Sutton A J, Kendrick D, Coupland C A C
Department of Health Sciences, University of Leicester, Leicester, UK.
Stat Med. 2008 Feb 28;27(5):651-69. doi: 10.1002/sim.2916.
The methodology described here was developed for a systematic review and individual participant-level meta-analysis of home safety education and the provision of safety equipment for the prevention of childhood accidents. This review had a particular emphasis on exploring whether effectiveness was related to socio-demographic characteristics previously shown to be associated with injury risk. Individual participant data were only made available to us for a proportion of the included studies. This resulted in the need for developing a new methodology to combine the available data most efficiently. Our objective was to develop a (random effects) meta-analysis model that could synthesize both individual-level and aggregate-level binary outcome data while exploring the effects of binary covariates also available in a combination of individual participant and aggregate level data. To add further complication, the studies to be combined were a mixture of cluster and individual participant-allocated designs.A Bayesian model using Markov chain Monte Carlo methods to estimate parameters is described which efficiently synthesizes the data by allowing different models to be fitted to the different study design and data format combinations available. Initially we describe a model to estimate mean effects ignoring the influence of the covariates, and then extend it to include a binary covariate. The application of the method is illustrated by application to one outcome from the motivating home safety meta-analysis for illustration. Using the same general approach, it would be possible to develop further 'tailor made' evidence synthesis models to synthesize all available evidence most effectively.
此处描述的方法是为一项关于家庭安全教育及提供安全设备以预防儿童事故的系统评价和个体参与者水平的荟萃分析而开发的。该评价特别强调探讨有效性是否与先前显示与伤害风险相关的社会人口学特征有关。仅一部分纳入研究向我们提供了个体参与者数据。这就需要开发一种新方法来最有效地合并现有数据。我们的目标是开发一种（随机效应）荟萃分析模型，该模型可以综合个体水平和总体水平的二元结局数据，同时探索个体参与者和总体水平数据组合中也存在的二元协变量的影响。更复杂的是，要合并的研究包括整群分配设计和个体参与者分配设计。本文描述了一种使用马尔可夫链蒙特卡罗方法估计参数的贝叶斯模型，该模型通过允许对不同的研究设计和可用数据格式组合拟合不同模型来有效地综合数据。最初，我们描述一个忽略协变量影响来估计平均效应的模型，然后将其扩展以纳入一个二元协变量。通过将该方法应用于家庭安全激励荟萃分析的一个结局进行说明。使用相同的一般方法，有可能开发进一步的“量身定制”的证据综合模型，以最有效地综合所有可用证据。
