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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.

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

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

Patients, doctors, and researchers helped design the study.

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

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

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

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

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

The 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.

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

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

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

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

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

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

• Poisson3armNI
• Binary3armNI
• bayesianSWcontinuous
• SMART3armNI

Comparative effectiveness research compares two or more treatments to see which one works best for which patients. But patient traits, such as age or income, may affect patients' treatment choices. These traits may also affect patients' responses to treatments. As a result, researchers may have trouble telling whether a patient's traits, the treatment, or a mix of the two affected how well a treatment worked.

Statistical methods called matching methods can help address this problem when researchers use patient data to compare the effects of treatments. Matching methods help researchers find data from patients who had similar traits such as age or race and received different treatments. Because the patients are similar except for the treatment they receive, the differences in patients' health can more likely be credited to the treatment. Existing methods work well for comparing up to two treatments. But they may not work with three or more treatments.

In this study, the research team created two new matching methods to compare the effects of three or more treatments. The team then analyzed the new methods under different conditions to see how well each worked."

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.

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

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

Comparative effectiveness research compares two or more treatments to see which one works better for which patients. Sometimes, groups of people respond differently to the same treatment. For example, women might, on average, receive more benefit from a treatment than men do. If researchers group women and men together when they analyze study data, they may miss this difference and overlook some of the benefits of a treatment.

Researchers can analyze data on the effects of a treatment in many ways. Each way has strengths and weaknesses. Bayesian regression is one method that allows researchers to consider various factors in their analysis, such as patients' ages, sex, or health problems. This method can help researchers understand how different groups of people respond to a treatment. But it requires advanced computer programs that are not readily available to all researchers.

In this study, the research team wanted to make it easier for researchers to use Bayesian regression.

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

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.

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 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.

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.

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.

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

Patients' beliefs and expectations may affect how they respond to treatment. But these feelings are hard to measure.

In this study, the research team created a set of surveys called Healing Encounters and Attitudes Lists, or HEAL. HEAL helps researchers understand patients' beliefs and expectations about treatment. HEAL measures patients'

• Connections with their doctors and nurses
• Feelings about their doctor's office and staff
• Expectations about treatment
• Outlook on life
• Strength of spiritual beliefs
• Comfort with complementary and alternative medicine, or CAM
• The team also used the Patient-Reported Outcomes Measurement Information System, or PROMIS, to measure patients' pain, health, and function. PROMIS is a set of surveys researchers and doctors use for many diseases and treatments.

The team wanted to learn if HEAL could predict how patients respond to treatment for chronic pain. Chronic pain is pain that lasts for months or years. The team used HEAL and PROMIS to look at why some groups of patients respond differently to treatment for chronic pain. Patients got either conventional treatment, such as physical therapy or medicine, or CAM, such as acupuncture, chiropractic treatment, or massage.

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.

Comparative effectiveness research compares two or more treatments to see which treatment works better for which patients. Such research may include

• Randomized controlled trials, or RCTs. Researchers assign patients to a treatment by chance. Researchers consider RCTs to be the best way to figure out when changes in patients' health result from the treatment.
• Observational studies. Researchers study what happens when patients and their doctors choose treatments. Patient traits, such as age or health, may affect treatment choices. These traits may also affect patients' responses to treatments. Determining whether a patient's traits, the treatment, or a mix of the two affected how well the treatment worked may be difficult.

In observational studies, researchers use statistical methods to help find out whether changes in patients' health result from treatment or something else. Existing methods work well when studies look at whether treatment affects the risk of a health event, such as a heart attack. In these cases, researchers can compare how often patients had heart attacks before and after patients receive treatment. But existing methods don't work well when studies look at the risk of a one-time event, such as death.

In this study, the research team tested a new statistical method for observational studies called posttreatment event rate ratio, or PTERR, that helps figure out whether a treatment reduces the risk of death.

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.

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.

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.

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.

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.

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.

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.

Current medical guidelines recommend a type of medicine called ACE/ARBs to help patients live longer and protect their kidneys after a stroke. But studies show that rates of kidney disease have gone up at the same time that more people have been using this medicine. Additional research may help show if some patients shouldn't take ACE/ARBs after a stroke.

In this study, the research team wanted to learn about the effects of taking ACE/ARBs for patients over age 65 who've had a stroke. The team reviewed Medicare claims for stroke survivors with and without chronic kidney disease, or CKD. CKD is a health problem in which the kidneys don't remove waste from the blood well. The team compared patients in areas of the country with different rates of ACE/ARB use. The team looked at how many patients lived and how many had kidney problems over two years.

Researchers can use data on patient traits such as age, health problems, and treatment preferences, to create personalized treatment rules, or PTRs. PTRs provide doctors with guidance on how to treat patients' health problems based on their traits. But PTRs based on a single data source may not apply to all patients. For example, if researchers create a PTR using data from older people with heart failure, it may not apply to younger people with heart failure.

To avoid this problem, researchers can create PTRs by combining data from many sources. PTRs based on many data sources can help guide treatment for patients with different traits.

In this study, the research team created and tested a new method for creating PTRs using data from multiple sources.

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.