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Network meta-analysis, or NMA, is a statistical method that researchers use to combine results from many clinical trials done within a research network. A research network is a group of scientists and doctors from different places, like hospitals and research centers, who do studies together and share data. Researchers can use NMA to compare how well different treatments work for a specific health problem. But current NMA methods don't work well when comparing three or more treatments across many health outcomes.

In this study, the research team developed new NMA methods to compare three or more treatments that bring on labor to start the process of childbirth across many health outcomes using research network data.

Clinical trials study the effects of medical treatments, like how safe they are and how well they work. But most clinical trials don't get all the data they need from patients. Patients may not answer all questions on a survey, or they may drop out of a study after it has started. The missing data can affect researchers' ability to detect the effects of treatments.

To address the problem of missing data, researchers can make different guesses based on why and how data are missing. Then they can look at results for each guess. If results based on different guesses are similar, researchers can have more confidence that the study results are accurate. In this study, the research team created new methods to do these tests and developed software that runs these tests.

To access the sensitivity analysis methods and software, please visit the MissingDataMatters website.

This project aims to improve the methods that researchers use to compare how treatments affect different patients. When researchers use data from patients' health records to compare treatments, it's often hard to know whether changes in a patient's health are from the treatment or something else. Factors other than the treatment may affect the patient's health, including

• A patient's traits, such as age, gender, or other health problems
• Group-level factors, such as where patients get care or where they live

To address this problem, researchers rely on statistical methods. Existing methods use data from patients who have similar traits but received different treatments. But they may not work well if some group-level factors affect both the treatment and patients' health. In this study, the research team created two new ways of including group-level factors in the methods they use to find similar patients.

Data from healthcare systems, patients and communities, and health plans can support health research. Two types of data sources are

• Patient-powered research networks, or PPRNs. In PPRNs, patients, families, caregivers, and community members share health data with the network. They work closely with researchers to plan and conduct research.
• Health plan research networks, or HPRNs. In HPRNs, networks of health plans have access to health claims data from members for research.

By linking patient records across PPRNs and HPRNs, researchers may be able to do more robust research. To link records, researchers use computer programs to connect the records of people in a PPRN with their claims data in an HPRN. Current methods to link records require use of personal information, such as names and dates of birth. But patients may not want to share this information.

In this project, the research team developed methods for linking data from PPRNs and HPRNs without using patients' personal information.

Patients treated for metastatic cancer, or cancer that has spread to another part of the body, often have symptoms from cancer and its treatment. They may feel tired, depressed, or nauseated. They may find it hard to do their usual activities. Better symptom tracking may help improve patients' care. For example, symptom tracking could quickly alert doctors when a patient may need a different medicine. In this study, the research team compared use of a weekly electronic symptom tracking system versus usual care for patients with cancer. Patients receiving usual care could report their symptoms to their care team during regular clinic visits. The research team wanted to see if the tracking system helped patients live longer, have better quality of life, or go to the hospital or emergency room less often. The aims of this study were as follows:

1. Determine whether integrating electronic patient-reported outcomes (ePRO) in cancer care improves patient-centered outcomes;
2. Elicit perspectives about benefit burden tradeoffs for integrating patient-reported outcomes into clinical workflow; and
3. Identify barriers, facilitators, and strategies used by practices to integrate patient-reported outcomes into clinical workflow.

A total of 1,191 patients were enrolled from 52 U.S.-based community oncology practices. Randomization into intervention and control conditions occurred at the site level. Data collected as part of this study included patient clinical information; weekly symptom surveys, quality of life surveys, and cancer care surveys completed by patients; feedback on the ePRO intervention from patients, clinical research associates, nurses, and physicians; and symptom alerts sent to nursing staff. Please note that while qualitative data were collected as part of this study, they are not available.

