Chilcott J, Brennan A, Booth A, Karnon J, Tappenden P
School of Health and Related Research. University of Sheffield, UK.
Health Technol Assess. 2003;7(23):iii, 1-125. doi: 10.3310/hta7230.
To identify the role of modelling in planning and prioritising trials. The review focuses on modelling methods used in the construction of disease models and on methods for their analysis and interpretation.
Searches were initially developed in MEDLINE and then translated into other databases.
Systematic reviews of the methodological and case study literature were undertaken. Search strategies focused on the intersection between three domains: modelling, health technology assessment and prioritisation.
The review found that modelling can extend the validity of trials by: generalising from trial populations to specific target groups; generalising to other settings and countries; extrapolating trial outcomes to the longer term; linking intermediate outcome measures to final outcomes; extending analysis to the relevant comparators; adjusting for prognostic factors in trials; and synthesising research results. The review suggested that modelling may offer greatest benefits where the impact of a technology occurs over a long duration, where disease/technology characteristics are not observable, where there are long lead times in research, or for rapidly changing technologies. It was also found that modelling can inform the key parameters for research: sample size, trial duration and population characteristics. One-way, multi-way and threshold sensitivity analysis have been used in informing these aspects but are flawed. The payback approach has been piloted and while there have been weaknesses in its implementation, the approach does have potential. Expected value of information analysis is the only existing methodology that has been applied in practice and can address all these issues. The potential benefit of this methodology is that the value of research is directly related to its impact on technology commissioning decisions, and is demonstrated in real and absolute rather than relative terms; it assesses the technical efficiency of different types of research. Modelling is not a substitute for data collection. However, modelling can identify trial designs of low priority in informing health technology commissioning decisions.
Good practice in undertaking and reporting economic modelling studies requires further dissemination and support, specifically in sensitivity analyses, model validation and the reporting of assumptions. Case studies of the payback approach using stochastic sensitivity analyses should be developed. Use of overall expected value of perfect information should be encouraged in modelling studies seeking to inform prioritisation and planning of health technology assessments. Research is required to assess if the potential benefits of value of information analysis can be realised in practice; on the definition of an adequate objective function; on methods for analysing computationally expensive models; and on methods for updating prior probability distributions.
确定建模在试验规划及确定优先顺序中的作用。本综述聚焦于疾病模型构建中使用的建模方法及其分析与解释方法。
最初在MEDLINE中进行检索，然后将检索策略翻译至其他数据库。
对方法学及案例研究文献进行系统综述。检索策略聚焦于建模、卫生技术评估和确定优先顺序这三个领域的交叉点。
本综述发现，建模可通过以下方式扩展试验的有效性：从试验人群推广至特定目标群体；推广至其他环境和国家；将试验结果外推至更长期；将中间结局指标与最终结局相联系；将分析扩展至相关对照；在试验中对预后因素进行调整；以及综合研究结果。本综述表明，在技术影响持续时间长、疾病/技术特征不可观察、研究准备时间长或技术快速变化的情况下，建模可能带来最大益处。还发现建模可为研究的关键参数提供信息：样本量、试验持续时间和人群特征。单向、多向和阈值敏感性分析已用于为这些方面提供信息，但存在缺陷。投资回收期方法已进行试点，尽管其实施存在不足，但该方法确实具有潜力。信息期望值分析是唯一已在实践中应用且能解决所有这些问题的现有方法。该方法的潜在益处在于，研究的价值直接与其对技术委托决策的影响相关，并以实际和绝对而非相对的方式体现；它评估不同类型研究的技术效率。建模并非数据收集的替代品。然而，建模可识别在为卫生技术委托决策提供信息方面优先级较低的试验设计。
开展和报告经济建模研究的良好实践需要进一步传播和支持，特别是在敏感性分析、模型验证和假设报告方面。应开展使用随机敏感性分析的投资回收期方法的案例研究。在旨在为卫生技术评估的优先级确定和规划提供信息的建模研究中，应鼓励使用完全信息的总体期望值。需要开展研究，以评估信息分析价值的潜在益处能否在实践中实现；评估适当目标函数的定义；评估分析计算成本高昂的模型的方法；以及评估更新先验概率分布的方法。
