标题： 快速且鲁棒的不变广义线性模型 显示英文标题

标题： Fast and robust invariant generalized linear models

摘要： 统计整合多种数据源是构建可推广的预测工具的重要步骤，尤其是在精准健康领域。 不变特征模型是一种多源数据整合的新范式，它假设少量协变量在所有可能的环境中对结果产生相同的影响。 现有估计不变效应的方法存在巨大的计算成本，或仅在严格假设下才能提供良好的统计性能。 在本工作中，我们提供了一个在不变特征模型下进行估计的通用框架，该框架计算效率高且统计灵活性强。 我们还提供了我们所提出方法的鲁棒扩展，以防止可能被破坏或错误指定的数据源。 我们通过模拟展示了我们方法的鲁棒性，并利用All of Us研究计划的电子健康记录建立了一个可转移的终末期肾病预测模型。 显示英文摘要

摘要： Statistical integration of diverse data sources is an essential step in the building of generalizable prediction tools, especially in precision health. The invariant features model is a new paradigm for multi-source data integration which posits that a small number of covariates affect the outcome identically across all possible environments. Existing methods for estimating invariant effects suffer from immense computational costs or only offer good statistical performance under strict assumptions. In this work, we provide a general framework for estimation under the invariant features model that is computationally efficient and statistically flexible. We also provide a robust extension of our proposed method to protect against possibly corrupted or misspecified data sources. We demonstrate the robust properties of our method via simulations, and use it to build a transferable prediction model for end stage renal disease using electronic health records from the All of Us research program.