to use less-restrictive assumptions concerning the relationship between the model inputs and net benefits, and therefore may permit greater accuracy in estimating EVPIs. Assuming independent treatment efficacy, the 'per patient' EVPI for all uncertainty parameters within the ScHARR MS model is 8855 British pounds. This leads to a population EVPI of 86,208,936 British pounds, which represents the upper estimate for the overall EVPI over 10 years. Assuming all treatment efficacies are perfectly correlated, the overall per patient EVPI is 4271 British pounds. This leads to a population EVPI of 41,581,273 British pounds, which represents the lower estimate for the overall EVPI over 10 years. The partial EVPI analysis, undertaken using both the linear regression metamodel and Gaussian process metamodel clearly, suggests that further research is indicated on the long-term impact of these therapies on disease progression, the proportion of patients dropping off therapy and the relationship between the EDSS, quality of life and costs of care.
CONCLUSIONS
The applied methodology points towards using more sophisticated metamodelling approaches in order to obtain greater accuracy in EVPI estimation. Programming requirements, software availability and statistical accuracy should be considered when choosing between metamodelling techniques. Simpler, more accessible techniques are open to greater predictive error, whilst sophisticated methodologies may enhance accuracy within non-linear models, but are considerably more difficult to implement and may require specialist expertise. These techniques have been applied in only a limited number of cases hence their suitability for use in EVPI analysis has not yet been demonstrated. A number of areas requiring further research have been highlighted. Further clinical research is required concerning the relationship between the EDSS, costs of care and health outcomes, the rates at which patients drop off therapy and in particular the impact of disease-modifying therapies on the progression of MS. Further methodological research is indicated concerning the inclusion of epidemiological population parameters within the sensitivity analysis; the development of criteria for selecting a metamodelling approach; the application of metamodelling techniques within health economic models and in the specific application to EVI analyses; and the use of metamodelling for EVSI and ENBS analysis.