Learning About Missing Data Mechanisms in Electronic Health Records-based Research: A Survey-based Approach.
Learning About Missing Data Mechanisms in Electronic Health Records-based Research: A Survey-based Approach.
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DOI：
10.1097/ede.0000000000000393
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发表时间：
2016-01
期刊：
影响因子：
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通讯作者：
Arterburn D
中科院分区：
文献类型：
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作者：
Haneuse S;Bogart A;Jazic I;Westbrook EO;Boudreau D;Theis MK;Simon GE;Arterburn D
Bias due to missing data is a major concern in electronic health record (EHR)-based research. As part of an ongoing EHR-based study of weight change among patients treated for depression, we conducted a survey to investigate determinants of missingness in the available weight information and to evaluate the missing-at-random assumption. We identified 8,345 individuals enrolled in a large EHR-based health care system who had monotherapy treatment for depression from 04/2008-03/2010. A stratified sample of 1,153 individuals completed a detailed survey. Logistic regression was used to investigate determinants of whether or not a patient (i) had an opportunity to be weighed at treatment initiation (baseline) and (ii) had a weight measurement recorded. Parallel analyses were conducted to investigate missingness during follow-up. Throughout, inverse-probability weighting was used to adjust for the design and survey non-response. Analyses were also conducted to investigate potential recall bias. Missingness at baseline and during follow-up was significantly associated with numerous factors not routinely collected in the EHR including whether or not the patient had ever chosen not to be weighed, external weight control activities, and self-reported baseline weight. Patient attitudes about their weight and perceptions regarding the potential impact of their depression treatment on weight were not related to missingness. Adopting a comprehensive strategy to investigate missingness early in the research process gives researchers information necessary to evaluate key assumptions. While the survey presented focuses on outcome data, the overarching strategy can be applied to any and all data elements subject to missingness.