Researchers often combine data from different sources, such as insurance claims and health records, to get a better picture of patients' health and use of health care. Researchers use unique identifiers, like Social Security numbers, to connect patient records and make them more complete. But sometimes this approach doesn't work well, especially when records don't have much personal information. Having limited personal data can lead to errors when linking records.

In this study, the research team created new methods to link data sets with limited personal information. Then they compared the new methods with existing ones. They also applied the new methods with real patient data.

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.

This study consists of a two-group, randomized, controlled comparative effectiveness trial with 300 individuals from Appalachian Kentucky who do not have a primary care provider (and thus are not able to receive the standard of care without intercession) and who are at risk for CVD (cardiovascular disease) by virtue of having two or more modifiable CVD risk factors. The researchers compared (1) the standard of care alone, referral to a primary care provider for management of CVD risk factors, with (2) standard of care supplemented by patient-centered, culturally appropriate, self-care CVD risk reduction intervention (HeartHealth) designed to improve multiple CVD risk factors while overcoming barriers to success.

The researchers compared the 4 month (short-term) and 1 year (long-term) impact of the interventions on: 1) CVD risk factors selected by patients (i.e., tobacco use, blood pressure, lipid profile, HgA1c for diabetics, body mass index, waist circumference, depressive symptoms, or physical activity level); 2) all CVD risk factors for each patient; 3) quality of life; 4) patient and healthcare provider satisfaction; 5) desirability and adoptability by assessing adherence to recommended CVD risk reduction protocols, and retention of recruited individuals.

Demographic variables include gender, age, ethnicity, marital status, employment status, and level of education.

The Preserving Kidney Function in Children With Chronic Kidney Disease (PRESERVE) study was designed to provide new knowledge to inform shared decision-making regarding blood pressure (BP) management for pediatric chronic kidney disease (CKD). PRESERVE compared the effectiveness of alternative strategies for monitoring and treating hypertension on preserving kidney function; expanded the National Patient-Centered Clinical Research Network (PCORnet) Common Data Model by adding pediatric- and kidney-specific variables and linking electronic health record data to other kidney disease databases; and assessed the lived experiences of patients related to BP management.

Participants were recruited from 15 clinical institutions across the United States. The research team analyzed electronic health record (EHR) data from 11,851 children with CKD and their caregivers to compare different ways to monitor and treat BP to preserve kidney function. In addition, a subset of patients and caregivers completed an online survey detailing patient-reported outcomes, such as fatigue, life satisfaction, pain levels, sleep disturbance, anxiety, and peer relationships (n=395).

Due to the risk of re-identification based on unique patterns in the individual-level PCORnet electronic health record (EHR) data, patient privacy regulations prohibit the public release of the individual-level data. This collection contains the code underlying the analysis; instructions, codesets, and output lists for the PCORnet queries; and the survey questionnaires for patients and family members.

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.

Clinical prediction models, or CPMs, are statistical models that can predict a patient's risk for a specific event, such as a health problem, adverse effect, or even death. To create a CPM, researchers use a single data set, such as data from a clinical trial. To find out whether the CPM accurately predicts risks for patients who weren't part of the original data, researchers can test the CPM with other data sets. This testing can help researchers know if the CPM is accurate for patients from different backgrounds and whether it can be used to make health decisions. But few CPMs have been tested with other data sets.

In this study, the research team used other data sets to look at how well CPMs for heart disease predict patients' risks. They also looked at how to improve CPMs.

To access the software and methods, please visit the Tufts Race CPM Registry.

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.

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 TARGET (Targeted Interventions to Prevent Chronic Low Back Pain in High-Risk Patients) Trial was a primary care-based, multisite, cluster randomized, pragmatic trial comparing guideline-based care (GBC) to GBC + referral to Psychologically Informed Physical Therapy (PIPT) for patients presenting with acute lower back pain (LBP) and identified as high risk for persistent disabling symptoms. Chronic lower back pain (LBP) is defined as a response of "more than three months" to question 1, and a response of "half the days or more than half the days" in the past 6 months to question 2. See Appendix 1 for the LBP Questionnaire in the Protocol report.

