Evidence synthesis for decision making 3: heterogeneity--subgroups, meta-regression, bias, and bias-adjustment.
Evidence synthesis for decision making 3: heterogeneity--subgroups, meta-regression, bias, and bias-adjustment.
复制标题
DOI：
10.1177/0272989x13485157
复制
发表时间：
2013-07
期刊：
影响因子：
--
通讯作者：
Ades AE
中科院分区：
文献类型：
--
作者：
Dias S;Sutton AJ;Welton NJ;Ades AE
在Meta分析中，研究间的异质性表明了效应修饰物的存在，并对成本效益分析和决策的结果进行了解释。由于患者群体或环境的变化而导致的治疗效果的真实可变性和与试验进行方式相关的偏差通常是有区别的。相对治疗效果的可变性威胁到审判证据的外部有效性，并限制了从结果中得出结论的能力；审判行为的不完美对内部有效性构成了威胁。我们提供了指导方法的元回归和偏差调整，在配对和网络元分析(包括间接比较)，使用说明性的例子。我们认为，在许多情况下，“新”试验中治疗效果的预测分布可能比平均效果的分布与决策更相关。研究人员在考虑它们对异质性的反应时，应该考虑真实变异性和由于偏差引起的随机变异性的相对贡献。在网络元分析中，当试验水平的效应修正协变量存在或被怀疑时，各种类型的元回归模型是可能的。我们认为，只有一个交互作用项的模型在决策环境中最有可能是有用的。提供了针对连续协变量的贝叶斯元回归和针对“基线”风险的元回归的说明性例子。带注释的WinBUGS代码在附录中列出。
In meta-analysis, between-study heterogeneity indicates the presence of effect-modifiers and has implications for the interpretation of results in cost-effectiveness analysis and decision making. A distinction is usually made between true variability in treatment effects due to variation in patient populations or settings and biases related to the way in which trials were conducted. Variability in relative treatment effects threatens the external validity of trial evidence and limits the ability to generalize from the results; imperfections in trial conduct represent threats to internal validity. We provide guidance on methods for meta-regression and bias-adjustment, in pairwise and network meta-analysis (including indirect comparisons), using illustrative examples. We argue that the predictive distribution of a treatment effect in a “new” trial may, in many cases, be more relevant to decision making than the distribution of the mean effect. Investigators should consider the relative contribution of true variability and random variation due to biases when considering their response to heterogeneity. In network meta-analyses, various types of meta-regression models are possible when trial-level effect-modifying covariates are present or suspected. We argue that a model with a single interaction term is the one most likely to be useful in a decision-making context. Illustrative examples of Bayesian meta-regression against a continuous covariate and meta-regression against “baseline” risk are provided. Annotated WinBUGS code is set out in an appendix.