Motivation Mendelian randomization methods that estimate non-linear exposure-outcome relationships typically require individual-level data. This package implements non-linear Mendelian randomization methods using stratified summarized data, facilitating analyses where individual-level data cannot easily be shared, and additionally increasing reproducibility as summarized data can be reported. Dependence on summarized data means the methods are independent of the form of the individual-level data, increasing flexibility to different outcome types (such as continuous, binary or time-to-event outcomes). Implementation SUMnlmr is available as an R package (version 3.1.0 or higher). General features The package implements the previously proposed fractional polynomial and piecewise linear methods on stratified summarized data that can either be estimated from individual-level data using the package or supplied by a collaborator. It constructs plots to visualize the estimated exposure-outcome relationship, and provides statistics to assess preference for a non-linear model over a linear model. Availability The package is freely available from GitHub [https://github.com/amymariemason/SUMnlmr].

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软件应用程序简介：SUMnlmr，一个 R 包，可促进灵活且可重复的非线性孟德尔随机化分析

估计非线性暴露-结果关系的动机孟德尔随机化方法通常需要个体水平的数据。该软件包使用分层汇总数据实施非线性孟德尔随机化方法，便于在无法轻松共享个人级别数据的情况下进行分析，并且还可以报告汇总数据，从而提高可重复性。对汇总数据的依赖意味着这些方法独立于个体级别数据的形式，从而增加了对不同结果类型（例如连续、二元或时间到事件结果）的灵活性。实现 SUMnlmr 可作为 R 包（版本 3.1.0 或更高版本）使用。一般特性 该软件包在分层汇总数据上实现了先前提出的分数多项式和分段线性方法，这些数据既可以使用该软件包从个人级别的数据中估计，也可以由合作者提供。它构建图表以可视化估计的暴露-结果关系，并提供统计数据来评估非线性模型对线性模型的偏好。可用性 该软件包可从 GitHub [https://github.com/amymariemason/SUMnlmr] 免费获得。