Study sites included primary care clinics within each of four geographical regions in the United States, with clinics randomized to either GBC or GBC+PIPT. Acute LBP patients at all clinics were risk stratified (high, medium, low) using the STarT Back Tool. The primary outcomes were the presence of chronic LBP and LBP-related functional disability determined by the Oswestry Disability Index at 6 months. Secondary outcomes were LBP-related processes of health care and utilization of services over 12 months, determined through electronic medical records.

Study enrollment began in May 2016 and concluded in June 2018. The trial was powered to include at least 1,860 high-risk patients in the cluster-randomized controlled trial cohort. A prospective observational cohort of approximately 6,900 low and medium-risk acute LBP patients was enrolled concurrently.

This data collection contains a single data file with 223 variables and 9,730 cases. The number of respondents at each of the study locations were:

• Boston Medical Center: 997 respondents
• Intermountain Health (Salt Lake City): 2,094 respondents
• Johns Hopkins University (Baltimore): 1,615 respondents
• University of Pittsburg Medical Center: 5,024 respondents

Amplification is an evidence-based front-line treatment for those with impaired communication secondary to Age Related Hearing Loss (ARHL). ARHL is the most prevalent cause of communication impairment among older adults and multiple existing evidence-based care models exist to address it. This study compared the two most common models of care (defined below) for ARHL provided to adults in assisted living/personal care communities.

• The Consult Model (i.e., usual care) was an acute care strategy, relying on a monthly Audiologist visit to the facility.
• The Engage Model was a chronic care approach to supportive hearing loss self-management of ARHL. Engage includes (a) hearing screening for all residents, (b) an individualized communication plan for those with an identified hearing loss (e.g., one-to-one, group, telephone, television plans, hearing aid trouble shooting, communication strategies, etc.), (c) provision of simple, non-custom amplifiers, (d) referral to audiology if needed, and (e) ongoing support provided by trained personnel (Communication Facilitator) under the supervision of the audiologist.

This study included three separate sample populations at 10 medical facilities. The staff at the medical facilities were selected to measure job satisfaction (DS1). Residents of the medical facilities were sampled to collect measures related to the impact of hearing on an individual's life and general demographics (DS2 and DS3). And the family of the residents were sampled to measure caregiver burden (DS4).

Cluster randomized trials, or CRTs, are studies that compare treatments across different groups of patients, or clusters. An example of a cluster is people who receive care at one clinic.

To reduce bias in CRT results, researchers assign clusters by chance to different treatments. But what happens after they assign treatment can lead to differences across clusters and bias the results. For example, patients who visit clinics assigned to a treatment may be older than patients who visit clinics not assigned to that treatment. Current statistical methods for analyzing data from CRTs don't work well to account for these differences.

In this study, the research team developed new methods to account for differences across clusters after treatment assignment.

In a stepped-wedge cluster randomized trial, or SW-CRT, researchers compare new treatments to standard treatments in groups of patients, such as patients at different clinics, to look at the treatments' effectiveness. They assign groups by chance to switch from the standard to new treatment at different time points until all groups have received the new treatment. The different time points to switch treatments are called steps.

SW-CRTs take time and resources. If researchers know they can't collect data on all groups and all steps in a SW-CRT, they can plan to use an incomplete SW-CRT design. In incomplete SW-CRTs, researchers plan the study knowing that some clinics or steps will have missing data. But researchers need better guidance for planning incomplete SW-CRTs that still get accurate results.

Also, current methods for planning how many patients and groups should take part in SW-CRTs don't work well for large studies. They also don't work well with certain types of outcomes, like yes or no outcomes; outcomes that have counts, like number of hospital visits; or continuous outcomes, like a score from 0 to 100.

In this study, the research team developed and tested new methods to design and analyze SW-CRTs with different patterns of planned missing data, large data sets, and different types of outcomes.

