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Researchers can use experiments to learn about what patients prefer. Discrete choice experiments, or DCEs, describe treatments with different features, such as out-of-pocket costs or wait times. Patients fill out surveys about which treatments they prefer. From their choices, researchers learn what is most important to patients and how they think about the different features.

DCEs can be hard to design and analyze. When surveys are complex, patients may ignore information or take shortcuts, which leads to inaccurate results.

To make DCE results more accurate, researchers can

• Change the design of the DCE
• Apply statistical methods

But current knowledge of how to do this is limited. In this project, the research team looked at improving methods to design and analyze DCEs.

Electronic health records, or EHRs, have data on patient traits, health problems, and treatments. Researchers can use EHR data to study how treatments work or which patient traits affect health outcomes. But EHR data can have errors.

The best way to get accurate EHR data is to closely review patients' original records. But reviewing all patient records isn't possible when many patients are in a study. In such cases, researchers can review and correct records for a few patients and use the revised records to adjust data for all patients. But existing methods for using revised records don't address some kinds of errors, such as errors that are related. For example, errors in a treatment starting date can lead to mistakes in the data on length of treatment.

In this project, the research team created and tested new methods to improve the accuracy of EHR data. The new methods corrected records from some patients. Then the team used the corrections to address related errors for all patients.

To access the methods and software, please visit the MeasurementErrorMethods GitHub repository.

Clinical practice guidelines help doctors decide on treatments to recommend for their patients. Guidelines are based on research that looks at the benefits and harms of different treatments. Patient and caregiver input can improve the usefulness of guidelines. But guideline developers often rely on the input of only a few patients and caregivers.

In this study, the research team created a process for getting feedback on guidelines from larger groups of patients and caregivers. This process is called the RAND/PPMD Patient-Centeredness Method, or RPM. The team tested RPM with guidelines for Duchenne muscular dystrophy, or DMD. DMD is a severe form of muscle loss that mostly affects young boys.

Patient-reported outcome measures, or PROMs, are surveys that ask patients how they feel and what activities they can do. All PROMs have measurement properties. These properties help researchers understand how well the PROM was designed. For example, one property is the ability to get consistent responses over time. Another is how accurately the PROM measures how patients feel about a health problem. But researchers may not consistently report these properties in studies about PROMs.

In this study, the research team created guidance about reporting measurement properties in studies on PROM.

Patients may respond differently to the same treatment due to individual traits such as age or gender. Knowing how different traits can affect a patient's response to treatment can help doctors and patients make better treatment decisions. For example, this information can help doctors know what types of cancer medicines work better for certain patients. This project focuses on improving the methods that researchers use to compare how treatments work for different patients.

In this project, the research team developed and tested a statistical method called random forests, or RF. RF is a way to analyze data using a technique called machine learning. In machine learning, computers use data to learn how to perform different tasks with little or no human input. Many types of RF methods exist. The team compared multiple RF methods to learn how well the methods would work to find out how patients with different traits respond to the same treatment.

To access the R package, please visit the randomForestSRC CRAN webpage.

Organizations that fund research often seek input on which topics are important to study. Patients can take part in setting priorities for research to help make sure that future studies focus on the topics that matter most to them. Researchers can collect patients' views on which research topics are most important in different ways.

In this study, the research team compared three methods of research priority setting:

• Online crowd voting, where patients submit, discuss, and vote on ideas online
• Focus groups with nominal group technique, where patients come up with ideas on their own and then discuss those ideas in a group with a moderator
• Modified Delphi method, where patients give input through a series of mailed surveys

The research team looked at research topic rankings and experiences with each method among patients with low back pain.

In this project, the study team created a method of setting research agendas called Stakeholder Engagement in quEstion Development, or the SEED (Stakeholder Engagement in Question Development) Method. These agendas help identify research topics and questions that are important to study. The team tested the SEED Method at two sites in Virginia. The team wanted to learn if the method developed research agendas that reflected differing views and if people were satisfied with the process.

