Comparative Effectiveness of Anti-TNF in Combination with Low Dose Methotrexate vs Anti-TNF Monotherapy in Pediatrics Crohn's Disease (COMBINE), United States, 2015-2022 (ICPSR 38680)
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.
Design and Methodological Improvements for Patient-Centered Small n Sequential Multiple Assignment Randomized Trials (snSMARTs) in the Setting of Rare Diseases [Methods Study], 2016-2020 (ICPSR 39636)
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.
Engaging Patients and Caregivers Managing Rare Diseases to Improve the Methods of Clinical Guideline Development [Methods Study], United States, 2016-2020 (ICPSR 39626)
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.
Improving Trial Design and Analysis for Treatments for Rare Diseases [Methods Study], 2020 (ICPSR 39118)
A rare disease is one that affects fewer than 200,000 people in the United States. Because few people have these diseases, clinical studies on treatments can be hard to conduct. One way to study rare disease treatments is with an small n sequential multiple assignment randomized trial (snSMART) study.
snSMART studies have two stages. In the first stage, researchers assign patients to a treatment by chance. In the second stage, patients may stay with the same treatment or switch treatments. Patients stay on the same treatment if it's working well. If the treatment isn't working, researchers assign patients by chance to a new treatment.
snSMARTs can help researchers learn more from a smaller number of patients than a standard clinical study. But most current methods for analyzing snSMARTs use data only from the first stage, which can lead to inefficient results.
In this project, the research team developed and tested new methods that use data from both stages to analyze snSMARTs. The team compared results from the new methods to actual treatment effectiveness to see:
- Bias, or whether results are too high or too low
- Efficiency, or how big the difference is between the results and actual treatment effectiveness
This study contains two supplementary documentation files. There is no data included in this release.
Methods for Comparative Effectiveness and Safety Analyses in a High-Dimensional Covariate Space with Few Events [Methods Study], 2013-2017 (ICPSR 39486)
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.
Patient Centered Adaptive Treatment Strategies (PCATS) Using Bayesian Causal Inference [Methods Study], 2015-2020 (ICPSR 39520)
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.
Propensity Score-Based Methods for Clinical Evaluation Report (CER) Using Multilevel Data: What Works Best When [Methods Study], 2014-2019 (ICPSR 39574)
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.
Validating and Generalizing Personalized Treatment Rules by Leveraging Different Data Sources [Methods Study], United States, 2019-2022 (ICPSR 39735)
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.