Comparative effectiveness research, or CER, compares two or more treatments. In some CER studies, researchers use patient data from electronic health records, or EHRs, to compare treatments. But patient traits like age may affect doctors' and patients' choice of treatments, which can bias results. Using EHR systems to identify eligible patients and assign them to treatments by chance could improve results of CER studies that use EHR data.

In this study, the research team explored the views of patients, clinic staff, and clinicians, such as doctors or nurses, on doing CER studies in clinics. The team also tested software with a widely used EHR system. The software finds patients who qualify for a study. During a clinic visit, the software prompts doctors to invite patients to take part in the study. If patients agree, the software assigns patients by chance to a treatment.

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.

Using a pragmatic trial design to limit exclusions, the investigators conducted a 36-month multi-center randomized effectiveness trial to compare the impact of an enhanced usual care (control) intervention, with exercise coaching (exercise), on fragility fractures and serious fall-related injuries (FF/SFRI) in patients with a previous fragility fracture. Specifically, the investigators examined the impact of the intervention on social loneliness, physical function, and bone strength. 1,139 individuals over 65 with a history of fragility fractures and/or osteoporosis were recruited over two years across three regions of Pennsylvania and randomized into either the enhanced usual care control group or exercise with coaching treatment group, where in-person exercise activities were led by trained volunteers.

Dataset (DS) 1 contains the following data used for analysis: participant characteristics at baseline by study group (referred to as Table 5 in the documentation), intervention participant characteristics at baseline based on exercise session type (referred to as Table 6), cumulative incidences of first serious fall-related injury compared by study group (referred to as Figure 3), cumulative incidence for first serious fall-related injury by age, gender, race, and osteoporosis medication (referred to as Table 8 and Figure 4), and cumulative incidence for first series fall-related injury by tertile of average intervention sessions per month (referred to as Figure 5). Other datasets used for analysis are fall injury data (DS2), monthly workout sessions data (DS3), secondary outcomes data (DS4, referred to as Table 7), and adverse events data (DS5, referred to as Table 9). DS6 includes markers designating before and after the start of the COVID-19 pandemic (March 11, 2020), allowing for analyses of participants who experienced fall-related injuries relative to COVID-19.

Datasets labeled "Miscellaneous" were not used in any analysis. These datasets contain extra measures from screening (DS7), baseline assessments (DS8), 4-month check-in visits (DS9), participant's distance to study site (DS10), coaching check-ins for weeks 1-12 (DS11), exercise sessions by month (DS12), adverse events (DS13), and end of study information (DS14).

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.

Meta-analyses combine the results of many studies to find out how well a treatment or other healthcare intervention works. Most meta-analyses use public sources of data, such as published journal articles, as the main sources of information for study results. But journal articles are not the only sources of study results. Some results appear in other places, such as clinical study reports. Clinical study reports are documents that describe what researchers did and found in much more detail than journal articles. However, these reports may not be available to the public. As a result, meta-analyses may not include all available information about a treatment.

The research team wanted to learn whether adding or replacing public and nonpublic data sources changed the results of meta-analyses. To find out, the research team added and replaced data as they conducted two meta-analyses. The first looked at adult use of a nerve-pain medicine. The second meta-analysis looked at adult use of a medicine to treat bipolar depression.

The standard care of hospital or emergency department patients experiencing an acute ischemic stroke includes intravenously administered tissue plasminogen activator (tPA). Mobile stroke units (MSUs) are ambulances equipped with staff and a computed tomographic scanner that can allow for tPA to be administered more quickly. This comparative effectiveness trial examined clinical outcomes in stroke patients who received either an earlier diagnosis and treatment using an MSU or standard triage and transport by Emergency Medical Services (EMS). The sample included multicenter cohorts with randomized deployment weeks and blinded assessment of both trial entry and clinical outcomes.

