DOI: 10.3886/icpsr39483.v1
title: Patient-Centered Enrollment in Comparative Effectiveness Trials: Mathematical Equipoise [Methods Study], Massachusetts, 2013-2018
title: Version 1

description: Comparative effectiveness research compares two or more treatments to see which one works better for certain patients. This research may include randomized controlled trials, or RCTs, in which researchers assign patients to one of the treatments by chance.
A patient may enroll in an RCT when, based on current knowledge of that patient's traits, the treatments being tested have about the same chance of helping. If one treatment is known to have a better chance of helping a patient, then the patient would not enroll and would receive that treatment from the doctor.
Sometimes there isn't enough research to show if one treatment has a better chance of helping than another. In this case, researchers may use computer programs. The programs estimate how well different treatments work in patients with certain traits. For example, a person's age and pain level may affect how much a treatment helps.
These programs would be useful for patients with knee osteoarthritis. Not many RCTs have compared total knee replacement surgery with other treatments such as medicine or physical therapy.
In this study, the research team made a computer program for patients with knee osteoarthritis. It uses data from electronic health records. The program could help identify patients for whom 

The treatments in the study have about the same chance of helping. These patients may wish to take part in an RCT. 
A certain treatment may help more than another. These patients could choose that treatment. 

The research team also made an online system based on the program for patients and doctors to use during a visit. Doctors can use the results from the system to talk with patients about treatment. If appropriate, they could talk about taking part in an RCT.
description: This project aimed to use mathematical equipoise for making patient-specific comparisons of alternative treatment outcomes of TKR versus nonsurgical treatment of knee osteoarthritis as a way to consider enrollment into a comparative effectiveness RCT. The study aims were: (1) To use nonrandomized data from patients with knee osteoarthritis to create models that predict patient-specific outcomes of different treatment options; (2) To use the models to develop software to help identify patients who may be eligible to enroll in randomized controlled trials (RCTs).
description: When conducting RCTs to compare treatments, researchers must recruit only patients with clinical equipoise, that is, patients for whom insufficient evidence exists to favor one treatment over another. When limited prior RCT evidence is available to identify patients with clinical equipoise, researchers can apply mathematical models to clinical registries, electronic health records (EHRs), and other non-RCT data to predict patient-specific outcomes of the treatments under study. If predicted outcomes are similar across treatments, called mathematical equipoise, random treatment assignment may be appropriate, and patients may wish to consider participating in an RCT.
For patients with knee osteoarthritis, the choice between total knee replacement and nonsurgical treatment is an important clinical question for which there are few RCTs. Nonsurgical treatment may include medication and/or physical therapy. In this study, the researchers developed Knee Osteoarthritis Mathematical Equipoise Tool (KOMET) software, with accompanying clinician and patient web-based interfaces, for use in EHR systems to 


Help identify patients with mathematical equipoise who could consider enrolling in RCTs
Support decision making by providing patients with individualized, predicted outcomes for treatment options
To develop KOMET, the researchers used nonrandomized data from four databases to match total-knee-replacement knees to similar nonsurgical-treatment knees. The researchers then developed models to predict one-year outcomes for knee pain and functional status, modeling each outcome separately. Analysis consisted of three rounds of predictive modeling based on estimation and testing using data from various combinations of the four available databases. After identifying optimal prediction models for each outcome, the researchers programmed associated algorithms into the KOMET software.
During the study, knee osteoarthritis researchers, patients, clinicians, and patient advocates provided input on study questions, modeling issues, outcomes, and user interface development.
description: Datasets: 
DS1: Patient-Centered Enrollment in Comparative Effectiveness Trials: Mathematical Equipoise [Methods Study], Massachusetts, 2013-2018
description: Consolidated database of 1,452 patient knees, half treated with a total knee replacement and the other half with non-surgical treatment using 4 databases. .