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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.

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

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

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

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

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

Health registries record data about patients with a specific health problem. These data may include age, weight, blood pressure, health problems, medical test results, and treatments received. But data in some patient records may be missing. For example, some patients may not report their weight or all of their health problems.

Research studies can use data from health registries to learn how well treatments work. But missing data can lead to incorrect results. To address the problem, researchers often exclude patient records with missing data from their studies. But doing this can also lead to incorrect results. The fewer records that researchers use, the greater the chance for incorrect results.

Missing data also lead to another problem: it is harder for researchers to find patient traits that could affect diagnosis and treatment. For example, patients who are overweight may get heart disease. But if data are missing, it is hard for researchers to be sure that trait could affect diagnosis and treatment of heart disease.

In this study, the research team developed new statistical methods to fill in missing data in large studies. The team also developed methods to use when data are missing to help find patient traits that could affect diagnosis and treatment.

To access the methods, software, and R package, please visit the Long Research Group website.

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 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

Medical devices, such as pacemakers or stents, can help diagnose, treat, or prevent health problems. Companies that make medical devices label them with unique device identifiers, or UDIs. UDIs contain data about a device, such as the make, model, and expiration date. Healthcare providers can scan UDIs when they use the devices and record UDI data in patients' health records.

Right now, UDI data can only be accessed by the health systems that use the devices. Having the UDI data in insurance claim forms, instead of only in patients' health records, would mean that researchers could look at data over time and across health systems. They could then use these data to help monitor devices for safety or study questions like how well devices are working.

In this study, the research team created ways to send UDI data from health systems to insurance claims forms.

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.

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.

Researchers often combine patient health data from different sources, such as claims and health records. These data contain personal information, such as names and social security numbers.

In this study, the research team wanted to learn patients' views on sharing and combining health data for research. The team surveyed patients about their views on

• Sharing health and personal data, such as social security numbers
• Benefits and risks of data sharing
• Ways to help patients feel comfortable sharing health data

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.

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.

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.

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.

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

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

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

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

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

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

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 data from health registries or electronic health records to compare two or more treatments. Registries store data about patients with a specific health problem. These data include how well those patients respond to treatments and information about patient traits, such as age, weight, or blood pressure. But sometimes data about patient traits are missing.

Missing data about patient traits can lead to incorrect study results, especially when traits change over time. For example, weight can change over time, and the patient may not report their weight at some points along the way. Researchers use statistical methods to fill in these missing data.

In this study, the research team compared a new statistical method to fill in missing data with traditional methods. Traditional methods remove patients with missing data or fill in each missing number with a single estimate. The new method creates multiple possible estimates to fill in each missing number.

To access the methods, software, and R package, please visit the SimulateCER GitHub and SimTimeVar CRAN website.

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.

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).

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.

Comparative effectiveness research compares two or more treatments to see which one works better for which patients. One type of research study is a randomized controlled trial, or an RCT. In an RCT, the research team assigns patients to a treatment by chance.

Other types of studies use information from health records and registries. Registries store data about patients with a specific health problem. They often include information on how each patient responds to a treatment. Because researchers don't assign treatments by chance in such studies, differences in how patients respond to a treatment may be from the treatment or something else, such as a patient's age or the severity of their illness. In studies using registries and health records, researchers apply statistical approaches, called causal inference methods, to estimate how treatments work. At the same time, they look at other things that could affect results, like a patient's age.

Researchers can choose among many different causal inference methods. But they may have a hard time knowing which methods to use or how to use complex methods correctly. In this study, the research team made an interactive online guide for researchers. The guide, called CERBOT, helps researchers design studies and select these methods.

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.

Comparative effectiveness research compares two or more treatments to see which one works better for which patients. When researchers can't assign patients by chance to treatments, they can use observational studies. In observational studies, researchers use data like health records to compare treatment effects. But it can be hard to know if the effects are due to the treatment or to patient traits, like age.

To address this issue, researchers can use statistical methods called propensity score matching, or PSM. With PSM, researchers create groups of patients for analysis who have received different treatments. They match patients with similar traits across groups. This method reduces bias when comparing treatments. But current PSM methods don't work well or may take many hours when comparing three or more treatments or when using large data sets.

In this study, the research team created and tested a new method for matching patients from large data sets to compare the effects of three or more treatments.

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.

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.

One goal of comparative effectiveness research is to find out which treatments work best for different groups of patients. For example, treatments may work differently for patients with only one health problem than for those with more than one health problem.

In observational studies, researchers look at health outcomes when patients and their doctors choose the treatments. These studies often use data from electronic health records, or EHRs. Researchers can apply propensity score, or PS, methods to look at different groups of patients. With PS methods, researchers create groups of patients with similar traits who had different treatments. But PS methods require researchers to have data on all patient traits that could affect how well the treatment works. With EHR data, data on some patient traits, like health problems, may be missing. Using current PS methods in observational studies may lead to biased results.

In this study, the research team created new guidance for using PS methods with EHR data to look at the effects of treatment in different groups of patients. The team also created and tested new PS methods to make groups of patients with similar traits.

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.

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.

A randomized controlled trial, or RCT, is often the best way to learn if one treatment works better than another. RCTs assign patients to different treatments by chance. But RCTs are not always feasible. In such cases, researchers can use observational studies. In observational studies, researchers look at what happens when patients and their doctors choose the treatments. Traits such as age, gender, or health status may affect treatment choices. These traits may also affect patients' health, making it hard to know if changes in patients' health are due to treatment or to patient traits.

