Centre for Biostatistics and Genetic Epidemiology, Department of Health Sciences, University of Leicester, Adrian Building, University Road, Leicester LE1 7RH, UK.
BMC Med Res Methodol. 2012 Mar 23;12:34. doi: 10.1186/1471-2288-12-34.
An Individual Patient Data (IPD) meta-analysis is often considered the gold-standard for synthesising survival data from clinical trials. An IPD meta-analysis can be achieved by either a two-stage or a one-stage approach, depending on whether the trials are analysed separately or simultaneously. A range of one-stage hierarchical Cox models have been previously proposed, but these are known to be computationally intensive and are not currently available in all standard statistical software. We describe an alternative approach using Poisson based Generalised Linear Models (GLMs).
We illustrate, through application and simulation, the Poisson approach both classically and in a Bayesian framework, in two-stage and one-stage approaches. We outline the benefits of our one-stage approach through extension to modelling treatment-covariate interactions and non-proportional hazards. Ten trials of hypertension treatment, with all-cause death the outcome of interest, are used to apply and assess the approach.
We show that the Poisson approach obtains almost identical estimates to the Cox model, is additionally computationally efficient and directly estimates the baseline hazard. Some downward bias is observed in classical estimates of the heterogeneity in the treatment effect, with improved performance from the Bayesian approach.
Our approach provides a highly flexible and computationally efficient framework, available in all standard statistical software, to the investigation of not only heterogeneity, but the presence of non-proportional hazards and treatment effect modifiers.
个体患者数据（IPD）荟萃分析通常被认为是综合临床试验生存数据的金标准。IPD 荟萃分析可以通过两阶段或一阶段方法实现，具体取决于试验是单独分析还是同时分析。先前已经提出了一系列一阶段分层 Cox 模型，但这些模型已知计算密集，并且目前并非所有标准统计软件都可用。我们描述了一种使用基于泊松的广义线性模型（GLM）的替代方法。
我们通过应用和模拟，在两阶段和一阶段方法中展示了泊松方法的经典和贝叶斯框架。我们通过扩展到治疗协变量相互作用和非比例风险模型来概述我们的一阶段方法的优势。我们使用十种高血压治疗试验，以全因死亡为感兴趣的结局，来应用和评估该方法。
我们表明泊松方法几乎可以得到与 Cox 模型相同的估计值，并且计算效率更高，并且可以直接估计基线风险。在经典的治疗效果异质性估计中观察到一些向下偏差，贝叶斯方法的性能得到了改善。
我们的方法为调查不仅异质性，而且非比例风险和治疗效果修饰剂的存在提供了一个高度灵活且计算效率高的框架，可在所有标准统计软件中使用。
