DOI: 10.3886/icpsr39581.v1
title: Causal Analyses of Electronic Health Record Data for Assessing the Comparative Effectiveness of Treatment Regimens [Methods Study], United States, 2014-2019
title: Version 1

description: Patients with chronic health problems, such as diabetes, often need to change treatment plans over time to improve their health. To help with this process, doctors can monitor patients' health through follow-up clinic visits and lab tests. Doctors may also suggest changing a treatment plan in response to visits or lab test results. When a treatment plan changes in this way, it's called a dynamic treatment plan.
In this study, the research team developed and tested new statistical methods to learn how dynamic treatment plans and choices about follow-up care affect patients' health. These methods use electronic health records, or EHRs. Using EHRs is helpful because they have data on

What treatments patients have received over time
How treatments have affected patients' health
Follow-up information such as lab test results

But the data may differ for patients based on when and why they go to the doctor. These differences make it hard for researchers to accurately know the effect of dynamic treatment plans across many patients.
 To access the methods and software, please visit the simcasual R Package.
description: To develop and evaluate innovative causal inference methods that are suitable for analyzing electronic health record (EHR) data in comparative effectiveness research (CER) on dynamic treatment regimens
description: In dynamic treatment regimens, clinical management decisions evolve over time based on a patient's response to treatment and monitoring. CER on dynamic treatment regimens can use EHR data because the data include information about treatment and monitoring outcomes that vary during the course of an illness. However, statistical challenges for estimating causal effects limit wide-scale use of EHR data in CER. For example, the variation in timing and content of EHR data from patient to patient increases concerns about bias and can limit the generalizability of inferences obtained with existing statistical methods. To address these challenges, researchers can develop new methods using EHR variability to better inform treatment and monitoring decisions.
In this study, the research team developed and evaluated innovative causal estimation methods using EHR data to measure the effects of dynamic treatment regimens and monitoring regimens on health outcomes. The team derived theoretical results to construct new estimation methods from two general estimation approaches: inverse probability weighting (IPW) and targeted minimum loss-based estimation (TMLE). The new methods assume that monitoring would influence treatment decisions but would have no direct effect on the health outcome. The team evaluated the various methods developed using simulation studies and empirical analysis of real EHR data from a prior type 2 diabetes study. To simulate data and facilitate use of the methods developed in future CER, the team also developed free, publicly available software.
The research team worked with patients with diabetes, researchers, statisticians, physicians, patient advocates, and pharmacists to develop and test the methods with health topics that are important to patients and doctors.
description: Datasets: 
DS1: Causal Analyses of Electronic Health Record Data for Assessing the Comparative Effectiveness of Treatment Regimens [Methods Study], United States, 2014-2019
description: 58,000 patients with type 2 diabetes.