Evidence Synthesis Reports and Interactive Visualizations
- PCORI-AHRQ Systematic Reviews
- Rapid Reviews
Evidence Maps and Visualizations
- Social Needs Interventions to Improve Health Outcomes
- Drugs & Devices for Migraine Prevention
- Comparing Treatments for Clinically Localized Prostate Cancer
- Effect of Pelvic Floor Muscle Training on Urinary Incontinence
- The Impact of mHealth for Self-Management of Chronic Disease on Patient-Centered Outcomes
- Treatments for Fatigue in Multiple Sclerosis
- Topic Briefs
- Scoping Reviews
- Other Secondary Data Reuse Opportunities
Individual Participant-level Data (IPD) Meta-analysis
Evaluating Progestogen for Prevention of Preterm Birth International Collaborative (EPPPIC): Individual Participant Data Meta-Analysis
This approach obtains and synthesizes all relevant measured characteristics of each participant in multiple related studies. It is a powerful method to identify the ways in which treatments may have varying benefits and risks for people with different characteristics. It is a way to see whether treatments should be targeted for use in specific groups of individuals. It is also an important method to reconcile differences between studies examining the same treatments that have conflicting results. We have to date awarded one IPD meta-analysis.
Principal Investigator: Lesley Stewart, PhD, MSc
Organization: University of York
State/Country: United Kingdom
Besides IPD meta-analyses, a statistical approach called predictive analytics can be applied to previous trials or other research data to provide more-personalized results that can optimize the use of specific treatments. This approach can be helpful in studies with a diverse group of people and a wide range in individual responses to an intervention. We have to date awarded one secondary data reuse project.
Using Statistical Methods to Predict Treatment Response Based on Patients' Likelihood of Having Benefits or Side Effects from the Treatment
The objective of this study, which was completed in 2019, was to examine the value of assessing heterogeneity of treatment benefits and harms in guiding clinical choices across patients. In this study, the research team grouped people based on their likelihood of having benefits or side effects from a certain medicine.
The study team reported that grouping data on people based on their likelihood of having benefits or side effects from each medicine showed differences in how the medicines affected them. The group of patients with a low risk of breaking a bone was more likely to benefit from pioglitazone. Patients in the group less likely to benefit from anthracycline had more heart problems. The team added that researchers can use the statistical methods to better understand how likely patients are to have benefits or harms from medicines.
Principal Investigator: David Kent, MD, MS
Organization: Tufts Medical Center, Inc.
Posted: June 13, 2019; Updated: February 22, 2022
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