Assessing Missing Data Assumptions in EHR-Based Studies: A Complex and Underappreciated Task-Reference-Cited by-同舟云学术

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Assessing Missing Data Assumptions in EHR-Based Studies: A Complex and Underappreciated Task

Published:2021-02-26 Issue:2 Volume:4 Page:e210184
ISSN:2574-3805
Container-title:JAMA Network Open
language:en
Short-container-title:JAMA Netw Open

Author:

Haneuse Sebastien¹²,Arterburn David³,Daniels Michael J.⁴

Affiliation:

1. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts

2. Statistical Editor, JAMA Network Open

3. Kaiser Permanente Washington Health Research Institute, Seattle

4. Department of Statistics, University of Florida, Gainesville

Publisher

American Medical Association (AMA)

Subject

General Medicine

Link

https://jamanetwork.com/journals/jamanetworkopen/articlepdf/2776905/haneuse_2021_ed_210001_1613711852.85699.pdf

Reference10 articles.

1. Distinguishing selection bias and confounding bias in comparative effectiveness research.;Haneuse;Med Care,2016

2. Association between bariatric surgery and macrovascular disease outcomes in patients with type 2 diabetes and severe obesity.;Fisher;JAMA,2018

3. Health indicator recording in UK primary care electronic health records: key implications for handling missing data.;Petersen;Clin Epidemiol,2019

4. Integration of genetic and clinical information to improve imputation of data missing from electronic health records.;Li;J Am Med Inform Assoc,2019

5. A general framework for considering selection bias in EHR-based studies: what data are observed and why?;Haneuse;EGEMS (Wash DC),2016

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