Improving the Use of Patient Registries for Comparative Effectiveness [Methods Study], Boston, Massachusetts, 2013-2018 (ICPSR 39476)
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.
Making the Patient-Reported Outcomes Measurement Information System Meaningful to Patients and Providers in Clinical Practice [Methods Study], United States, 2014-2019 (ICPSR 39509)
Patients and their healthcare providers, such as doctors and nurses, can use survey scores to track the symptoms of illnesses like rheumatoid arthritis, or RA, over time. Tracking symptoms in this way can help them understand if a treatment is working well for a patient.
When researchers create and test these surveys, they want to be sure that patients' survey scores match how severe patients feel their symptoms are. Researchers also want to know what changes in survey results show that symptoms have changed so much that patients might want to change treatment.
In this study, the research team had patients with RA and providers read stories that described what patients felt like with higher and lower scores of two symptoms:
- Fatigue, or lack of energy
- Pain interference, or how much pain interferes with their lives
Patients and providers decided whether each story showed a mild, moderate, or severe level of symptoms. They also gave their views about how large a change in scores would need to be to show that pain or fatigue was getting better or worse.
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.