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If patients don't take medicines as directed, the medicines don't work as well for treating a health problem. It may also lead to more health problems. If doctors knew which patients were less likely to take medicines as directed, they could find ways to help these patients.

In this study, the research team wanted to learn if knowing who took medicines as directed in the past would predict if patients take a new medicine as directed. The team created two statistical models to predict if patients would take a medicine as directed. First, the research team created a model to predict if patients would take medicines to lower cholesterol. Then, they created a second model using data from these patients plus others who were taking medicines to lower blood pressure or strengthen bones.

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.

Patient decision aids help people choose between two or more healthcare options based on what is most important to them. Involving users, such as patients and clinicians, in developing decision aids may make them more useful.

User-centered design is a way to get users involved in creating products. Learning from projects that apply user-centered design may suggest ways to involve users more in developing patient decision aids. In this study, the research team reviewed studies about developing decision aids and studies about user-centered design.

When choosing a treatment, doctors often look at research results that show how well the treatment worked in large groups of people. But many factors can affect how well a treatment works for an individual patient. These factors may include the patient's sex, age, other health problems, or how they responded to treatments in the past. Some patient data sources, such as electronic health records, have this information. But existing statistical methods may not use these data well. For example, existing methods may not be able to take advantage of data that include measurements of a patient's health from more than one point in time.

For this project, the research team developed new methods to analyze data that includes measurements of a patient's health from different points in time. To develop the new methods, the team used a Bayesian approach. Bayesian approaches include findings from previous studies in the analysis, which can make results more accurate.

To access the software and methods, please visit the Neuroconductor website and neuroc_travis GitHub.

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

Using data from all three waves of the Project on Human Development in Chicago Neighborhoods (PHDCN), this secondary data analysis examined the long-term effects of intimate partner violence (IPV) exposure during childhood and adolescence on subsequent externalizing behaviors (i.e., delinquency, violence, and substance use related offending).

The research questions for this study were as follows:

1. Are there significant differences in the mean scores of different externalizing behaviors (measured as "offending" in the present study) in any of the three PHDCN waves between youth exposed to IPV and youth not exposed to IPV?
2. Are there distinct developmental trajectories of externalizing behaviors among youth exposed to IPV when compared to those not exposed to IPV?
3. How do different individual- and neighborhood-level variables act in predicting the developmental paths of externalizing behaviors among youth exposed to IPV?

Propensity score matching (PSM) was employed to match individuals reporting IPV exposure with those not exposed to IPV on key variables. Longitudinal latent class analyses (LLCA) were utilized to estimate the longitudinal developmental trajectories of externalizing behaviors independently for IPV and non-IPV exposed males and females and compared to each other. Multinomial logistic regression models were estimated separately for males and females exposed to IPV during their childhoods to examine the effect of different hypothesized class membership predictors.

This collection contains a master dataset primarily sourced from Emery's (2006) data augmentation along with key variables from all three waves from the PHDCN Longitudinal Cohort Study, cohorts 12 and 15 (DS1); datasets constructed solely for multinomial logistic regressions for youth exposed to IPV, separated by sex (DS2 and DS3); data for the final LLCA models separated by sex and exposure to IPV (DS4 to DS7); and probabilities and latent classes created using Mplus (DS8 to DS9) that can be merged to the multinomial regression data using the SUBID variable. Additionally, syntax for variable and model constructions, as well as Mplus output, have been included as a zip package. Please refer to the P.I. documentation for more information.

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.

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.

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.

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

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

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

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.

The National Spinal Cord Injury Statistical Center (NSCISC) is operated by the University of Alabama at Birmingham Department of Physical Medicine and Rehabilitation through funding from the National Institute on Disability, Independent Living, and Rehabilitation Research (NIDILRR). NSCISC supports and directs the collection, management, and analysis of the world's largest and longest spinal cord injury (SCI) research database. Organizationally, NSCISC is currently at the hub of a network of 14 NIDILRR-sponsored and 5 subcontract-funded Spinal Cord Injury Model Systems located at major medical centers throughout the United States. In addition to maintaining the national SCI database, NSCISC personnel conduct ongoing, database-oriented research. NSCISC produces annuals reports and "Facts and Figures at a Glance" which can be accessed here.

