Use of Multiparameter Evidence Synthesis to Assess the Appropriateness of Data and Structure in Decision Models
Use of Multiparameter Evidence Synthesis to Assess the Appropriateness of Data and Structure in Decision Models
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DOI：
10.1177/0272989x13480130
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发表时间：
2013-07-01
影响因子：
3.6
通讯作者：
Soares, Marta O.
中科院分区：
文献类型：
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作者：
Epstein, David;Garcia Mochon, Leticia;Soares, Marta O.
目标。卫生技术评估的决策模型由其结构和数据定义。对于如何指定模型以及包含哪些数据，通常有多种选择，并且需要标准来指导这些选择。本研究使用多参数证据合成（MPES）来合成来自不同来源的数据并测试替代模型结构。通过比较患有 1 型糖尿病的年轻人的血酮检测与尿酮检测来说明这些方法。方法。比较了两种方法。使用简单的统计模型（模型 1）来估计不良事件发生率与随机对照试验 (RCT) 结果数据的差异。 MPES（模型 2）的构建是为了综合 RCT 的结果和过程变量数据以及特异性和敏感性非随机研究的数据。使用 MPES 的替代模型规范进行敏感性分析，并评估数据的一致性。结果。模型 1 估计，通过血酮测试，每天不良事件发生率的平均差异降低了 0.0011（95% 置信区间 0.0005-0.00229）。模型 2 估计了类似的结果，但也估计了没有直接数据的参数，包括高酮水平的患病率以及家庭使用的测试的敏感性和特异性。结论。模型 1 仅使用随机对照试验的结果数据，表明血酮测试更有效，但没有解释为什么会这样。 MPES 估计的模型 2 表明血液检测更准确，患者更有可能遵守方案。
Objectives. Decision models for health technology appraisal are defined by their structure and data. Often there are alternatives for how the model might be specified and what data to include, and criteria are required to guide these choices. This study uses multiparameter evidence synthesis (MPES) to synthesize data from diverse sources and test alternative model structures. The methods are illustrated by a comparison of blood ketone testing versus urine ketone testing for young people with Type 1 diabetes. Methods. Two approaches were compared. A simple statistical model (Model 1) was used to estimate the difference in the rates of adverse events from the outcome data of a randomized controlled trial (RCT). MPES (Model 2) was constructed to synthesize data on outcome and process variables from the RCT with data from nonrandomized studies on specificity and sensitivity. Sensitivity analyses were carried out using alternative model specifications for the MPES, and the consistency of the data was evaluated. Results. Model 1 estimated that the mean difference in the rate of adverse events per day was 0.0011 (95% confidence interval 0.0005-0.00229) lower with blood ketone testing. Model 2 estimated a similar outcome but also estimated parameters for which there were no direct data, including the prevalence of high ketone levels and the sensitivity and specificity of the tests as used in the home. Conclusions. Model 1, which used only outcome data from an RCT, showed that blood ketone testing is more effective but did not explain why this is so. Model 2, estimated by MPES, suggested that the blood test is more accurate and that patients are more likely to comply with the protocol.