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Learning About Missing Data Mechanisms in Electronic Health Records-based Research: A Survey-based Approach
Sebastien Haneuse1, Andy Bogart, Ina Jazic
1From the aHarvard T.H. Chan School of Public Health, Boston, MA; bRAND Corporation, Santa Monica, CA; and cGroup Health Research Institute, Seattle, WA.
Epidemiology (Cambridge, Mass.)
|October 21, 2015
Summary
Missing weight data in electronic health record (EHR) research is common. A survey found missingness linked to patient choices and external weight management, not attitudes towards weight or depression treatment impact.
Area of Science:
- Health Informatics
- Clinical Research Methodology
- Psychiatric Epidemiology
Background:
- Missing data in electronic health record (EHR) research poses significant bias concerns.
- Weight change is a critical factor in depression treatment outcomes.
- Understanding missingness is crucial for accurate EHR-based studies.
Purpose of the Study:
- Investigate factors contributing to missing weight data in depression treatment studies.
- Evaluate the missing-at-random assumption in EHR data.
- Inform strategies for handling missing data in clinical research.
Main Methods:
- Surveyed a stratified sample (n=1,153) of patients (n=8,345) receiving depression monotherapy.
- Used logistic regression to identify determinants of baseline and follow-up weight measurement missingness.
- Applied inverse-probability weighting to adjust for survey design and nonresponse.
Main Results:
- Missing weight data was associated with patient decisions not to be weighed, weight control activities, and self-reported baseline weight.
- Factors not routinely in EHRs significantly influenced data completeness.
- Patient attitudes and perceptions of treatment impact did not correlate with missingness.
Conclusions:
- Early investigation of missing data determinants is essential for evaluating research assumptions.
- A comprehensive strategy for addressing missingness can be applied broadly across EHR data elements.
- This approach enhances the reliability of clinical research using EHR data.
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