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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.

Researchers often use data from patients' health records to compare treatments. But many things--not just treatments--affect patients' health. To figure out whether changes in patients' health result from treatment or something else, researchers can use statistical methods called instrumental variables, or IVs. IV methods account for factors that affect health but aren't in patients' health records, such as eating habits. Existing IV methods work well when looking at health outcomes that are measured using certain types of scales, such as blood pressure. But existing methods don't work as well to measure the time until a health event occurs, particularly when an event, like death, has not occurred for many patients in the study.

In this study, the research team created and tested a new IV method to more accurately estimate how a treatment relates to the time until a health event.

Researchers often use surveys to learn about what patients prefer. The wording of survey questions may affect how patients answer.

In this study, the research team compared different ways of asking patients with type 2 diabetes questions in a national survey. The questions asked patients about managing their diabetes and the medicines they prefer. The team wanted to see how accurately the different ways of asking questions measured patients' preferences. The study looked at whether patients thought the different ways of asking questions:

• Were easy to understand and answer
• Led to answers that matched what patients really wanted

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.

When choosing a treatment, doctors often look at research results that show how well the treatment worked in large groups of people. But many factors can affect how well a treatment works for an individual patient. These factors may include the patient's sex, age, other health problems, or how they responded to treatments in the past. Some patient data sources, such as electronic health records, have this information. But existing statistical methods may not use these data well. For example, existing methods may not be able to take advantage of data that include measurements of a patient's health from more than one point in time.

For this project, the research team developed new methods to analyze data that includes measurements of a patient's health from different points in time. To develop the new methods, the team used a Bayesian approach. Bayesian approaches include findings from previous studies in the analysis, which can make results more accurate.

To access the software and methods, please visit the Neuroconductor website and neuroc_travis GitHub.

The research team based their new method on an existing method called Bayesian Additive Regression Trees, or BART. To test the new method, the team used data created by a computer program to look like real patient data. Then they compared the new method with current methods under different scenarios. Each scenario included three treatments. The team changed the total number of patients, the number of patients who took each treatment, and how alike or different the patients were who took each treatment. Across all scenarios, the team predicted the average treatment effect for all patients and for only patients who received a treatment.

Next, the research team used the new method with real data from patients with lung cancer who were receiving care in New York City hospitals. The team compared three types of surgery: open chest, robotic assisted, and video assisted. The team looked at the effects of each type of surgery on four health outcomes: breathing problems; length of hospital stay after surgery; stay in an intensive care unit, or ICU; and the need to return to the hospital.

Patients, doctors, and researchers helped design the study.

The overall goal for this project was to reduce the incidence of COVID-19, hospitalization, and mortality among adults with serious mental illness (SMI) and intellectual disabilities/developmental disabilities (IDD) in congregate living settings (i.e., group homes) in Massachusetts, as well as to reduce COVID-19 incidence among staff who work in these settings. The research team was guided by two comparative effectiveness questions:

1. With the goal of prioritizing and making actionable best practices available as resources, what is the comparative effectiveness of various types and intensities of preventative interventions (e.g., screening, isolation, contact tracing, hand hygiene, physical distancing, use of face masks) in reducing rates of COVID-19, related hospitalizations, and related mortality in this population?
2. With the goal of effectively implementing best practices, what is the most effective implementation strategy to reduce rates of COVID-19 in this population: using tailored best practices (TBP) with SMI/IDD residents and staff of group homes in mind, or general best practices (GBP) from state and federal standard guidelines for all congregate care settings?

The specific aims of this study were as follows:

Aim 1a. Synthesize existing baseline data collected by 6 state behavioral health agencies on COVID-19 rates, hospitalization, mortality, and use of infection prevention practices.

Aim 1b. Collect stakeholder input via surveys and virtual focus groups on staff and resident experiences and on barriers/facilitators to implementing recommended preventative practices.

Aims 2a and 2b. Determine the comparative effectiveness of various COVID-19 preventative practices by (Aim 2a) using a validated simulation model to estimate COVID-19 spread in group homes and (Aim 2b) obtaining stakeholder input on prioritizing and defining tailored best practices for implementation.

Aim 3. Compare the effectiveness of TBPs with GBPs by using a hybrid effectiveness-implementation cluster randomized controlled trial.

Data collected to answer Aims 1 and 2 served as the foundation for designing the Aim 3 trial. Data for the trial were collected in 3-month intervals beginning January 2021 (baseline) until October 2022 (15-month follow-up). Residents and staff were sampled from approximately 400 group homes. Primary implementation outcome measures were COVID-19 vaccination rates and fidelity scores. The primary effectiveness outcome measure was COVID-19 infection.

Notes: This collection contains only data from Aim 1a and Aim 3. Throughout the data and documentation, "intellectual and/or developmental disabilities" is abbreviated as both IDD and ID/DD.

Many people have health problems that affect how well they can do normal activities, either for a short time or for their lifetime. These problems may be present from birth or result from illness, injury, or aging. Rehabilitation, or rehab, can help patients regain the ability to do normal activities. Rehab providers include doctors, nurses, psychologists, and physical, occupational, speech, or language therapists.

Rehab treatments often lack a common definition. Rehab providers often name treatments by the type of professional who delivers them or the problem they treat, rather than by the content of the treatment. Also, treatments can vary across rehab providers. Using a standard way to define rehab treatments may help researchers compare these treatments.

In this study, the research team created and tested a manual to help rehab providers use standard ways to define rehab treatments.

Many healthcare systems use electronic health records. Researchers use data from these records in their studies. Some records have missing or incorrect data. When this happens, people might not be able to trust a study's results. The research team wanted to:

• Create guidance to judge whether data that a study used were high quality
• Find new ways to display the quality of data
• Learn why researchers don't always report the quality of data that they used in studies

To access the methods and software, please visit the DQCODE-A-Thon GitHub.

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.

A randomized controlled trial, or RCT, is the best way to compare how well different treatments work to improve patients' health. In RCTs, researchers assign patients to treatment groups by chance. But RCTs aren't always an option due to high costs or ethical concerns. In these cases, researchers use other types of study designs such as

• Cohort studies, which look at patients' data over time to see how a treatment affects the risk of a certain health event, such as a heart attack
• Case-control studies, which compare data from patients who did and didn't have a certain health event

These designs often use data from health records to compare treatment results. In these studies, researchers use statistical methods to make results more like results from RCTs. Current methods work well for cohort studies but not for case-control studies.

In this study, the research team created and tested new methods and a guide to analyze case-control studies so that results would be more like results from an RCT.

In randomized controlled trials, or RCTs, researchers assign patients by chance to different treatments to compare the benefits and harms. In RCTs, researchers have a high level of control over how patients receive treatment. RCTs often take place in research clinics with staff who monitor how patients follow treatment plans.

Pragmatic RCTs, or pRCTs, take place where patients typically receive treatment, such as a regular clinic. pRCTs can help capture the real-world effects of treatment but determining whether a treatment works can be hard in pRCTs. Also, no clear guidance exists about how to collect and analyze data from pRCTs. Some kinds of analysis are better for helping researchers focus on what's important to patients.

In this study, the research team created guidance for collecting and analyzing data in pRCTs so that results reflect what matters to patients and researchers.

To access the methods and software, please visit the following Github repositories:

• CDP-analysis-2018
• GFORMULA-RCT-SAS
• IV-Bounds
• CHARM_reanalysis
• Adherence_LRCCPPT

The COMBINE study was a longitudinal examination of pediatric Crohn's Disease (CD) patients in the United States with data collected from 2015-2022. This study was a randomized, double blind, placebo controlled pragmatic trial to compare low dose oral methotrexate versus a placebo in children with Crohn's disease initiating anti-TNF (tumor necrosis factor) therapy with Infliximab or Adalimumab. Eligible participants were randomized with a 1:1 allocation and followed for a minimum of 12 months and maximum of 36 months in the context of routine clinical care. The primary outcome was a composite of indicators of treatment failure and/or toxicity. Secondary outcomes included patient reported outcomes of pain interference and fatigue.

Crohn's disease (CD) is a chronic inflammatory bowel disease (IBD) that affects approximately 600,000 Americans with estimated direct costs of $3.6 billion annually. Typical symptoms (e.g., abdominal pain, bloody diarrhea) result in substantial morbidity, including hospitalization and surgery, missed work and school, and diminished quality of life. The primary treatment goals for all CD patients are to induce remission by eradicating intestinal inflammation and related symptoms and maintain remission by preventing disease flares and progression. Additional treatment goals for pediatric CD include restoring physical and emotional development.

