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Approaches to Handling Partially Observed Confounder Data From Electronic Health Records (EHR) In Non-randomized Studies of Medication Outcomes

Basic Details
Date Posted
Status
Complete
Description

This project aims to systematically investigate approaches to detect underlying missingness mechanisms, compare imputation approaches and showcase sensitivity analyses to build confidence in pharmacoepidemiological analyses with partially observed confounder variables. Special emphasis will be given to informative missingness in the context of studying causal treatment effects in Electronic Health Records (EHR) and EHR-linked databases. The overall goal of this proposal is to develop standardized “toolkits” that can be readily implemented in EHRs to describe and, when assumptions permit, address missingness in confounding variables.

Related Links
Related Link(s)
Sentinel Innovation Center
Publication: smdi: An R Package to Perform Structural Missing Data Investigations on Partially Observed Confounders in Real-world Evidence Studies
Presentation: Principled Approaches to Handle Partially Observed Confounder Data from Electronic Health Records: A Plasmode Simulation Study
Presentation: High-Dimensional Multiple Imputation (hdMI) Strategies for Partially Observed Confounders with Structured and Natural Language Processing-Derived Auxiliary Covariates
Manuscript: Analyzing Missingness Patterns in Real-World Data Using the SMDI Toolkit: Application to a Linked EHR-Claims Pharmacoepidemiology Study
Workgroup Leader(s)

Rishi Desai, MS, PhD; Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA

Workgroup Member(s)

Janick Weberpals, PhD; Robert Glynn, PhD, ScD; Luke Zabotka, BA; Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA

Sudha Raman, PhD; Brad Hammill, PhD; Department of Population Health Sciences, Duke University of School of Medicine, Raleigh, NC

Elizabeth Messenger-Jones, MSPH; Darren Toh, ScD; John Connolly, ScM, ScD; Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, MA 

Pamela Shaw, PhD, MS; Kaiser Permanente Washington Health Research Institute, Seattle, WA

Fang Tian, MPH, MHS, PhD; Wei Liu, PhD; Jenni Li, PhD; Jose Hernandez, RPh, MPH, MSc, PhD; Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD

Hana Lee, MS, PhD; Office of Biostatistics, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD