Centre for Prognosis Research, Research Institute for Primary Care and Health Sciences, Keele University, Keele, Staffordshire, ST5 5BG, UK.
Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
BMC Med Res Methodol. 2018 May 18;18(1):41. doi: 10.1186/s12874-018-0492-z.
Researchers and funders should consider the statistical power of planned Individual Participant Data (IPD) meta-analysis projects, as they are often time-consuming and costly. We propose simulation-based power calculations utilising a two-stage framework, and illustrate the approach for a planned IPD meta-analysis of randomised trials with continuous outcomes where the aim is to identify treatment-covariate interactions.
The simulation approach has four steps: (i) specify an underlying (data generating) statistical model for trials in the IPD meta-analysis; (ii) use readily available information (e.g. from publications) and prior knowledge (e.g. number of studies promising IPD) to specify model parameter values (e.g. control group mean, intervention effect, treatment-covariate interaction); (iii) simulate an IPD meta-analysis dataset of a particular size from the model, and apply a two-stage IPD meta-analysis to obtain the summary estimate of interest (e.g. interaction effect) and its associated p-value; (iv) repeat the previous step (e.g. thousands of times), then estimate the power to detect a genuine effect by the proportion of summary estimates with a significant p-value.
In a planned IPD meta-analysis of lifestyle interventions to reduce weight gain in pregnancy, 14 trials (1183 patients) promised their IPD to examine a treatment-BMI interaction (i.e. whether baseline BMI modifies intervention effect on weight gain). Using our simulation-based approach, a two-stage IPD meta-analysis has < 60% power to detect a reduction of 1 kg weight gain for a 10-unit increase in BMI. Additional IPD from ten other published trials (containing 1761 patients) would improve power to over 80%, but only if a fixed-effect meta-analysis was appropriate. Pre-specified adjustment for prognostic factors would increase power further. Incorrect dichotomisation of BMI would reduce power by over 20%, similar to immediately throwing away IPD from ten trials.
Simulation-based power calculations could inform the planning and funding of IPD projects, and should be used routinely.
研究人员和资助者应考虑计划中的个体参与者数据（IPD）荟萃分析项目的统计功效，因为这些项目通常既耗时又昂贵。我们提出了一种基于模拟的功效计算方法，该方法利用两阶段框架，并举例说明了一项针对具有连续结局的随机试验的计划 IPD 荟萃分析，其目的是确定治疗协变量交互作用。
模拟方法有四个步骤：（i）为 IPD 荟萃分析中的试验指定一个基本的（数据生成）统计模型；（ii）利用可获得的信息（例如来自出版物）和先验知识（例如承诺提供 IPD 的研究数量）来指定模型参数值（例如对照组均值、干预效果、治疗协变量交互作用）；（iii）从模型中模拟一个特定大小的 IPD 荟萃分析数据集，并应用两阶段 IPD 荟萃分析来获得感兴趣的汇总估计量（例如交互作用效应）及其相关的 p 值；（iv）重复上一步骤（例如数千次），然后通过具有显著 p 值的汇总估计量的比例来估计检测真实效应的功效。
在一项计划中的 IPD 荟萃分析中，生活方式干预措施旨在减少妊娠体重增加，有 14 项试验（1183 名患者）承诺提供其 IPD，以检验治疗-BMI 交互作用（即基线 BMI 是否改变了干预对体重增加的影响）。使用我们的基于模拟的方法，两阶段 IPD 荟萃分析检测 BMI 每增加 10 个单位体重增加减少 1 公斤的功效 <60%。来自另外 10 项已发表试验（包含 1761 名患者）的额外 IPD 将提高功效至 80%以上，但前提是适当应用固定效应荟萃分析。预先指定对预后因素的调整将进一步提高功效。BMI 的不正确二分法将使功效降低 20%以上，与立即丢弃 10 项试验的 IPD 类似。
基于模拟的功效计算可以为 IPD 项目的规划和资助提供信息，应常规使用。