People experiencing homelessness (PEH) were among the most likely to contract the novel coronavirus disease 2019 (COVID-19). Many PEH utilized high-density public places to satisfy their basic needs (e.g., soup kitchens for sustenance, public libraries for restrooms). This made it difficult for them to limit close contact with others and put them at increased risk of contracting and transmitting COVID-19. Furthermore, it was difficult to follow recommended protective measures--such as handwashing and social distancing--when living in shelters or on the streets.

PEH were at higher risk of COVID-19 related hospitalization and death than the rest of the population. The poor living conditions of PEH accelerated aging, leading them to experience geriatric conditions and medical complications more typical of individuals 10-20 years older. They were also at increased risk of cardiovascular and respiratory disease, HIV/AIDS, and diabetes, all conditions that increase vulnerability to serious COVID-19-related complications and death. These risks were compounded by the fact that PEH also faced significant barriers to accessing quality health care. In the absence of protective action, it was estimated that more than 21,000 PEH would require hospitalization due to COVID-19, more than 7,000 would require critical care, and nearly 3,500 would die.

Consequently, the COVID-19 pandemic made housing and health care for PEH one of the top priorities for the U.S. health care and public health systems. State and local governments across the country used federal relief funds to allocate private hotel rooms as protective shelter for vulnerable PEH. In Los Angeles County (LAC), which contains the largest unsheltered homeless population in the nation, 2,400 PEH were placed in hotels. COVID-19 response plans included accommodating up to 15,000 PEH in hotels who would then be moved to permanent housing in 90 days. This rapid push into housing amid a pandemic necessitated a delicate balance between social distancing and maintaining patients' basic needs, continuity of existing care, and personal and social well-being.

Permanent supportive housing (PSH)--programs that provide immediate access to independent living situations coupled with support services--is the most effective approach for serving PEH. Numerous studies have demonstrated PSH's effectiveness in improving housing retention, quality of life, and HIV outcomes. Though evidence concerning its impact on other health outcomes, health behaviors, and health care utilization is limited, the National Academies of Sciences, Engineering, and Medicine has nonetheless recognized PSH as extremely beneficial for PEH's health. COVID-19 was what this organization termed a "housing-sensitive condition"--one whose transmissibility, course, and medical management are particularly influenced by homelessness. Consequently, the National Alliance to End Homelessness recommended the use of PSH as part of its framework to address COVID-19 and homelessness.

However, significant questions remain about what types of PSH programs can best address COVID-19-related risk and promote patient-centered outcomes at a time of social and community disruption. There are two distinct approaches to implementing PSH: place-based (PB) PSH, or single-site housing placement in a congregate residence with on-site services, and scattered-site (SS) PSH, which uses apartments rented from a private landlord to house clients while providing mobile case management services. The strengths and weaknesses of these two approaches remain largely unknown but may have direct implications for adherence to COVID-19 prevention protocols and other health-related outcomes.

Based on the state of the scientific evidence, the Sequenced Treatment Effectiveness for Posttraumatic Stress (STEPS) Trial was designed to address three specific aims. The first aim was to quantitatively compare engagement, self-reported PTSD symptom severity (primary outcome), quality of life, and recovery outcomes of primary care patients randomized to initially receive brief psychotherapy (Written Exposure Therapy - WET) or their choice of the three selective serotonin reuptake inhibitors (SSRIs). The second aim was, among patients not responding to initial treatment, to quantitatively compare outcomes of primary care patients randomized to: 1) augment the SSRI with WET, 2) switch from the SSRI to another class of antidepressants (serotonin-norepinephrine reuptake inhibitors - SNRI), or 3) switch from WET to the choice of the three SSRIs. The third specific aim was to quantitatively examine treatment heterogeneity among subgroups of primary care patients receiving pharmacotherapy and psychotherapy, including veterans, women, and those using cannabis.

The COVID-19 Citizen Science (CCS) Study was launched early in the pandemic to collect patient-reported information about exposures, risk behaviors and outcomes relevant to the pandemic. The Patient-Centered Outcomes Research Institute (PCORI) funded the research team to expand recruitment into CCS using PCORnet, the National Patient-Centered Clinical Research Network, and to use the resulting data to compare the patient-reported impact of pandemic associated policies. The research team systematically collected pandemic-associated policies enacted by counties across the United States (focusing in areas where there were many CCS participants), and to do so on a weekly basis from the beginning of the pandemic using publicly available sources.

Researchers combined data from various sources to answer two primary research questions (RQ):

1. What is the comparative impact of different shelter-in-place/reopening policies, overall and in vulnerable populations, on patient-reported financial insecurity, mental health, and other subjective outcomes important to patients?
2. What is the comparative effectiveness of county-level containment and mitigation strategies at achieving timely access to COVID-19 vaccination, testing, healthcare, information and contact tracing?

The research team collected patient-reported data from the CCS study and policy data from the U.S COVID-19 County Policy (UCCP) database. Electronic health record (EHR) data were also available from some participants recruited from health systems located across 7 U.S. states who consented and authorized use of these data for the study. Data for these participants were extracted from the PCORnet Common Data Model (CDM). Additional county-level contextual variables were included in analysis.

This collection contains CCS survey data on patient-reported anxiety with county-level policies data (DS1), respondent demographics (DS2), baseline survey results (DS3), daily (DS4) and weekly (DS5) COVID-19 symptoms reports, COVID-19 vaccination surveys repeated monthly (DS6) as well as a one-time vaccination survey (DS7), and pandemic impacts check-in surveys (DS8). CDM datasets include logistic regression model outcomes to predict study enrollment among all invited participants (DS9), codes for immunizations (DS10), laboratory tests (DS11), and procedures (DS12). County-level variables are also available for years 2021 (DS13) and 2023 (DS14).

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 University of Pennsylvania Health System (Penn Medicine) developed COVID Watch, an automated text message-based, remote monitoring program with 24/7 clinical support. Remote outpatient monitoring of patients with COVID-19 became needed because patients with SARS-CoV-2 infection can decline rapidly and unpredictably, and because of their own limited capacity to manage acute symptoms and concerns about staff safety, office-based outpatient practices often redirect patients with confirmed or suspected COVID-19 to hospitals. As a result, emergency departments (EDs) and hospitals became overwhelmed during surge periods of high community incidence rates and prevalence. Remote monitoring has the potential to facilitate ED- and hospital-level care for patients who require it while supporting access to care for patients who can safely remain at home.

This study compared outcomes for patients enrolled in COVID Watch with those of patients who were eligible to enroll but received usual care, with the hypothesis that enrollment in COVID Watch was associated with reduced mortality. The present research examined whether patients with COVID-19 who were enrolled in COVID Watch experienced better health outcomes compared with usual care (Aim 1) and whether augmenting COVID Watch with at-home monitoring of SpO2 (blood-oxygen saturation) improves patient outcomes (Aim 2).

In an effort to make hearing aids more affordable and accessible to U.S. adults with hearing difficulty, the Food and Drug Administration (FDA) empowered these adults to self-fit over-the-counter (OTC) hearing aids. The effectiveness of two self-fit methods, Consumer Decides (CD) and Efficient Fitting (EF), was hypothesized to be non-inferior to the professional-fit method, (Audiology Based, AB). This was evaluated for both short-term (6 weeks post-fit) and long-term (6 months post-fit) outcomes.

Antibiotics are considered a feasible treatment for appendicitis, yet appendectomy remains the treatment standard in the United States. Previous randomized trials comparing these treatments excluded important subgroups and recruited small sample sizes but questions remain about the applicability of these previous findings. This study conducted the Comparison of Outcomes of antibiotic Drugs and Appendectomy (CODA) randomized clinical trial to compare antibiotics with appendectomy among adults with appendicitis, including those with appendicolith. Those recruited comprised a diverse population, compared an overall measure of health status as the primary outcome, and included several secondary clinical and patient-reported outcomes, complications, and measures of healthcare utilization.

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.

Patients often take surveys about their health or quality of life. Results from these surveys can help doctors meet patients' needs. Young children can't fill out surveys by themselves. They may not be able to read or understand the questions. Most often, parents or hospital staff read the questions aloud, or parents answer the questions for their children. But this method may not give accurate results.

In this study, the research team tested three surveys for children ages 4 to 12 who are going to have or who recently had surgery. The first survey asks about general health. The second survey asks about feeling worried before surgery. The third survey asks about pain after surgery. A computer program reads the survey questions aloud. The surveys are animated and choices for the answers appear as cartoons.