To diagnose rare genetic conditions, doctors look at patients' genetic data and a phenotypic profile. A phenotypic profile is a record of all the physical traits of a condition. It uses a list of standard terms called Human Phenotype Ontology, or HPO. Doctors and clinic staff do a thorough exam with the patient to create the profile. The exam takes a long time and often more than one visit.

Patients may be able to create phenotypic profiles themselves using surveys. These surveys may take less time than clinic visits. But it is unclear whether patient surveys can provide enough details to correctly identify conditions.

In this project, the research team tested two surveys:

• Phenotypr. This survey asks patients to describe their symptoms and then matches the descriptions to plain language HPO or clinical HPO terms.
• GenomeConnect. This survey uses multiple choice questions to asks patients about their health and symptoms.

A rare disease is one that affects fewer than 200,000 people in the United States. Because few people have these diseases, clinical studies on treatments can be hard to conduct. One way to study rare disease treatments is with an snSMART study.

snSMART studies have two stages. In the first stage, researchers assign patients to a treatment by chance. In the second stage, patients may stay with the same treatment or switch treatments. Patients stay on the same treatment if it's working well. If the treatment isn't working, researchers assign patients by chance to a new treatment.

snSMARTs can help researchers learn more from a smaller number of patients than a standard clinical study. But most current methods for analyzing snSMARTs use data only from the first stage, which can lead to inefficient results.

In this project, the research team developed and tested new methods that use data from both stages to analyze snSMARTs. The team compared results from the new methods to actual treatment effectiveness to see Bias, or whether results are too high or too low effficiency, or how big the difference is between the results and actual treatment effectiveness

To access the software, please visit the snSMART Sample Size App.

A rare disease is one that affects fewer than 200,000 people in the United States. Because few people have these diseases, clinical studies on treatments can be hard to conduct. One way to study rare disease treatments is with an small n sequential multiple assignment randomized trial (snSMART) study.

snSMART studies have two stages. In the first stage, researchers assign patients to a treatment by chance. In the second stage, patients may stay with the same treatment or switch treatments. Patients stay on the same treatment if it's working well. If the treatment isn't working, researchers assign patients by chance to a new treatment.

snSMARTs can help researchers learn more from a smaller number of patients than a standard clinical study. But most current methods for analyzing snSMARTs use data only from the first stage, which can lead to inefficient results.

In this project, the research team developed and tested new methods that use data from both stages to analyze snSMARTs. The team compared results from the new methods to actual treatment effectiveness to see:

• Bias, or whether results are too high or too low
• Efficiency, or how big the difference is between the results and actual treatment effectiveness

This study contains two supplementary documentation files. There is no data included in this release.

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

In the United States, tobacco smoking is associated with significant morbidity and premature mortality for individuals with serious mental illness (SMI) (e.g., schizophrenia, post-traumatic stress disorder, bipolar disorder, major depressive disorder). While many smokers with SMI wish to quit smoking, few are offered advice or treatments with demonstrated effectiveness in reducing tobacco dependence, primarily medication-assisted treatments. The overall aim of this randomized controlled trial was to test the effects of provider education (PE) (i.e. provider-level educational intervention focused on evidence-based smoking cessation treatment for those with SMI) and community health worker (CHW) support on the provision and utilization of smoking cessation treatment to those with SMI, and cessation rates for adults with SMI who smoke or use tobacco over a 2-year period. The objectives of this trial were to:

1. Examine whether an intervention combining PE and CHW support would increase prescriber provision of advice and assistance to quit smoking, and improve tobacco cessation rates in smokers with SMI compared to usual care/treatment as usual (TAU) and compared to PE-only treatment
2. Determine the effect of the combined PE+CHW intervention on patient-reported overall health compared to TAU and PE-only treatment

Eligible individuals were recruited from two outpatient psychiatric service providers in the Boston, Massachusetts metropolitan area. Clinics where individuals received services were randomized into either the TAU condition or into the PE condition, where health care providers would receive additional education on first-line medications used to treat tobacco use disorder. Within clinics in the PE arm, individuals were further randomized into the community health worker (CHW) support condition (PE+CHW), where CHWs would assist participants with smoking cessation care access and provide community outreach and education, or no CHW support (PE-only). Enrolled participants (n=1,010) completed surveys on smoking/tobacco use at 3 timepoints: study baseline, 1 year post-randomization, and 2 years post-randomization.

A mixed-methods evaluation of the trial was also conducted post-intervention, using an interactive convergent design. The aims of the evaluation were to identify barriers and facilitators to effective implementation; examine how primary care providers differed by performance and engagement level, and how experiences with the intervention compared across these groups; and identify anticipated barriers to implementing the intervention as discussed by stakeholders. Quantitative outcome and visit data from the trial were used in the evaluation. For the evaluation's qualitative component, interviews were conducted with purposively sampled community health workers, smoker participants, primary care providers, and other stakeholders in policy, payor, and clinical administration. Please note that the qualitative evaluation data are not available for this collection.

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).

Clinical practice guidelines are recommendations for doctors about when and how to treat health problems. Guidelines are often based on research that compares the benefits and harms of different tests or treatments for one health problem. But this research doesn't always consider that people may have other health problems or different preferences. Guideline developers need to know what is important to patients.

In this study, the research team developed a process to inform development of clinical guidelines. The team wanted to learn how the balance of benefits and harms of treatment options changes when it includes patient preferences. In this new process, the team

• Defined questions comparing treatment options based on input from patients with three or more long-term health problems
• Used data from prior research studies to answer these questions and assess the balance of benefits and harms of treatment options
• Used results from a patient survey, looking to see if the balance of benefits and harms could change when patients have different preferences.

Comparative effectiveness research compares two or more treatments to see which one works better for certain patients. For example, research can see if medicines or stents work better for people with heart problems. Such research may include:

• Observational studies. A research team studies what happens when patients and their clinicians choose the treatments. Traits, such as age or health, may affect patients' treatment choices. These traits may also affect patients' responses to treatments. It may be hard for the team to tell if a patient's traits, the treatment, or a mix of the two affected how well the treatment worked.
• Clinical trials. The team assigns patients to a treatment by chance. Traits may affect a patient's ability to join a clinical trial.

Comparative effectiveness research compares two or more treatments to see which one works better for certain patients. Researchers often use data from patients' electronic health records to compare different treatments. This study addresses some problems that can arise from this practice. In some long-term research studies, researchers use data collected when patients in the studies see their doctors. Regularly scheduled doctor visits, called well visits, include yearly checkups or periodic blood pressure checks. Other doctor visits, called sick visits, occur when a patient feels sick or needs special care.

Well and sick visits can produce different types of health record data. In addition, test results at sick visits may be different from results at well visits. Using data from sick visits may inappropriately influence, or bias, a study's results. Also, patients may go to the doctor more often when they have symptoms or chronic health problems. Researchers may then collect more data from these patients than they collect from the healthier patients. Unequal amounts of data per patient make it harder to compare treatment results.

For this study, the research team created three tests to find if data from sick visits lead to bias in a study's findings. The team also compared standard and newer statistical methods for analyzing data that include sick visits. Researchers designed the newer methods to reduce bias from data obtained at sick visits. With less biased results, doctors can be more certain about which treatment worked better for certain patients.

Research studies called clinical trials test treatments to see if they are safe and effective for patients. When designing clinical trials, researchers must plan to include enough patients with different traits for the study to have accurate results. Once the study starts, researchers must follow the plan. Sometimes, early results from a trial show that a group of patients with a certain trait may have more benefits or harms from the treatment than other groups. For example, the treatment may not work for patients with a history of heart disease. In the standard trial design, researchers can't change the plan to stop enrolling these patients once the trial starts.

