描述和分析电子健康记录中部分观测到的混杂因素数据的原则性方法

Clinical Epidemiology Pub Date : 2024-05-01 DOI:10.2147/clep.s436131

Janick Weberpals, Sudha Raman, Pamela A. Shaw, Hana Lee, Massimiliano Russo, Bradley G. Hammill, S. Toh, John G. Connolly, Kimberly Dandreo, Fang Tian, Wei Liu, Jie Li, José J. Hernández-Muñoz, Robert J. Glynn, R. Desai

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引用次数: 2

摘要

目的：部分观测到的混杂因素数据给电子健康记录（EHR）的统计分析带来了挑战，而对潜在的潜在缺失机制缺乏系统的评估。我们旨在提供一种原则性方法，从经验上描述缺失数据的过程，并研究分析方法的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A Principled Approach to Characterize and Analyze Partially Observed Confounder Data from Electronic Health Records

Objective: Partially observed confounder data pose challenges to the statistical analysis of electronic health records (EHR) and systematic assessments of potentially underlying missingness mechanisms are lacking. We aimed to provide a principled approach to empirically characterize missing data processes and investigate performance of analytic methods

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Clinical Epidemiology

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