Results Summary

What was the research about?

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.

What were the results?

The research team developed the mathematical basis for creating new methods to address these problems in using EHR data. Then they developed and tested the methods. The team showed these methods could accurately measure the effect of dynamic treatment plans on patients’ health. They also created computer programs to help other researchers use the methods.

What did the research team do?

The research team used data created by computer programs to see how the methods worked. The team used the methods to analyze real EHRs from patients with diabetes. The analysis looked at how dynamic treatment plans and choices about follow-up care affect patients’ health.

The research team worked with patients with diabetes, doctors, and patient advocates to develop and test the methods with health topics that are important to patients and doctors.

What were the limits of the study?

The methods may not work if the EHRs don’t have data on certain patient traits, such as smoking or past illness, that affect treatment or follow-up choices and patients’ health.

Future research could develop statistical approaches to check how results from these methods change if data on patients’ traits are missing from EHRs.

How can people use the results?

Researchers could use the methods to give doctors and patients data about how different ways to treat and follow up with patients can affect their health.

Final Research Report

View this project's final research report.

Peer-Review Summary

Peer review of PCORI-funded research helps make sure the report presents complete, balanced, and useful information about the research. It also assesses how the project addressed PCORI’s Methodology Standards. During peer review, experts read a draft report of the research and provide comments about the report. These experts may include a scientist focused on the research topic, a specialist in research methods, a patient or caregiver, and a healthcare professional. These reviewers cannot have conflicts of interest with the study.

The peer reviewers point out where the draft report may need revision. For example, they may suggest ways to improve descriptions of the conduct of the study or to clarify the connection between results and conclusions. Sometimes, awardees revise their draft reports twice or more to address all of the reviewers’ comments. 

Peer reviewers commented, and the researchers made changes or provided responses. The comments and responses included the following:

  • The reviewers suggested moving some details from the results to the methods section. While the researchers agreed that they had described methods in the results section, they maintained that doing so was appropriate because the project aimed to develop analytic methods, and the methods described in the results are products of the project.
  • The reviewers indicated that the report did not adequately explain the proof the investigators used for the identifiability result and recommended that the authors explain their results with more detail. The researchers added a more intuitive explanation, as well as a sketch describing the proof that they used to establish the result in the report’s appendix.
  • The reviewers asked for additional details on how the researchers constructed simulations  and estimated various parameters . Also, they asked for  what specific algorithms the researchers used. The researchers said they understood that it would be helpful to have details needed to replicate the study findings, but they felt constrained by the word limit set for the report. They included specifics of simulations and data analyses in the appendix.

Conflict of Interest Disclosures

Project Information

Romain S. Neugebauer, PhD
Kaiser Foundation Research Institute
Causal Analyses of Electronic Health Record Data for Assessing the Comparative Effectiveness of Treatment Regimens

Key Dates

September 2014
July 2019

Study Registration Information


Has Results
Award Type
Health Conditions Health Conditions These are the broad terms we use to categorize our funded research studies; specific diseases or conditions are included within the appropriate larger category. Note: not all of our funded projects focus on a single disease or condition; some touch on multiple diseases or conditions, research methods, or broader health system interventions. Such projects won’t be listed by a primary disease/condition and so won’t appear if you use this filter tool to find them. View Glossary
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Last updated: March 14, 2024