In this study, the research team compared the standard trial design with more flexible approaches known as adaptive enrichment designs. These designs set up rules that allow researchers to change the study plan. For example, if early results show a treatment doesn't work for patients with heart disease, researchers can stop enrolling these patients in the trial. The team compared the trial designs using data from four completed trials.

To access the methods and software, please visit the AdaptiveDesignStreamlinedOptimizer GitHub.

Electronic health records store a lot of data about a patient. These data often include age, health problems, current medicines, and lab results. Looking at these data may help doctors treating patients after a trauma predict how likely it is that they will respond well to a treatment and survive. This information can help doctors make better treatment decisions. But first, researchers need to figure out how to combine and analyze data to make accurate predictions. In this study, the research team created new statistical methods to combine data from patient records. They used these methods to predict patient health outcomes. Then the team used health record data collected from patients in hospital trauma centers to test their predictions.

To access the methods and software, please visit the following GitHubs:

• origami
• varimpact
• opttx

Cluster randomized trials, or CRTs, are research studies that compare treatments among different groups of patients, or clusters. An example of a cluster is a group of people who receive care at a single clinic. One type of CRT is a stepped-wedge CRT. These CRTs compare patients' health before and after a new treatment. In stepped-wedge CRTs, all groups start with the standard treatment. Then, each group switches to the new treatment at a specific time during the study. By the end of the study, all groups are receiving the new treatment. In stepped-wedge CRTs, group characteristics, such as how clinics follow up with patients, can affect how well a treatment works. It is hard to figure out if changes in a patient's health are due to the treatment or group characteristics. In this study, the research team wanted to improve how to plan and analyze stepped-wedge CRTs for studying the effect of treatments.

The study had two parts. In the first part, the research team looked at ways to measure how well treatments work in stepped-wedge CRTs in ways that account for group characteristics. In the second part, the research team looked at which statistical methods got accurate results when using data from stepped-wedge CRTs. The team first used a computer program to create test data that looked like data from a stepped-wedge CRT. The team created the test data using nine scenarios; each scenario had a different set of conditions. For example, the number of patient groups varied across each scenario. Using the test data, the team compared six statistical methods for analyzing data from stepped-wedge CRTs. The research team also created a statistical program to help plan and analyze stepped-wedge CRTs.

This collection contains the R software package swCRTdesign and accompanying documentation. The package source as a .tar.gz file and six different versions are available in a zipped package. Files have been released as received by ICPSR from the depositor:

• For R version 4.2.3, created March, 11, 2024 (Windows)
• For R version 4.3.3, created March, 10, 2024 (Windows)
• For R version 4.4.0, created March, 11, 2024 (Windows)
• For R version 4.2.0, created August, 27, 2023 (macOS)
• For R version 4.3.0, created August, 26, 2023 (macOS)
• For R version 4.3.0, created August, 27, 2023 (macOS)

Randomized controlled trials, or RCTs, look at how well treatments work. But people who take part in RCTs may differ from patients who receive care in clinics. For instance, patients who take part in RCTs may be less likely to smoke or may have fewer health problems. These differences can affect how well a treatment works. As a result, a treatment may work differently for a patient receiving care in a clinic than it did for patients who took part in the RCT.

Researchers can use statistical methods to account for differences in patient traits and behaviors. In this project, the research team developed and tested new methods to account for these differences. They used the methods to apply RCT results to patients receiving care in clinics.

To access the methods and software, please visit the generalizability_g_form_IPW and ExtendingInferences GitHub repositories.

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).

Treatment plans for patients with long-term health problems such as diabetes or arthritis often change over time. Such plans are called adaptive treatment plans as doctors adapt treatment based on the patient's health problem and response to earlier treatments. Adaptive treatment plans are common, but the methods to assess how well a plan works may not always provide accurate results. To know which plans are best for patients, researchers need better methods to compare these adaptive plans.

In this study, the research team developed and tested a new statistical method and looked at whether it could more accurately compare adaptive treatment plans.

