Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK.
National Institute for Health and Care Research (NIHR) Birmingham Biomedical Research Centre, UK.
Res Synth Methods. 2024 Nov;15(6):905-916. doi: 10.1002/jrsm.1737. Epub 2024 Jul 24.
Collecting data for an individual participant data meta-analysis (IPDMA) project can be time consuming and resource intensive and could still have insufficient power to answer the question of interest. Therefore, researchers should consider the power of their planned IPDMA before collecting IPD. Here we propose a method to estimate the power of a planned IPDMA project aiming to synthesise multiple cohort studies to investigate the (unadjusted or adjusted) effects of potential prognostic factors for a binary outcome. We consider both binary and continuous factors and provide a three-step approach to estimating the power in advance of collecting IPD, under an assumption of the true prognostic effect of each factor of interest. The first step uses routinely available (published) aggregate data for each study to approximate Fisher's information matrix and thereby estimate the anticipated variance of the unadjusted prognostic factor effect in each study. These variances are then used in step 2 to estimate the anticipated variance of the summary prognostic effect from the IPDMA. Finally, step 3 uses this variance to estimate the corresponding IPDMA power, based on a two-sided Wald test and the assumed true effect. Extensions are provided to adjust the power calculation for the presence of additional covariates correlated with the prognostic factor of interest (by using a variance inflation factor) and to allow for between-study heterogeneity in prognostic effects. An example is provided for illustration, and Stata code is supplied to enable researchers to implement the method.
为个体参与者数据荟萃分析 (IPDMA) 项目收集数据可能既耗时又耗资源，并且仍然可能没有足够的能力来回答感兴趣的问题。因此，研究人员在收集 IPD 之前应该考虑他们计划的 IPDMA 的功效。在这里，我们提出了一种方法来估计计划的 IPDMA 项目的功效，该项目旨在综合多个队列研究，以调查（未经调整或调整后的）二分类结局的潜在预后因素的效果。我们考虑了二分类和连续因素，并提供了一种三步法来预先估计收集 IPD 之前的功效，假设每个感兴趣的预后因素的真实预后效果。第一步使用每个研究中通常可用的（已发表的）汇总数据来近似 Fisher 信息矩阵，从而估计每个研究中未经调整的预后因素效果的预期方差。然后，这些方差在第二步中用于估计来自 IPDMA 的汇总预后效果的预期方差。最后，第三步使用该方差根据双侧 Wald 检验和假设的真实效果来估计相应的 IPDMA 功效。还提供了扩展，以调整功效计算，以适应与感兴趣的预后因素相关的附加协变量的存在（通过使用方差膨胀因子），并允许研究之间预后效果的异质性。提供了一个示例来说明，并且提供了 Stata 代码，以使研究人员能够实现该方法。
