School of Social and Community Medicine, University of Bristol, Bristol, UK (SD, NJW, AEA)
Department of Health Sciences, University of Leicester, Leicester, UK (AJS)
Med Decis Making. 2013 Jul;33(5):657-70. doi: 10.1177/0272989X13485155.
Most cost-effectiveness analyses consist of a baseline model that represents the absolute natural history under a standard treatment in a comparator set and a model for relative treatment effects. We review synthesis issues that arise on the construction of the baseline natural history model. We cover both the absolute response to treatment on the outcome measures on which comparative effectiveness is defined and the other elements of the natural history model, usually "downstream" of the shorter-term effects reported in trials. We recommend that the same framework be used to model the absolute effects of a "standard treatment" or placebo comparator as that used for synthesis of relative treatment effects and that the baseline model is constructed independently from the model for relative treatment effects, to ensure that the latter are not affected by assumptions made about the baseline. However, simultaneous modeling of baseline and treatment effects could have some advantages when evidence is very sparse or when other research or study designs give strong reasons for believing in a particular baseline model. The predictive distribution, rather than the fixed effect or random effects mean, should be used to represent the baseline to reflect the observed variation in baseline rates. Joint modeling of multiple baseline outcomes based on data from trials or combinations of trial and observational data is recommended where possible, as this is likely to make better use of available evidence, produce more robust results, and ensure that the model is internally coherent.
大多数成本效益分析都包含一个基线模型,该模型代表在标准治疗下比较组中的绝对自然史,以及相对治疗效果的模型。我们回顾了在构建基线自然史模型时出现的综合问题。我们涵盖了对定义比较有效性的结果测量上的治疗绝对反应,以及自然史模型的其他要素,通常是临床试验中报告的短期效应之后的“下游”。我们建议,用于合成相对治疗效果的相同框架用于对“标准治疗”或安慰剂对照的绝对效果进行建模,并且基线模型独立于相对治疗效果模型构建,以确保后者不受关于基线的假设的影响。但是,当证据非常稀少,或者其他研究或研究设计有充分理由相信特定的基线模型时,同时对基线和治疗效果进行建模可能会有一些优势。建议使用预测分布而不是固定效应或随机效应均值来表示基线,以反映基线率的观察到的变化。如果可能,应推荐基于试验数据或试验和观察数据组合的多个基线结果的联合建模,因为这可能会更好地利用现有证据,产生更稳健的结果,并确保模型具有内在一致性。