Centre for Health Economics, University of York, York, UK.
Med Decis Making. 2024 Oct;44(7):802-810. doi: 10.1177/0272989X241262037. Epub 2024 Jul 26.
Decision models are time-consuming to develop; therefore, adapting previously developed models for new purposes may be advantageous. We provide methods to prioritize efforts to 1) update parameter values in existing models and 2) adapt existing models for distributional cost-effectiveness analysis (DCEA).
Methods exist to assess the influence of different input parameters on the results of a decision models, including value of information (VOI) and 1-way sensitivity analysis (OWSA). We apply 1) VOI to prioritize searches for additional information to update parameter values and 2) OWSA to prioritize searches for parameters that may vary by socioeconomic characteristics. We highlight the assumptions required and propose metrics that quantify the extent to which parameters in a model have been updated or adapted. We provide R code to quickly carry out the analysis given inputs from a probabilistic sensitivity analysis (PSA) and demonstrate our methods using an oncology case study.
In our case study, updating 2 of 21 probabilistic model parameters addressed 71.5% of the total VOI and updating 3 addressed approximately 100% of the uncertainty. Our proposed approach suggests that these are the 3 parameters that should be prioritized. For model adaptation for DCEA, 46.3% of the total OWSA variation came from a single parameter, while the top 10 input parameters were found to account for more than 95% of the total variation, suggesting efforts should be aimed toward these.
These methods offer a systematic approach to guide research efforts in updating models with new data or adapting models to undertake DCEA. The case study demonstrated only very small gains from updating more than 3 parameters or adapting more than 10 parameters.
It can require considerable analyst time to search for evidence to update a model or to adapt a model to take account of equity concerns.In this article, we provide a quantitative method to prioritze parameters to 1) update existing models to reflect potential new evidence and 2) adapt existing models to estimate distributional outcomes.We define metrics that quantify the extent to which the parameters in a model have been updated or adapted.We provide R code that can quickly rank parameter importance and calculate quality metrics using only the results of a standard probabilistic sensitivity analysis.
决策模型的开发耗时耗力；因此，将已开发的模型应用于新的目的可能具有优势。我们提供了方法来优先考虑以下两项工作：1）更新现有模型中的参数值，2）使现有模型适应分布成本效益分析（DCEA）。
现有的方法可以评估不同输入参数对决策模型结果的影响，包括信息价值（VOI）和单向敏感性分析（OWSA）。我们应用 1）VOI 来优先搜索额外的信息以更新参数值，2）OWSA 来优先搜索可能因社会经济特征而异的参数。我们强调了所需的假设，并提出了量化模型中参数更新或适应程度的指标。我们提供了 R 代码，可以根据概率敏感性分析（PSA）的输入快速进行分析，并通过肿瘤学案例研究演示了我们的方法。
在我们的案例研究中，更新 21 个概率模型参数中的 2 个，解决了 71.5%的总 VOI，而更新 3 个解决了大约 100%的不确定性。我们提出的方法表明，这是应该优先考虑的 3 个参数。对于 DCEA 的模型适应，总 OWSA 变化的 46.3%来自单个参数，而前 10 个输入参数被发现占总变化的 95%以上，这表明应该针对这些参数进行努力。
这些方法提供了一种系统的方法，可以指导使用新数据更新模型或使模型适应进行 DCEA 的研究工作。案例研究表明，更新超过 3 个参数或适应超过 10 个参数只能带来很小的收益。
搜索证据更新模型或调整模型以考虑公平性问题可能需要分析师大量时间。在本文中，我们提供了一种定量方法来优先考虑参数，以 1）更新现有模型以反映潜在的新证据，2）使现有模型适应估计分布结果。我们定义了量化模型中参数更新或适应程度的指标。我们提供了 R 代码，可以仅使用标准概率敏感性分析的结果快速对参数重要性进行排名并计算质量指标。
