Evidence synthesis for decision making 5: the baseline natural history model.
Evidence synthesis for decision making 5: the baseline natural history model.
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DOI：
10.1177/0272989x13485155
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发表时间：
2013-07
期刊：
影响因子：
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通讯作者：
Ades AE
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文献类型：
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作者：
Dias S;Welton NJ;Sutton AJ;Ades AE
大多数成本效益分析包括一个基线模型和一个相对治疗效果模型，前者代表比较组中标准治疗下的绝对自然历史。我们回顾了在基线自然历史模型的构建中出现的综合问题。我们涵盖了结果测量对治疗的绝对反应，其中比较有效性是定义的，以及自然历史模型的其他元素，通常是试验中报告的短期效果的“下游”。我们建议对“标准治疗”或安慰剂比较剂的绝对效果进行建模的框架与对相对治疗效果进行综合的框架相同，并且基线模型的构建独立于相对治疗效果模型，以确保后者不受对基线的假设的影响。然而，当证据非常稀少或当其他研究或研究设计提供强有力的理由相信特定基线模型时，同时对基线和治疗效果进行建模可能具有一些优势。应该使用预测分布，而不是固定效应或随机效应均值来表示基线，以反映观察到的基线率变化。在可能的情况下，建议根据试验数据或试验和观测数据的组合对多个基线结果进行联合建模，因为这可能更好地利用现有证据，产生更可靠的结果，并确保模型内部一致。
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.