The team wanted to learn if the surveys were

• Accurate, or correctly capturing how the children were feeling
• Reliable, or if children answered in a consistent way when asked similar questions

Research that focuses on what's most important to patients can inform health decisions. Researchers use different methods to identify what's most important to patients.

In this study, the research team compared two methods for identifying what's most important to patients: one-on-one interviews and group concept mapping, or GCM. GCM is a three-round process that helps researchers get input from a group. In the first round, people brainstorm topics that are important to them. Next, people sort the topics into clusters based on similar ideas. Finally, researchers create a map to display and discuss the topics. Researchers can use the complete GCM process or the brainstorming round only.

The research team looked at one-on-one interviews versus GCM and compared the number of topics patients named and the amount of time and money required.

To address health problems in communities, healthcare providers usually look at evidence from studies about what has worked elsewhere. But sometimes these solutions don't fit local needs. A process called Boot Camp Translation, or BCT, brings community members and researchers together to look at research evidence and decide how to use it locally. BCT groups turn evidence into messages that make sense in their communities.

When no evidence exists, one way to find answers is to look for local stories of people who have overcome problems. These stories can provide ideas that other people can use to solve similar problems. Appreciative Inquiry, or AI, is a way to collect stories about how to overcome problems.

In this study, the research team combined AI and BCT in five communities around Colorado to find and share local solutions to health problems. Each community worked on a different health problem, such as getting mental health care or managing pain. The team wanted to identify lessons that other research teams can use.

Before people can join a research study, they need to provide informed consent. Informed consent is a process that helps people know

• What a study is about
• What will happen in the study
• The benefits and risks people may experience
• That they have a choice whether to join the study

Getting informed consent can take a long time, and people may not understand all the details. Researchers want to know what patients think about having short or longer discussions with doctors for studies with low risk. Low risk means the study's risks are no greater than those in daily life or from usual health care. In this study, the research team compared people's views about a shorter consent discussion for a study on blood pressure with the usual, longer discussion.

Community-engaged research is an umbrella term for forms of research that have community and stakeholder engagement as a core principle, for example, patient-centered outcomes research (PCOR), participatory action research, and community-based participatory research. However, the implementation and category of community engagement can vary across a spectrum from minimal engagement to fully collaborative engagement. A major methodological gap is the assessment of stakeholder engagement from a stakeholder perspective. Evaluation of the impact of stakeholder engagement on research development, implementation, and outcomes requires the development and validation of tools that assess engagement. This study's objective was to develop and validate comprehensive and condensed versions of a survey instrument that will be completed by stakeholders and used to assess engagement in research studies from a stakeholder perspective.

Researchers usually design studies to find out if a new treatment works better than no treatment or better than a treatment that is currently used. But sometimes researchers may want to know that a new treatment is not worse than one that's in use. These studies are called non-inferiority, or NI, studies. Researchers conduct NI studies when a new treatment has other benefits such as fewer side effects, even though it may not work better than the one in use. NI studies can provide useful information, but they are hard to design and conduct.

In this study, the research team tested different statistical methods for NI studies that compare treatments.

To access the methods and software, please visit the following Github repositories:

• Poisson3armNI
• Binary3armNI
• bayesianSWcontinuous
• SMART3armNI

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.

Patient-reported outcome measures, or PROMs, ask patients how they feel and what activities they can do in daily life. Patients receiving cancer treatment, such as chemotherapy, often have side effects. PROMs can help cancer centers know if patients are getting high-quality care that helps manage their side effects.

In this study, the research team wanted to

• Learn from patients and clinicians, like doctors and nurses, what side effects are important to track during chemotherapy
• Create PROMs that can measure important side effects of chemotherapy

The research team also wanted to test the PROMs to see

• If patients find them easy to complete
• If the PROMs can detect differences in how well cancer centers control patients' treatment side effects

Research teams usually decide on research agendas, or issues and questions to study in research projects, even when community stakeholder organizations, or CSOs, such as healthcare networks and patient organizations, are involved.

In this project, the research team explored a process for helping CSOs create research questions and set research agendas.

The way doctors communicate with patients during office visits can affect the quality of care. Studying conversations between doctors and patients can help doctors improve their communication skills.

To study conversations, researchers rely on written records, or transcripts, of office visits. They read the transcripts and give each conversation topic a label. For example, topics may include smoking or pain. But labeling topics in this way may take a lot of time.

