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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.

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.

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 individual traits such as age or gender. Knowing how different traits can affect a patient's response to treatment can help doctors and patients make better treatment decisions. For example, this information can help doctors know what types of cancer medicines work better for certain patients. This project focuses on improving the methods that researchers use to compare how treatments work for different patients.

In this project, the research team developed and tested a statistical method called random forests, or RF. RF is a way to analyze data using a technique called machine learning. In machine learning, computers use data to learn how to perform different tasks with little or no human input. Many types of RF methods exist. The team compared multiple RF methods to learn how well the methods would work to find out how patients with different traits respond to the same treatment.

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

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."

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.

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

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.

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.

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.

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.

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.

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.

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.

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

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

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.

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.

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.

Many research studies seek to learn how treatments affect patients' quality of life. Quality of life includes mood and energy. It also includes how people view their roles in their families or communities and whether they can perform those roles. Researchers use surveys to ask about patients' quality of life. But patients may answer the same question differently depending on different characteristics, such as their age or where they live. Some patients may think about their work roles while others may think about their families or social lives. How patients think about quality of life can affect what researchers learn about the effects of treatment.

In this study, the research team tested two surveys they created to measure differences in how patients think about their quality of life. The first, long survey had 74 questions, and the second, short survey had 23 questions.

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 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.

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

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

• origami
• varimpact
• opttx

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

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

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

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

Propensity scores (PS) and instrumental variables (IV) are methods used to assess treatment effects in observational studies when randomized controlled trials (RCTs) are not feasible. However, these methods have limitations, especially when using high-dimensional data, or data with numerous variables or many non-linear and interaction terms. Choices on which variables and non-linear and interaction terms to include may lead to model misspecification. The objective of this study was to develop and test a set of PS and IV methods that account for model misspecification when estimating causal effects of treatments using high-dimensional data.

First, the research team created the two new methods for use with high-dimensional data. The team then used a computer program to create test data that look like real patient data. The team applied the new methods to the test data. Next, the research team applied the new methods to real data from previous studies. They applied the PS method to data from Connors et al. (1996) and applied the IV method to data used by Card (1995). Using both test and real data, the research team compared findings from the new methods with those from existing PS and IV methods and checked to see if findings from the new methods were accurate when including different patient traits and health conditions in the analysis.

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

• For R version 4.2, created April 24, 2022 (Windows, r-oldrel)
• For R version 4.3, created October 20, 2023 (Windows, r-release)
• For R version 4.4, created March 14, 2024 (Windows, r-devel)
• For R version 4.2, created April 1, 2023 (Mac, arm64, r-oldrel)
• For R version 4.3, created April 6, 2023 (Mac, arm64, r-release)
• For R version 4.3, created April 11, 2023 (Mac, x86_64, r-release)