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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.

The main objective of this pragmatic randomized clinical trial (PCT) is to identify the optimal dose of aspirin for secondary prevention in atherosclerotic cardiovascular disease (ASCVD). A total of 15,076 high-risk patients with a history of a myocardial infarction (MI) or documented ASCVD were randomized in a 1:1 ratio to receive 81 mgs versus 325 mgs of aspirin every day. This trial addressed the following specific aims:

1. To compare the effectiveness of two doses of aspirin (81 mg and 325 mg) in reducing a composite of all-cause mortality and hospitalization for nonfatal MI, or nonfatal stroke, and the primary safety endpoint of major bleeding. Secondary endpoints include the components of the primary endpoint and hospitalization for transient ischemic attack, unstable angina, or coronary revascularization procedures.
2. To compare the effects of aspirin in selected subgroups of patients by sex, age, race, Internet users vs. non-users, and those with diabetes or advanced chronic kidney disease (CKD).
3. To develop and refine the infrastructure for PCORnet to conduct multiple comparative effectiveness trials in the future.
4. To explore biological mediators of heterogeneity of response to aspirin and of impact on clinical events.

The COMBINE study was a longitudinal examination of pediatric Crohn's Disease (CD) patients in the United States with data collected from 2015-2022. This study was a randomized, double blind, placebo controlled pragmatic trial to compare low dose oral methotrexate versus a placebo in children with Crohn's disease initiating anti-TNF (tumor necrosis factor) therapy with Infliximab or Adalimumab. Eligible participants were randomized with a 1:1 allocation and followed for a minimum of 12 months and maximum of 36 months in the context of routine clinical care. The primary outcome was a composite of indicators of treatment failure and/or toxicity. Secondary outcomes included patient reported outcomes of pain interference and fatigue.

Crohn's disease (CD) is a chronic inflammatory bowel disease (IBD) that affects approximately 600,000 Americans with estimated direct costs of $3.6 billion annually. Typical symptoms (e.g., abdominal pain, bloody diarrhea) result in substantial morbidity, including hospitalization and surgery, missed work and school, and diminished quality of life. The primary treatment goals for all CD patients are to induce remission by eradicating intestinal inflammation and related symptoms and maintain remission by preventing disease flares and progression. Additional treatment goals for pediatric CD include restoring physical and emotional development.

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."

Severe acute respiratory syndrome coronavirus 2 associated disease (COVID-19) is caused by a novel betacoronavirus, SARS-CoV-2, that was first isolated in January 2020 and has since caused a global pandemic unseen in decades in cases and mortality. At the time of initial protocol submission in April 2020, human vaccine clinical trials had just begun and experts predicted that a vaccine would not be available until April 2021 at the earliest. Therefore, new measures remained needed to prevent the spread of disease. In vitro studies suggested a potential moderate antiviral effect of hydroxychloroquine (HCQ).

This study aimed to evaluate the efficacy of HCQ to prevent COVID-19 clinical infection and to prevent viral shedding of SARS-CoV-2 among healthcare workers (HCWs), as well as to evaluate the safety and tolerability of HCQ. Participants prescreened through the Healthcare Exposure Response and Outcomes (HERO) Registry across 34 U.S. clinical centers were randomly assigned to take a placebo (n=676) or HCQ (n=683) for 30 days, with in-person clinic visits at baseline and 30 days, and an end-of-study virtual visit at 60 days. This collection contains analysis (DS1 through DS6) and tabulation (DS7 through DS44) data and accompanying documentation.

Clinical notes in electronic health records, or EHRs, can help researchers study treatments. For example, EHR notes may contain information about whether patients take their medicines as directed. But it takes researchers a lot of time to find this information.

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 current NLP methods don't work well to find and label text about medicine use.

In this study, the research team created and tested a new NLP method to find and label EHR notes on patients' medicine use.