Using the SEED Method, the study team gathered input from three sets of people at each site:

• Research Team. Community members and staff from local colleges who worked with the study team to lead the project
• Topic Groups. Three groups of people who developed research questions. Each group had people with a type of viewpoint, such as patients, health professionals, or research funders. The groups worked independently with the Research Team.
• Stakeholder Consultants. Patients and health professionals with knowledge of the research topic who took part in interviews and group discussions

To access the methods please visit the The SEED Method for Stakeholder Engagement website.

If patients don't take medicines as directed, the medicines don't work as well for treating a health problem. It may also lead to more health problems. If doctors knew which patients were less likely to take medicines as directed, they could find ways to help these patients.

In this study, the research team wanted to learn if knowing who took medicines as directed in the past would predict if patients take a new medicine as directed. The team created two statistical models to predict if patients would take a medicine as directed. First, the research team created a model to predict if patients would take medicines to lower cholesterol. Then, they created a second model using data from these patients plus others who were taking medicines to lower blood pressure or strengthen bones.

Patients may respond differently to the same treatment due to differences in personal traits such as age, gender, or the number and type of health problems they have. Researchers use statistical methods to predict how well a treatment may work for patients based on their personal traits. But current methods may not work well if patients have many health problems or are taking other medicines.

In this project, the research team created new methods to figure out which patient traits are related to treatment benefits to help doctors and patients understand the likely treatment benefits for individual patients.

To access the methods, software, and R package, please visit the personalized CRAN webpage and personalized GitHub

To develop a patient engagement translation table (PETT) to give researchers guidance about which engagement methods are suitable for different steps of a research study. The study had 4 main objectives:

1. Developing a PETT that maps patient engagement methods to the 10-Step PCOR Continuum Framework for Patient and Stakeholder Engagement
2. Assessing the time, staff, and financial resources required for planning and executing patient engagement activities for each method and for special considerations for underrepresented patient groups
3. Highlighting patient engagement methods that enhance engagement of underrepresented patient groups
4. Developing a collaborative strategy for dissemination with PCORI and other stakeholders

For research studies, researchers can use data about patients' health and treatments from electronic health records, or EHRs. They may also collect self-reported data directly from patients. But a patient's EHR and self-reported data may not always agree. For example, differences may exist between the medicines that patients report taking and the medicines listed in their EHRs. Researchers don't know which of these two data sources is the most accurate.

In this project, the research team looked at EHR and self-reported data to learn which data source was more accurate.

Comparative effectiveness research compares two or more treatments to see which one works better for which patients. Electronic healthcare data are useful for this type of research. These data come from medical records and insurance claims. The data include information about how well patients respond to treatments. But many things--not just treatments--affect whether a patient's health improves.

How well a patient responds to a treatment may depend on the patient's age or what medicines the patient takes. It could also depend on what other health problems a patient has and how severe those problems are. Or a doctor may suggest one treatment instead of another because of a patient's personal situation and health. Researchers need ways to determine whether changes in a patient's health result from a certain treatment or something else.

Different statistical methods help researchers account for the various things that can affect treatment results. But researchers don't know which methods work best. This study compared several methods. The team looked at how well the methods worked to predict patients' responses to treatment, taking into account their personal situations and health. The team then created a computer program to help researchers use the methods.

To access the methods and software, please visit the Hdps GitHub and TargetedLearning GitHub.

The BRITE study (Brain Injury Rehabilitation: Improving the Transition Experience) was a six-center, 1:1 randomized controlled pragmatic trial with masked outcome assessment that compared the effectiveness of two established approaches to managing transition from inpatient rehabilitation facility discharge to the next phase of care for individuals with moderate-to-severe traumatic brain injury (TBI). The two established transition methods were (1) a standardized version of existing discharge procedures used at all six sites and (2) a standardized remotely-delivered case management approach that extended beyond the point of discharge, based on the protocol used within the Veteran's Health Administration and enhanced with input from patient and family stakeholders. The sample was stratified by site and discharge location (skilled nursing facility vs. discharge to home/community) based on the relatively lower frequency of discharge to facility (22 percent across all six study sites in 2015) and the expectation of high impact of discharge destination on outcomes. When a caregiver was available for an enrolled patient, they were also approached for consent to be surveyed, with some patients having up to two caregivers enrolled to account for changes in primary caregiver.

