Meta‐analysis of individual patient data (IPD) is increasingly used to synthesize data from multiple trials. IPD meta‐analysis offers several advantages over meta‐analyzing aggregate data, including the capacity to individualize treatment recommendations. Trials usually collect information on many patient characteristics. Some of these covariates may strongly interact with treatment (and thus be associated with treatment effect modification) while others may have little effect. It is currently unclear whether a systematic approach to the selection of treatment‐covariate interactions in an IPD meta‐analysis can lead to better estimates of patient‐specific treatment effects. We aimed to answer this question by comparing in simulations the standard approach to IPD meta‐analysis (no variable selection, all treatment‐covariate interactions included in the model) with six alternative methods: stepwise regression, and five regression methods that perform shrinkage on treatment‐covariate interactions, that is, least absolute shrinkage and selection operator (LASSO), ridge, adaptive LASSO, Bayesian LASSO, and stochastic search variable selection. Exploring a range of scenarios, we found that shrinkage methods performed well for both continuous and dichotomous outcomes, for a variety of settings. In most scenarios, these methods gave lower mean squared error of the patient‐specific treatment effect as compared with the standard approach and stepwise regression. We illustrate the application of these methods in two datasets from cardiology and psychiatry. We recommend that future IPD meta‐analysis that aim to estimate patient‐specific treatment effects using multiple effect modifiers should use shrinkage methods, whereas stepwise regression should be avoided.

中文翻译：

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比较在个体患者数据荟萃分析中估计患者特异性治疗效果的方法

个体患者数据 (IPD) 的 Meta 分析越来越多地用于综合多个试验的数据。与荟萃分析汇总数据相比，IPD 荟萃分析具有多项优势，包括个性化治疗建议的能力。试验通常会收集有关许多患者特征的信息。这些协变量中的一些可能与治疗强烈相互作用（因此与治疗效果修改相关），而其他协变量可能几乎没有影响。目前尚不清楚在 IPD 荟萃分析中选择治疗协变量相互作用的系统方法是否可以更好地估计患者特定的治疗效果。我们旨在通过模拟比较 IPD 荟萃分析的标准方法（无变量选择，模型中包含的所有治疗协变量相互作用）和六种替代方法：逐步回归和五种对治疗协变量相互作用进行收缩的回归方法，即最小绝对收缩和选择算子 (LASSO)、岭、自适应 LASSO、贝叶斯LASSO 和随机搜索变量选择。探索一系列场景后，我们发现收缩方法对于各种设置的连续和二分结果都表现良好。在大多数情况下，与标准方法和逐步回归相比，这些方法给出的患者特异性治疗效果的均方误差较低。我们在心脏病学和精神病学的两个数据集中说明了这些方法的应用。