In this project, the research team created and tested a new method to make this work easier using natural language processing, or NLP. With NLP, computer programs interpret written language. NLP methods use a process called machine learning, where computer programs use data to learn how to perform different tasks with little or no human input.

Many research studies seek to learn how treatments affect patients' quality of life. Quality of life includes mood and energy. It also includes how people view their roles in their families or communities and whether they can perform those roles. Researchers use surveys to ask about patients' quality of life. But patients may answer the same question differently depending on different characteristics, such as their age or where they live. Some patients may think about their work roles while others may think about their families or social lives. How patients think about quality of life can affect what researchers learn about the effects of treatment.

In this study, the research team tested two surveys they created to measure differences in how patients think about their quality of life. The first, long survey had 74 questions, and the second, short survey had 23 questions.

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.

Healthy sleep is important for a child's well-being, school performance, and mood. Doctors can ask children about their sleep health to identify and treat sleep problems. However, few reliable surveys are available for doctors to use to ask children about their sleep.

In this study, the research team created sets of survey questions that asked children or their parents about sleep health. The team interviewed children and their parents to make sure they could understand the questions and that the questions included sleep health topics important to them.

Systematic reviews combine the results of many studies. In health research, these reviews can help determine which treatments or types of care work best. As part of a systematic review, researchers find and record important study information, such as design and results, from published journal articles. This process, called abstraction, takes time. If researchers make errors during this process, the systematic review may come to incorrect conclusions, which can affect healthcare decisions. Researchers abstract information in different ways. In single abstraction and verification, one person abstracts information and a second person reviews it for accuracy. In dual abstraction, two people abstract information on their own and compare the results.

In this study, the research team created and tested a new software program to help with abstraction. In the new software, researchers place flags within a journal article, displayed next to a data collection form on a computer screen, to easily find abstracted information. The team compared three approaches for abstracting information:

• Single abstraction and verification with the new software
• Single abstraction and verification without the new software
• Dual abstraction without the new software

The research team looked at how accurate the abstractions were and how much time it took to do them.

To access the software and methods, please visit the DAA Bitbucket.

According to the World Health Organization, palliative care is "an approach that improves the quality of life of patients and their families facing the problems associated with life-threatening illness, through the prevention and relief of suffering by means of early identification and impeccable assessment and treatment of pain and other problems, physical, psycho-social and spiritual." The goal of the study was to generate comparative effectiveness research evidence to support the delivery of coordinated, community-based palliative care that effectively implements care plans consistent with the goals and preferences of older adults with advanced illness and their caregivers.

This study included a pragmatic, two-arm, multi-site randomized controlled trial of older adults (50+ years) with either poor prognosis cancer or end-stage organ failure who were recruited during an emergency department (ED) visit, along with their informal caregivers, to compare nurse-led telephonic case management to facilitated, outpatient specialty palliative care on: 1) quality of life in patients, 2) loneliness, 3) healthcare use in the 12 months following enrollment, 4) symptom burden, 5) caregiver strain, 6) caregiver quality of life, and 7) bereavement.

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.

Medical studies usually focus on learning how a treatment works for most people. These studies have strict rules about who can take part. Patients with multiple health problems, such as high blood pressure and diabetes together, often can't take part because in such medical studies having multiple health problems can cause patients to respond differently to treatments than people with fewer health problems.

N-of-1 studies are a different way to design research. People with multiple health problems can take part in N-of-1 studies. An N-of-1 study has only one patient. Doctors try different treatments with that person and look for the treatment that works best. In this study, the research team wanted to know:

• Whether patients and doctors thought N-of-1 studies could be useful
• Which health problems patients thought N-of-1 studies could be helpful for
• Which features, such as the length of the study or how much time it took to track treatment results, affected whether patients wanted to be in an N-of-1 study

A randomized trial is one of the best ways to learn if one treatment works better than another. Randomized trials assign patients to different treatments by chance. But they are not always affordable, and they take a long time to complete.

When randomized trials aren't possible, researchers can use observational studies to learn how treatments work. In observational studies, researchers look at what happens when patients and their doctors choose the treatments. Traits such as age or health may affect treatment choices. These traits may also affect patients' responses to treatment, making it hard to know if the treatment or the traits affected the patients' responses.

