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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.

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.

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.

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

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

Clinical 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.

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

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

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

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

Comparative effectiveness research 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.

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 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.

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.

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.

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.

Researchers can use experiments to learn about what patients prefer. Discrete choice experiments, or DCEs, describe treatments with different features, such as out-of-pocket costs or wait times. Patients fill out surveys about which treatments they prefer. From their choices, researchers learn what is most important to patients and how they think about the different features.

DCEs can be hard to design and analyze. When surveys are complex, patients may ignore information or take shortcuts, which leads to inaccurate results.

To make DCE results more accurate, researchers can

• Change the design of the DCE
• Apply statistical methods

But current knowledge of how to do this is limited. In this project, the research team looked at improving methods to design and analyze DCEs.

Factors, such as personal traits, behaviors, or the environment, can affect a person's risk of getting an illness. Doctors can use risk models, which account for these factors, to predict a person's chance of getting an illness. The risk models group patients into different levels for certain illnesses, such as high risk or low risk.

Most risk models look at only a small number of factors, which affects how well the models can separate patients into different levels. Combining factors from different studies into a single risk model may improve how well the model works. Researchers can use statistical methods to combine data from different studies. But current methods don't work when the studies look at different traits or other factors.

In this study, the research team developed a new method for combining data from studies that have information on different risk factors. The new method is called Generalized Meta-Analysis, or GENMETA.

To access the R package, please visit the Implements Generalized Meta-Analysis Using Iterated Reweighted Least Square Algorithm CRAN webpage.

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.

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

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

A patient's preference for a treatment may affect how well the treatment works. For example, if patients prefer a specific medicine, they may be more likely to take that medicine.

Traditional randomized clinical trials can't tell how much patient preferences affect how well a treatment works. But a two-stage clinical trial might. In a two-stage trial, researchers assign patients by chance to one of two groups. In the first group, researchers assign patients by chance to get a specific treatment, regardless of their preference. In the second group, patients choose their treatment. In a two-stage trial, researchers can compare health outcomes for patients who choose their treatment with patients who don't. But few methods exist for researchers to design and analyze this type of trial.

In this project, the research team developed new statistical methods for two-stage trials. The team wanted to find out how many patients are needed for two-stage trials to provide accurate results. They also wanted to learn how to measure whether patient preference for a specific treatment affects patients' health outcomes.

To access the software, methods and R package, please visit the preference CRAN webpage and preference GitHub.

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

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

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

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

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

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.

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.

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

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

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.

Clinical practice guidelines help doctors decide on treatments to recommend for their patients. Guidelines are based on research that looks at the benefits and harms of different treatments. Patient and caregiver input can improve the usefulness of guidelines. But guideline developers often rely on the input of only a few patients and caregivers.

In this study, the research team created a process for getting feedback on guidelines from larger groups of patients and caregivers. This process is called the RAND/PPMD Patient-Centeredness Method, or RPM. The team tested RPM with guidelines for Duchenne muscular dystrophy, or DMD. DMD is a severe form of muscle loss that mostly affects young boys.

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

In this study, the research team wanted to

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

The research team also wanted to test the PROMs to see

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

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.

In this project, the study team created a method of setting research agendas called Stakeholder Engagement in quEstion Development, or the SEED (Stakeholder Engagement in Question Development) Method. These agendas help identify research topics and questions that are important to study. The team tested the SEED Method at two sites in Virginia. The team wanted to learn if the method developed research agendas that reflected differing views and if people were satisfied with the process.

Using the SEED Method, the study team gathered input from three sets of people at each site:

• Research Team. Community members and staff from local colleges who worked with the study team to lead the project
• Topic Groups. Three groups of people who developed research questions. Each group had people with a type of viewpoint, such as patients, health professionals, or research funders. The groups worked independently with the Research Team.
• Stakeholder Consultants. Patients and health professionals with knowledge of the research topic who took part in interviews and group discussions

To access the methods please visit the The SEED Method for Stakeholder Engagement website.

