NDLI: Learning About Missing Data Mechanisms in Electronic Health Records-based Research: A Survey-based Approach.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Haneuse, Sebastien Bogart, Andy Jazic, Ina Westbrook, Emily O. Boudreau, Denise Theis, Mary Kay Simon, Greg E. Arterburn, David
Description	Author Affiliation: Haneuse S ( From the aHarvard T.H. Chan School of Public Health, Boston, MA)
Abstract	BACKGROUND: 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. METHODS: We identified 8,345 individuals enrolled in a large EHR-based health care system who had monotherapy treatment for depression from April 2008 to March 2010. A stratified sample of 1,153 individuals completed a detailed survey. Logistic regression was used to investigate determinants of whether a patient (1) had an opportunity to be weighed at treatment initiation (baseline), and (2) 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 nonresponse. Analyses were also conducted to investigate potential recall bias. RESULTS: Missingness at baseline and during follow-up was 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. CONCLUSION: 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.
File Format	HTM / HTML
ISSN	10443983
e-ISSN	15315487
DOI	10.1097/EDE.0000000000000393
Journal	Epidemiology
Issue Number	1
Volume Number	27
Language	English
Publisher	Lippincott Williams & Wilkins
Publisher Date	2016-01-01
Publisher Place	United States
Access Restriction	Open
Subject Keyword	Discipline Public Health Discipline Epidemiology Antidepressive Agents Adverse Effects Depression Drug Therapy Electronic Health Records Epidemiologic Research Design Weight Gain Drug Effects Weight Loss Adolescent Therapeutic Use Bias (epidemiology) Health Care Surveys Logistic Models Retrospective Studies Research Support, N.i.h., Extramural
Content Type	Text
Resource Type	Article
Subject	Epidemiology

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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