The following key outcome domains were assessed: (1) ability of patients to participate in the home and community as independently as possible, (2) health-related quality of life, (3) access to appropriate healthcare and reduced emergent or urgent healthcare, and (4) caregiver outcomes. These outcomes were assessed at 3, 6, 9 and 12 months after discharge from inpatient care. Participants were also given the standard TBI Model Systems follow-up assessment one-year post-injury. Types of medical insurance coverage and satisfaction with healthcare were examined at 6 and 12 months post-discharge.

Dementia is a health problem that can cause memory loss, trouble doing daily tasks, and behavioral problems. Sometimes patients with dementia and their caregivers have goals for patients that their usual medical care doesn't include. For example, caregivers may want to maintain patients' social activities in the community. They may also want patients to continue to live at home.

The research team wanted to test a process called Goal Attainment Scaling, or GAS, to see if patients and their caregivers could identify, set, and meet goals that were important to them. The team also wanted to see if GAS was valuable to patients and their caregivers.

During the COVID-19 pandemic, telemedicine emerged as the primary method of providing outpatient care in many regions with shelter-in-place and social distancing policies. This study aimed understand the impact of this rapid and widespread transition from in-person to remote visits on disparities in access to primary care, especially in chronic disease where ongoing communication between providers and patients is essential.

The newly developed or expanded telemedicine programs varied widely, raising questions about the effect of these differences on uptake of telemedicine among different patient populations and on patient-centered outcomes. Leveraging a natural experiment approach, this study examined rapidly changing telemedicine and in-person models of care during and after the COVID-19 crisis to determine whether certain patients could safely choose to continue telemedicine or telemedicine-supplemented care, rather than return to in-person care.

For people with serious chronic conditions, healthcare that defaults to all available treatments without considering patient preferences risks harms that may exceed benefits. Advance care planning (ACP) has the potential to align healthcare with what is important to patients and maximize quality of life. While primary care is where most people receive most of their care, engaging patients in ACP is not routine in primary care given competing demands and limited resources. Primary care clinicians, patients, and families agree that it is preferred to make plans before there is a medical crisis. The research team's goal was to make ACP routine in primary care and to "move it upstream" so that it included improving the quality of the last years of life as well as respecting wishes for end of life care.

This study included a comparative effectiveness trial of team-based versus individual clinician-focused ACP in primary care practices. The research team adapted Ariadne Labs' Serious Illness Care Program (SICP) and aimed to determine if a team approach produces better patient outcomes and explore factors influencing implementation of ACP across practices.

Seven practice-based research networks (PBRNs) in the United States and Canada randomized their primary care practices to team-based or individual clinician-focused versions of SICP. Team members and clinicians completed training, and implementation was supported through practice facilitation. Consented patient participants completed a baseline survey after initial conversations and follow-up surveys at 6 and 12 months later. Forty practices (21 team, 19 clinician) completed training and referred patients to the study. Half of the practices were rural, 80 percent were family medicine, and 33 percent were medical residency training sites. 535 healthcare staff completed training. Both arms trained primary care providers; the team arm also trained nurses, medical assistants, and other roles. 1,321 patients and care partners were referred; and 917 consented and were enrolled (455 from team practices, 462 from clinician). Data from 802 patients were included in the primary analyses. Qualitative implementation data was collected during practice facilitation and from practice interviews.

This collection includes quantitative data collected from primary care practices (DS1) and team members and clinicians (DS2) from study sites located in the United States.