To figure out whether changes in patients' health result from treatment or something else, researchers use statistical methods. Two of these methods are:

• Propensity score, or PS. PS methods compare the health of patients who have similar measured traits but received different treatments. These traits are in patient health records.
• Instrumental variable, or IV. IV methods account for things that may affect treatment choice and patients' health but aren't in the patients' health records, such as personal preference about treatment.

But existing PS and IV methods don't work well when data sets include a lot of traits and health conditions for each patient. Such data sets are called high-dimensional data. In this study, the research team created and tested one PS method and one IV method for use with high-dimensional data.

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.

Comparative effectiveness research compares two or more treatments to see which one works better for certain patients. This research may include randomized controlled trials, or RCTs, in which researchers assign patients to one of the treatments by chance.

A patient may enroll in an RCT when, based on current knowledge of that patient's traits, the treatments being tested have about the same chance of helping. If one treatment is known to have a better chance of helping a patient, then the patient would not enroll and would receive that treatment from the doctor.

Sometimes there isn't enough research to show if one treatment has a better chance of helping than another. In this case, researchers may use computer programs. The programs estimate how well different treatments work in patients with certain traits. For example, a person's age and pain level may affect how much a treatment helps.

These programs would be useful for patients with knee osteoarthritis. Not many RCTs have compared total knee replacement surgery with other treatments such as medicine or physical therapy.

In this study, the research team made a computer program for patients with knee osteoarthritis. It uses data from electronic health records. The program could help identify patients for whom

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

The 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.

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

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.

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.

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.

The Veterans' Pain Care Organizational Improvement Comparative Effectiveness (VOICE) Study was a 12-month pragmatic randomized comparative effectiveness trial. The target population was primary care patients (n=820) at ten sites within the United States Department of Veterans Affairs (VA) Health Care System with moderate to severe pain despite treatment with moderate or high-dose long-term opioid treatment (LTOT). The study's primary aim was to compare integrated pain team (IPT) versus pharmacist telecare collaborative management (TCM) for improving pain and reducing opioid use among patients with chronic pain prescribed LTOT. In TCM, a lower-intensity intervention, a clinical pharmacist provides care management, structured symptom monitoring, and pain medication optimization. In IPT, a higher-intensity intervention, an interdisciplinary clinician team delivers care using a multi-modal approach to target biopsychosocial contributors to pain and disability with emphasis on non-drug therapies and behavioral activation sessions.

All participants were randomized to receive either IPT or TCM interventions for 12 months. Common elements of both interventions included individualized pain care and opioid taper recommendations tailored to patient preferences and treatment goals. Outcomes were assessed every three months and the primary time point for comparisons was 12 months. The primary outcome was pain, assessed with the Brief Pain Inventory (BPI) total score, with secondary outcomes as opioid daily dose and quality of life measures.

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.

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.

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.

As people age, medical decisions become more complex, including conversations about cancer screening. For patients aged 76-85, the United States Preventive Services Task Force (USPSTF) advises clinicians that decisions about colorectal cancer (CRC) screening should be individualized based on overall health and prior screening history (C recommendation). However, studies find that many older adults are not well-informed about, nor meaningfully engaged in, decisions about whether to continue CRC screening. Shared decision making (SDM) has been shown to improve the quality of decisions about initiating cancer screening but little is known about its effectiveness for decisions about stopping interventions. This study addresses a gap in the understanding of how to support clinicians and older patients in making good decisions about whether to continue CRC screening or not.

The researchers conducted a comparative effectiveness trial that randomly assigned clinicians at participating academic and community practices to one of two different decision support strategies. The first strategy (Registry arm) took a population health management (PHM) approach and used a patient registry to identify and track use of CRC screening among older adults for each clinician. The second strategy enhanced the registry by adding a multi-faceted SDM training program for clinicians (SDM Skills arm). The researchers enrolled patients of participating primary care providers (PCPs), aged 76-85, who are due or overdue for CRC screening, and survey them shortly after an office visit to determine the impact of the two strategies on outcomes of importance to patients. The study randomly assigned about 60 participating PCPs to the SDM skills or Registry arms, and enroll about 500 of their eligible patients.

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.

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.

One way to see if a treatment works is to compare data from people who received the treatment with data from those who didn't or who received a different treatment. But sometimes the ways that people differ, such as their age or other health problems, can bias results. For example, if the people who didn't get the treatment are older or sicker than people who did get the treatment, results could suggest that the treatment works better than it really does.

One way to avoid this type of bias is to use case-only study designs. Case-only studies compare each patient's health before and after treatment. But case-only studies often report the relative risk of a health event, such as stroke, among two groups of patients, instead of the absolute risk. For example, relative risk can show how the risk of stroke differs between patients who smoke and those who do not. Absolute risk would give the percentage of patients having a stroke among all patients. Absolute risk can help inform treatment decisions. But methods to measure absolute risk in case-only studies are limited. Also, clear guidance is lacking on how to best design and analyze a case-only study.

In this study, the research team created a guide and new methods for designing and analyzing case-only studies.

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).

Sometimes patients don't tell their doctors about all of their symptoms. For example, they may not tell their doctors about having low energy or not sleeping well. These symptoms can be signs of a health problem. Knowing about symptoms can help doctors find ways to help patients feel better. Using patient-reported outcomes (PRO) surveys is one way for doctors to collect this information. These surveys ask how health problems and their treatments affect patients from the patients' point of view. Filling out PRO surveys helps patients tell their doctors how they are feeling.

The research team wanted to know if giving doctors information from their patients about symptoms at the start of an office visit would lead to the patients feeling better. The team collected information from patients about their symptoms using PRO surveys. The surveys tracked five common symptoms: sleep problems, pain, anxiety, depression, and low energy.