When patients, clinicians, or other healthcare stakeholders raise a question that doesn’t already have an evidence-based answer, sometimes new research is needed to answer it. But in other cases, a careful look at results from research studies already completed can more quickly provide findings that patients and those who care for them can use to make informed choices.
PCORI’s Evidence Synthesis Initiative takes that approach. The program includes rigorous reviews of the best evidence available on topics of critical concern to patients and other healthcare stakeholders. The goal is to synthesize all relevant completed studies on a particular clinical question in order to provide evidence that is stronger and more certain than the results of the individual studies.
Our Evidence Synthesis Initiative falls within our broader Research Synthesis Program, which takes advantage of a wide variety of tools to pull together and analyze results for public use. We are using two key approaches. One is conducting systematic reviews of results of already completed comparative clinical effectiveness research (CER) studies and other methodologic approaches that share an underlying foundational method on topics of critical concern to patients and other healthcare stakeholders. The other is reanalyzing data from already completed studies to discover which specific patient groups gain the most or least benefits—or are at greater risk for harms—from an intervention.
Once we have synthesized the evidence, we will disseminate it through products designed to meet the needs of our various stakeholder groups.
Types of Evidence Synthesis
Systematic reviews: The most common type of evidence synthesis is the systematic review. According to the National Academy of Medicine (formerly the Institute of Medicine) and PCORI, a systematic review features a set of clearly specified, rigorous, reproducible, and transparent methods. These reviews provide answers to specific clinical questions by analyzing published and unpublished results from all relevant studies on a given topic. They identify, select, and summarize findings of available research to make clear what is known about a topic—and what is still not known.
Based on input we have received from stakeholders, our initial efforts include systematic reviews of treatment options for:
- Stroke Prevention in Atrial Fibrillation Patients: A Systematic Review Update
- Psychological and Pharmacological Treatments for Adults with Posttraumatic Stress Disorder (PTSD): A Systematic Review Update
- Nonsurgical Treatments for Urinary Incontinence in Adult Women: A Systematic Review Update
- Drug Therapy for Early Rheumatoid Arthritis in Adults – An Update
Our goal is to produce up-to-date, actionable evidence to inform important healthcare choices. We’re implementing this initial effort in coordination with the Agency for Healthcare Research and Quality.
Meta-analysis: This approach uses statistical methods to combine published or unpublished study findings to produce estimates of an intervention’s effect and to illustrate how consistent and strong the effect is across the research. Meta-analyses typically summarize the average treatment effect for the entire study population.
Individual patient-level data (IPD) 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.
- Evaluating Progestogen for Prevention of Preterm-birth International Collaborative Individual Participant Data Meta-Analysis
Other secondary data reuse opportunities: 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.
Posted: October 27, 2017