To learn about people's experiences, researchers often use one-on-one interviews and group interviews, called focus groups. Researchers can do interviews and focus groups in person or online.

In this study, the research team wanted to learn if people shared more or different information in person versus online. The team did interviews and focus groups with women about safety during pregnancy and compared the information collected in person or online. To collect information online, the team used either online video, chat, a message board, or email. The team then compared differences in the average number of words and what women discussed across the methods.

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.

Sometimes a study can get better results using data from different sites. In these cases, researchers may want to share patient data, including personal and private information such as dates of birth and addresses. However, researchers may not want to share data across sites because of worries about patient privacy. Some statistical methods can change patients' sensitive individual data into summary data that hides individuals' personal information. These privacy-protecting methods, or PPMs, make it safe to share data across sites. But researchers don't know if PPMs produce accurate results.

In this study, the research team compared combinations of PPMs with methods that use patients' individual data.

To access the methods, software, and R package, please visit the distributed GitHub.

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.

Organizations that fund cancer research need to decide which studies to fund. Value of information (VOI) is a way to help rank research studies. VOI estimates the value of research by looking at the impacts on health and on healthcare that could result from the research.

SWOG (formerly the Southwest Oncology Group) is a network of cancer researchers funded by the National Cancer Institute. SWOG leaders review and score new study proposals based on the scientific value of the studies and their potential impact. Based on a study's score, SWOG's leadership committee decides whether to send the study to the National Cancer Institute for funding review.

In this study, the research team wanted to learn if giving VOI data to the committee affected its scoring of proposals. The team also wanted to see if providing VOI data was helpful in deciding which studies to fund. The study had two parts. The research team created a process to quickly estimate VOI. Then, the team tested the process on nine study proposals that the SWOG committee reviewed.

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

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

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.

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.

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 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.

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

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

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

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.

Currently many researchers get input on their research projects from other researchers. But researchers want their work to be more patient centered, or relevant to patients' preferences, needs, and values. To do this, researchers need a way to measure how patient centered a research project is. They also need ways to get input from patients and community members on research projects.

In this study, the research team created a scale with a set of questions to measure how patient centered input is on research projects. The team tested the scale to be sure it measured patient-centeredness reliably and accurately.

Then the research team compared two ways researchers could get input on their projects:

• Community Engagement Studio, or CE Studio, brings together patients and community members.
• Translational Studio, or T2 Studio, brings together researchers.

The team wanted to learn if there were differences in how patient centered input was from CE Studio and T2 Studio.

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.

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.

Comparative effectiveness research compares two or more treatments to see which one works better for which patients. In some studies, researchers assign patients by chance to several treatments or to have or not have a treatment. But approaches that assign patients by chance are not always suitable. For example, assigning patients to a new treatment may not be good medical care.

For this reason, researchers sometimes do studies using data collected when patients and their doctors choose the treatments. Data from such studies are observational data. When using observational data for research, it can be hard to know if the effect of a treatment is because of the treatment or other factors, such as patients' age or health history. In these cases, researchers use statistical methods to understand the effect of the treatment. Depending on the study's focus and design, some methods work better than others.

In this study, the research team developed guidance for researchers to help them choose methods for their study.

To access the methods and software, please visit the DECODE CER Tool website.

Researchers can use data from patient registries to look at which medicines or other treatments work best. Registries store data about people with a specific health problem. The data may include the health care and medicines patients receive over time and patient reports of their health status.

To find out patients' health status, registries ask patients to fill out surveys at different times during treatment. Researchers can compare survey results from when patients first take the survey with results from surveys taken after treatment. They can then find out how well a medicine works. But patients may not always take the first survey before they start a new medicine. Sometimes, they don't take the first survey until after starting treatment. When this happens, it is hard to know how well the medicine works.

In this study, the research team looked at different ways to use data from patient surveys in registries. The team wanted to learn which way would give the most accurate understanding of the effects of a new medicine. The study also looked at patients' views on taking part in registries.