Electronic health record (EHR)-linked biobank data hold tremendous promise for large-scale discoveries via genome-wide association study (GWAS) on diverse phenotypic traits and biomarkers routinely captured in the EHR. However, heterogeneous missingness in biomarkers compromises the validity and efficiency of statistical analyses. Prediction-based (PB) inference methods meet this challenge by using external machine learning (ML) predictions to impute missing biomarker outcomes, thereby improving statistical power and estimation accuracy in association analyses. Yet, their suitability remains unclear when outcomes are subject to clinically informative observation processes, that is, when laboratory tests are ordered based on both measured and unmeasured patient- and health system-level characteristics. In this paper, we review the statistical underpinnings of popular PB methods and then evaluate nine methods, including four PB methods and five traditional missing-data approaches, under an encompassing set of outcome observation processes for continuous and binary outcomes. PB methods can substantially improve statistical power and estimation efficiency when the missing-data mechanism is correctly specified. Under misspecification, however, these gains require both conditional independence between the covariates of interest and the missingness mechanism and independence between imputation error and the missingness mechanism. Using All of Us (AoU) data, we perform GWAS of six laboratory biomarkers and demonstrate that PB methods can replicate known genetic associations while improving efficiency relative to (weighted) complete-case analysis (CCA). Their performance in replicating existing GWAS results in AoU also depends on imputation quality and the underlying missingness mechanism.

翻译：电子健康记录关联生物库数据通过全基因组关联研究有望实现大规模发现，其研究对象涵盖电子健康记录中常规收集的多种表型特征与生物标志物。然而，生物标志物的异质性缺失会损害统计分析的效度与效率。基于预测的推断方法通过利用外部机器学习预测来填补缺失的生物标志物结局，从而提升关联分析中的统计功效与估计精度，以此应对这一挑战。但当结局受临床信息性观测过程影响时（即实验室检测的决策同时基于可测量和不可测量的患者及卫生系统层面特征），此类方法的适用性仍不明确。本文首先梳理了主流基于预测方法的统计学原理，继而针对连续型与二分类结局，在涵盖多种结局观测过程的统一框架下评估了九种方法（含四种基于预测方法与五种传统缺失数据处理方法）。当缺失数据机制被正确设定时，基于预测方法可显著提升统计功效与估计效率；但在模型设定错误的情况下，这些增益需要目标协变量与缺失机制的条件独立性，以及插补误差与缺失机制的独立性共同作为前提。我们利用"我们所有人"项目数据对六项实验室生物标志物进行全基因组关联研究，结果表明：相较于（加权）完整病例分析，基于预测方法在复现已知遗传关联的同时能提升分析效率。其在All of Us数据中复现现有全基因组关联研究结果的表现，还取决于插补质量与潜在缺失数据机制。