To access the methods and software, please visit the PCATS Application.

Clinical notes in electronic health records, or EHRs, may contain information that can help researchers study and compare treatments. But it takes researchers a lot of time to find information in EHR notes.

Natural language processing, or NLP, methods can help researchers find information in EHR notes. With NLP, computer programs read and identify written language to make it easier to sort and study. But in EHR notes, some sentences may contain more than one topic. Also, EHR notes may discuss a single topic over many sentences. In these cases, current NLP methods don't work well to find complete and accurate information about a specific topic.

In this study, the research team developed and tested new NLP methods to identify topics from EHR notes.

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.

Researchers can use data from electronic health records, or EHRs, in studies that compare two or more treatments. In these studies, researchers need to identify all patients with the same phenotype. Phenotypes are a person's known traits, like height and weight, or known health problems, like diabetes. However, in EHR data, some data on patient traits or health problems may be missing for some patients.

Missing data in EHRs make it hard to correctly identify all patients with the same phenotype. It's even harder when data are missing due to a patient's health status. For example, patients with uncontrolled diabetes may need more lab tests than patients with controlled diabetes. As a result, researchers who are looking at lab tests may not identify patients with controlled diabetes as having diabetes.

In this project, the research team developed and tested a new statistical method that accounts for missing EHR data to estimate patient phenotypes.

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

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

INtegrated Services for Pain: Interventions to Reduce Pain Effectively (INSPIRE) was a pragmatic randomized trial conducted from 2019 to 2023 with adults receiving chronic opioid therapy (COT) of at least 20 morphine milligram equivalents (MME) daily for chronic noncancer pain (CNCP). Participants were recruited from primary care and specialty pain clinics at three academic health centers in North Carolina and Tennessee. The study compared the effectiveness of the two behavioral interventions, 1) shared decision making (SDM) versus 2) motivational interviewing plus cognitive behavioral therapy for chronic pain (MI+CBT), on change in opioid dose, physical function, and pain interference. INSPIRE combined data from electronic health records (EHR) on opioid dose from baseline to 18 months and comorbidities with participant survey data at baseline, 6, and 12 months on the following topics:

• physical function,
• pain interference,
• pain intensity,
• anxiety,
• depression,
• pain severity,
• discontinuation of opioids,
• intent to reduce opioids,
• opioid use relative to baseline,
• adverse events,
• demographics,
• health insurance coverage,
• health literacy,
• patient-centered communication, and
• types of pain treatment used.

The collection includes three analysis datasets:

1. Adverse Events Dataset - one record per subject per adverse event
2. Opioid Prescriptions Dataset (post-processed opioid prescriptions used to derive the study's primary outcome) - one record per subject per opioid prescription
3. Outcomes Dataset (contains all of the study's demographics, primary, secondary, exploratory, and subgroup analysis variables) - one record per subject per timepoint

Researchers can use patient health data to compare treatments. But these data may include information, like names or social security numbers, that could identify patients. Researchers use different methods to remove such information and protect patients' privacy. Some methods work well to protect privacy but may make data less useful for research. Other methods don't protect privacy well enough.

Current methods for protecting privacy don't work well when:

• The number of patients in the data set is smaller than the number of data fields, such as patient traits or health conditions, and data are updated many times
• Patients' health and treatments are measured at more than one point in time
• Data are displayed as a graph to better capture some types of content

In this study, the research team created three new methods. The team wanted to see if the new methods better protect patient privacy but also make sure data remain useful for research.

To access the methods and software, please visit the AIMS Group at Emory University.

Data collected from interviews and group discussions, called qualitative data, can help researchers understand people's experiences, values, and cultures. But large amounts of qualitative data can be hard to show in a way that's easy for people to understand.

In this study, the research team created charts called ethnoarrays. These charts use color coding to show individual stories and overall patterns in qualitative data. The team wanted to learn whether ethnoarrays were useful and easy to understand.

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.

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.