Some study designs and statistical methods may help address this problem and make results from observational studies more useful. These methods can give researchers more data about whether treatments work and how the same treatment can affect groups of patients differently.

The research team conducted three studies to test different methods of designing and analyzing observational studies. They wanted to know if observational studies that used these methods produced results similar to randomized trials.

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.

Understanding which aspects of health and wellness are most important to patients living with HIV can help clinics improve care. Clinics often have patients complete surveys about their health and wellness. This study created new sets of survey questions for people living with HIV.

This study had two parts. In the first part, the research team asked patients living with HIV and their clinicians, such as doctors and nurses, what health and wellness topics they found important. In the second part, the team created sets of survey questions for two topics: domestic violence and social support. The team then asked patients living with HIV to answer the questions.

Understanding patients' expectations and their experiences can help hospitals improve care. Hospitals often send surveys to patients to ask about their care. To create surveys quickly, hospitals sometimes pick questions from an item bank. An item bank is a list of survey questions available to use about a specific topic.

In this study, the research team created an item bank about childbirth care. The team used the item bank to create and carry out a survey about women's preferences and experiences with care during and after childbirth. The team looked at which questions related closely to women's overall ratings of their hospital care.

Chronic pain, which lasts for months or even years, can disturb people's daily lives and their relationships with others. Doctors want to measure how chronic pain affects people's lives so they can help their patients manage pain.

In this study, the research team wanted to measure two aspects of living with and managing chronic pain:

• Pain appraisal describes how people think about their pain. It measures how much people worry about their pain and how well they cope with pain or distract themselves from thinking about it.
• Pain-related self-efficacy describes how confident a person is that they can live well with pain.

The research team created two pools of questions, called item banks, to measure each of the two aspects of living with chronic pain. From these larger item banks, the team created and tested brief versions using two and six questions. These brief versions take people less time to finish than the full item banks.

Patient-reported outcome measures are surveys that ask patients about their health or well-being. These surveys may include questions about sleep, depression, or pain. Many of the surveys now in use don't focus on a specific health problem.

The research team wanted to create and test a process for adapting patient-reported outcome measures for specific health problems. To test this process, the team developed surveys for two health problems, one for heart failure and one for knee arthritis.

Patient-reported outcome measures are surveys that ask patients how they feel and what activities they can do. These surveys ask about things such as how well people sleep and how much their pain interferes with daily life.

In this study, the research team wanted to learn if two clinics could gather patient-reported outcome measures during routine care visits, and if patients with type 2 diabetes could use the results to set goals for improving their health. The research team also wanted to learn if patients and clinic staff saw value in using these measures.

A learning health system, or LHS, is a health system that constantly looks for new ways to improve patient care. At an LHS, doctors and other hospital staff use learning activities to improve care, put what they've learned into practice, and share findings with other hospitals. Activities may include doing studies that compare treatments to see which one works better for which patients.

Governance refers to the way LHSs oversee learning activities. Governance includes people, committees, and policies that regulate learning activities. Including patients as partners in governance helps make sure learning activities address what's important to patients and protect patients' rights and interests. But LHSs don't always include patients in governance.

In this study, the research team wanted to learn how LHSs include patient partners in governance. The team interviewed patients and health system leaders to answer this question.

Severe acute respiratory syndrome coronavirus 2 associated disease (COVID-19) is caused by a novel betacoronavirus, SARS-CoV-2, that was first isolated in January 2020 and has since caused a global pandemic unseen in decades in cases and mortality. At the time of initial protocol submission in April 2020, human vaccine clinical trials had just begun and experts predicted that a vaccine would not be available until April 2021 at the earliest. Therefore, new measures remained needed to prevent the spread of disease. In vitro studies suggested a potential moderate antiviral effect of hydroxychloroquine (HCQ).

This study aimed to evaluate the efficacy of HCQ to prevent COVID-19 clinical infection and to prevent viral shedding of SARS-CoV-2 among healthcare workers (HCWs), as well as to evaluate the safety and tolerability of HCQ. Participants prescreened through the Healthcare Exposure Response and Outcomes (HERO) Registry across 34 U.S. clinical centers were randomly assigned to take a placebo (n=676) or HCQ (n=683) for 30 days, with in-person clinic visits at baseline and 30 days, and an end-of-study virtual visit at 60 days. This collection contains analysis (DS1 through DS6) and tabulation (DS7 through DS44) data and accompanying documentation.