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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.

Researchers often have trouble collecting complete information on patient health, as patients may receive care at different places. Linking patient records from different places may help researchers get a more complete picture.

One way to link records is through personal information, such as names and birth dates. But this method increases risks to patient privacy. Another way, known as privacy-preserving record linkage, or PPRL, masks personal information. But current PPRL methods only work when linking entire sets of patient data, including data that have already been shared and linked. Linking entire data sets takes a long time. Also, sharing the same records multiple times increases data privacy risks.

In this study, the research team developed and tested a new PPRL method called incremental PPRL. This method links only new or updated data rather than re-linking entire data sets.

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.

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.

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.

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.

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.

Some research studies, called clinical trials, test treatments to see if they are safe and effective for patients. Before patients enroll in a trial, researchers ask patients for informed consent. In informed consent, a doctor or researcher explains what the trial is about and the benefits and risks of taking part. Patients then choose whether to enroll in the trial. If a patient is too sick to decide, a surrogate, such as a family member or friend, can decide on the patient's behalf.

Trials that test treatments in health emergencies, such as heart attack or stroke, may need a different informed consent process. Emergency situations can be stressful, and patients may have little time to learn about the trial.

In this study, the research team worked with patients and surrogates who had experience with informed consent for trials in health emergencies. They created a new informed consent process to use for trials about stroke and heart attack.

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.

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 best for which patients. Information from health insurance claims could be useful for this type of research. These claims include data on how well patients respond to treatments. But many things--not just treatments--affect whether patients' health improves.

How well patients respond to treatments could depend on patients' ages or medicines they take. It could also depend on how many health problems a patient has and how severe the problems are. Also, a doctor may suggest one treatment instead of another because of a patient's personal situation and health. Researchers need ways to figure out whether changes in patients' health result from treatment or something else.

Comparing treatments is hard in small studies with only a few patients. When there are few patients in a study, researchers can study only a few events. An event is an outcome related to the health problem or treatment researchers are studying. When there are few events and many things that could affect treatment results, it is hard to figure out what causes changes in patients' health. To address this problem, researchers use different statistical methods to account for all the things that could affect treatment results. But researchers don't know which methods might work best in studies with few events. In this study, the research team compared several methods to see which ones worked best.

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

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

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.