School of Social and Community-Based Medicine, University of Bristol, Bristol, UK.
Value Health. 2011 Mar-Apr;14(2):205-18. doi: 10.1016/j.jval.2010.08.001.
Standard approaches to estimation of Markov models with data from randomized controlled trials tend either to make a judgment about which transition(s) treatments act on, or they assume that treatment has a separate effect on every transition. An alternative is to fit a series of models that assume that treatment acts on specific transitions. Investigators can then choose among alternative models using goodness-of-fit statistics. However, structural uncertainty about any chosen parameterization will remain and this may have implications for the resulting decision and the need for further research.
We describe a Bayesian approach to model estimation, and model selection. Structural uncertainty about which parameterization to use is accounted for using model averaging and we developed a formula for calculating the expected value of perfect information (EVPI) in averaged models. Marginal posterior distributions are generated for each of the cost-effectiveness parameters using Markov Chain Monte Carlo simulation in WinBUGS, or Monte-Carlo simulation in Excel (Microsoft Corp., Redmond, WA). We illustrate the approach with an example of treatments for asthma using aggregate-level data from a connected network of four treatments compared in three pair-wise randomized controlled trials.
The standard errors of incremental net benefit using structured models is reduced by up to eight- or ninefold compared to the unstructured models, and the expected loss attaching to decision uncertainty by factors of several hundreds. Model averaging had considerable influence on the EVPI.
Alternative structural assumptions can alter the treatment decision and have an overwhelming effect on model uncertainty and expected value of information. Structural uncertainty can be accounted for by model averaging, and the EVPI can be calculated for averaged models.
从随机对照试验中获得的数据来估计马尔可夫模型的标准方法，要么对治疗作用于哪些转移做出判断，要么假设治疗对每个转移都有单独的作用。另一种方法是拟合一系列假设治疗作用于特定转移的模型。然后，研究人员可以使用拟合优度统计量在替代模型之间进行选择。但是，任何选定参数化的结构不确定性仍然存在，这可能对最终决策和进一步研究的需求产生影响。
我们描述了一种贝叶斯模型估计和选择方法。使用模型平均来考虑使用哪种参数化的结构不确定性，我们开发了一种在平均模型中计算完美信息期望价值（EVPI）的公式。使用 WinBUGS 中的马尔可夫链蒙特卡罗模拟或 Excel（Microsoft Corp.，Redmond，WA）中的蒙特卡罗模拟为每个成本效益参数生成边际后验分布。我们使用来自四个治疗方案的连接网络在三个两两随机对照试验中比较的汇总水平数据，用哮喘治疗的例子来说明该方法。
与非结构化模型相比，结构化模型的增量净收益的标准误差最多减少了 8 倍或 9 倍，而决策不确定性的预期损失则增加了数百倍。模型平均对 EVPI 有很大影响。
替代结构假设可以改变治疗决策，并对模型不确定性和信息价值的预期产生压倒性影响。结构不确定性可以通过模型平均来考虑，并且可以为平均模型计算 EVPI。