The National Spinal Cord Injury Database has been in existence since 1973 and captures data from an estimated 6% of new SCI cases in the U.S. Since its inception, 28 federally funded SCI Model Systems have contributed data to the National SCI Database. As of March 2016, the database contained information on 31,645 persons who sustained traumatic spinal cord injuries. To assure comparability of data acquired by personnel in various centers, rigid scientific criteria have been established for the collection, management, and analysis of information entered into the database. National Spinal Cord Injury Statistical Center staff has also developed quality control procedures that further enhance the reliability and validity of the database.

Within the scope of the Spinal Cord Injury Model System program, the purposes of the National SCI Database are as follows:

1. to study the longitudinal course of traumatic SCI and factors that affect that course;
2. to identify and evaluate trends over time in etiology, demographic, and injury severity characteristics of persons who incur a SCI;
3. to identify and evaluate trends over time in health services delivery and treatment outcomes for persons with SCI;
4. to establish expected rehabilitation treatment outcomes for persons with SCI; and
5. to facilitate other research such as the identification of potential persons for enrollment in appropriate SCI clinical trials and research projects or as a springboard to population-based studies.

The Database, however, is not intended to study the effectiveness of model systems care as compared to other systems of health care delivery. It is also not by itself intended to gather and maintain population-based data on spinal cord injuries.

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.

With the growing popularity, technological ease of using propensity score modeling (PSM), and the concern over its reliability and validity among scholars and practitioners, the researchers aimed to answer whether PSM methods can replicate the results from randomized controlled trials (RCTs). In this secondary data analysis, the researchers gathered the datasets of 10 publicly available and restricted RCT studies from the National Archive of Criminal Justice Data (NACJD), introduced an artificial selection bias into the treatment groups of these investigations, and then used each PSM technique to remove this selection bias. The team then compared the results generated from the PSM methods to those derived from the original RCT experiments, and meta-analyzed the findings across all studies to reveal the true reliability and validity of PSM in relation to RCTs using criminal justice data.

For each study used in this analysis, the researchers created SPSS syntax for variable recodes and artificial bias creation and a codebook with original study items, recoded variables, and analytic variables. (In one study, two RCTs were conducted and thus two sets of syntax and codebooks were created.) Seven text files contain the Stata and R code used to run each PSM technique. These materials have been zipped into a package and are available for restricted download. Please refer to the ICPSR README for more information.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed.

This study addressed the dearth of information about facilitators of transnational organized crime (TOC) by developing a method for identifying criminal facilitators of TOC within existing datasets and extend the available descriptive information about facilitators through analysis of pre-sentence investigation reports (PSRs). The study involved a two-step process: the first step involved the development of a methodology for identifying TOCFs; the second step involved screening PSRs to validate the methodology and systematically collect data on facilitators and their organizations. Our ultimate goal was to develop a predictive model which can be applied to identify TOC facilitators in the data efficiently.

The collection contains 1 syntax text file (TOCF_Summary_Stats_NACJD.sas). No data is included in this collection.

The primary goal of this project was to increase understanding about the mechanisms and processes of desistance from crime and drug use among current urban, largely racial minority, and increasingly women criminal offenders. This research follows former drug-involved offenders for over 20 years post-release from prison in Delaware. The project was guided by Paternoster and Bushway's identity theory of desistance (2009), which relies on the concept of identity that is theorized to provide direction for an individual's behavior. The identity theory of desistance emphasizes the individual identity as reflexive, interpretive, and as such, premised on human agency.

The project featured a multi-method design and unfolded in two phases. The sample for this study originated from a previous sample used to evaluate the efficacy of therapeutic communities in reducing recidivism and relapse for drug involved offenders released from Delaware prisons in the early 1990s. In Phase I of the present study, official arrest records were obtained for the previous sample of 1,250 offenders from 1956 to 2008 from both official Delaware Statistical Analysis Center (SAC) and National Crime Information Center (NCIC) data sources. From these data, race- and gender-specific offending trajectory models were estimated. In Phase II, in-depth qualitative interviews were conducted with 304 respondents selected from within the different offending trajectory groups. The goal of the interviews was to examine the processes and mechanisms that led to persistence or desistance from crime and drugs.

DS1 contains NCIC and Delaware SAC arrest records for the full sample in Phase I. DS2 contains demographic information and trajectory group assignment for the Phase II interview sample participants. Qualitative data are not available for this collection.

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

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.

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

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

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

Researchers can use data 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.