Calculating the power to examine treatment-covariate interactions when planning an individual participant data meta-analysis of randomized trials with a binary outcome.
Calculating the power to examine treatment-covariate interactions when planning an individual participant data meta-analysis of randomized trials with a binary outcome.
复制标题
DOI：
10.1002/sim.9538
复制
发表时间：
2022-10-30
影响因子：
2
通讯作者：
Ensor, Joie
中科院分区：
文献类型：
--
作者：
Riley, Richard D.;Hattle, Miriam;Collins, Gary S.;Whittle, Rebecca;Ensor, Joie
关键词：
在开始进行个人参与者数据Meta分析（IPDMA）项目之前，研究人员和资助者需要确保其时间和成本是值得的。这应该包括考虑有多少研究承诺他们的IPD，以及考虑到这些研究的特点，包括他们的IPDMA的力量。在这里，我们展示了如何估计随机试验的计划IPDMA的功效，以检查参与者水平上的治疗-协变量相互作用（即治疗效应修饰因子）。我们专注于二元或连续协变量的二元结果，并提出了一个三步法，假设真实的相互作用大小是所有试验所共有的。在第一步中，用户必须指定最低重要的交互作用大小，并且对于每个单独的试验（例如，从试验出版物中获得），以下汇总数据：对照组和治疗组中的受试者数量和事件数量，每个连续协变量的平均值和SD，以及每个二元协变量的每个类别中的受试者比例。这允许使用来自逻辑回归模型的Fisher信息矩阵的解析解来计算每次试验的相互作用估计的方差。第2步计算计划IPDMA的汇总交互作用估计值的方差（等于第1步中试验方差之和的倒数），第3步基于双侧Wald检验计算相应的把握度。文中给出了Stata和R代码，并给出了两个例子。还考虑了允许研究间异质性的扩展。
Before embarking on an individual participant data meta‐analysis (IPDMA) project, researchers and funders need assurance it is worth their time and cost. This should include consideration of how many studies are promising their IPD and, given the characteristics of these studies, the power of an IPDMA including them. Here, we show how to estimate the power of a planned IPDMA of randomized trials to examine treatment‐covariate interactions at the participant level (ie, treatment effect modifiers). We focus on a binary outcome with binary or continuous covariates, and propose a three‐step approach, which assumes the true interaction size is common to all trials. In step one, the user must specify a minimally important interaction size and, for each trial separately (eg, as obtained from trial publications), the following aggregate data: the number of participants and events in control and treatment groups, the mean and SD for each continuous covariate, and the proportion of participants in each category for each binary covariate. This allows the variance of the interaction estimate to be calculated for each trial, using an analytic solution for Fisher's information matrix from a logistic regression model. Step 2 calculates the variance of the summary interaction estimate from the planned IPDMA (equal to the inverse of the sum of the inverse trial variances from step 1), and step 3 calculates the corresponding power based on a two‐sided Wald test. Stata and R code are provided, and two examples given for illustration. Extension to allow for between‐study heterogeneity is also considered.
登录
查看更多内容
影响因子：
15.8
作者：
Wilson E;Free C;Morris TP;Syred J;Ahamed I;Menon-Johansson AS;Palmer MJ;Barnard S;Rezel E;Baraitser P
通讯作者：
Baraitser P
影响因子：
2
作者：
Demidenko, Eugene
通讯作者：
Demidenko, Eugene
影响因子：
105.7
作者：
Greenland, Sander;Mansournia, Mohammad Ali;Altman, Douglas G.
通讯作者：
Altman, Douglas G.
影响因子：
2
作者：
Hartung, J;Knapp, G
通讯作者：
Knapp, G
影响因子：
7.7
作者：
Thompson S;Kaptoge S;White I;Wood A;Perry P;Danesh J;Emerging Risk Factors Collaboration
通讯作者：
Emerging Risk Factors Collaboration