The main objective of this pragmatic randomized clinical trial (PCT) is to identify the optimal dose of aspirin for secondary prevention in atherosclerotic cardiovascular disease (ASCVD). A total of 15,076 high-risk patients with a history of a myocardial infarction (MI) or documented ASCVD were randomized in a 1:1 ratio to receive 81 mgs versus 325 mgs of aspirin every day. This trial addressed the following specific aims:

1. To compare the effectiveness of two doses of aspirin (81 mg and 325 mg) in reducing a composite of all-cause mortality and hospitalization for nonfatal MI, or nonfatal stroke, and the primary safety endpoint of major bleeding. Secondary endpoints include the components of the primary endpoint and hospitalization for transient ischemic attack, unstable angina, or coronary revascularization procedures.
2. To compare the effects of aspirin in selected subgroups of patients by sex, age, race, Internet users vs. non-users, and those with diabetes or advanced chronic kidney disease (CKD).
3. To develop and refine the infrastructure for PCORnet to conduct multiple comparative effectiveness trials in the future.
4. To explore biological mediators of heterogeneity of response to aspirin and of impact on clinical events.

The primary aim of this randomized clinical trial is to compare the effectiveness of the Specific Carbohydrate Diet (SCD) and the Mediterranean style diet (MSD) in inducing symptomatic and clinical remission in patients with Crohn's disease. Secondary objectives are to compare the effectiveness of the SCD and MSD in reducing mucosal and systemic inflammation, assessed by measuring the concentration of fecal calprotectin (FCP) and C-reactive protein (CRP) respectively; to compare the diets' effectiveness in improving fatigue, pain, and joint symptoms; and to determine the proportion of patients who continue study diets when prepared food is no longer provided without cost and their reasons for discontinuing the diets. The research aims were guided by crowdsourcing patient-generated research priorities; those that received the most support from Patient-Powered Research Network (PPRN) members were related to diet.

Based on the book Breaking the Vicious Cycle (Gottschall 1987), the Specific Carbohydrate Diet (SCD) restricts all but simple carbohydrates. Fresh fruits, vegetables, unprocessed meats, lactose-free cheeses, and certain legumes are permitted; grains, processed foods, canned foods, and milk are not permitted. The Mediterranean style diet (MSD) involves a high intake of olive oil, fruit, nuts, vegetables, and cereals; moderate intake of legumes, fish, seafood, and poultry; and low dairy intake. Red and processed meats, soda drinks, bakery foods, and sweets are not permitted. The MSD was selected as the alternative diet in this trial due to its easier implementation, consistency with U.S. Department of Agriculture and World Health Organization recommendations, and evidence of its role in overall health and specific benefits for Crohn's patients.

A total of 194 adult patients with mild to moderate Crohn's disease were enrolled and randomized into either the SCD (intervention) or MSD (control) diet groups at 33 different sites across the United States. Patients received meal delivery for their assigned diet for six weeks, then were provided instructions and recipes to adhere to the diet on their own for weeks seven through twelve. Outcome measures were taken at baseline, six weeks, and twelve weeks.

The Comprehensive Post-Acute Stroke Services (COMPASS) Study is a pragmatic cluster-randomized clinical trial that evaluated the real-world effectiveness of the COMPASS transitional care (COMPASS-TC) model compared to usual care among adult stroke and transient ischemic attack (TIA) patients discharged home between 2016 and 2018. In Phase 1, 40 North Carolina hospital units were randomized 1:1 to the COMPASS-TC intervention or usual care, stratified by stroke patient volume and stroke center certification. In Phase 2, hospitals randomized to usual care crossed over to implement COMPASS-TC, and hospitals randomized to the intervention sustained COMPASS-TC. The intervention was patient-centered and assessed social and functional determinates of health to inform individualized care plans for secondary prevention, recovery, and referrals to services and community-based resources. COMPASS-TC was consistent with Centers for Medicare and Medicaid Services (CMS) TC management reimbursement requirements.

The primary outcome was functional status (Stroke Impact Scale-16; SIS-16) at 90 days; secondary outcomes were mortality, disability, medication adherence, depression, cognition, self-rated health, fatigue, care satisfaction, home blood pressure monitoring, falls, and caregiver strain. Telephone interviewers, blinded to treatment assignment, assessed these outcomes at 90 days.