Lead: Hubert Wong
A clinical trial is a research study that prospectively assigns humans to one or more intervention(s) to evaluate the effects on health outcomes (World Health Organization, 2020). Traditionally, a trial is conducted in an idealized setting to give an intervention its best chance to demonstrate a beneficial effect and often involves: narrow patient populations, well-controlled settings, interventions delivered by experts, close monitoring during study follow-up, and emphasis on one primary outcome (often clinical efficiency).
A real-world clinical trial (also called a pragmatic trial) is a trial intended to answer how well interventions work beyond the traditional clinical trial setting. It seeks to include broad patient populations, deliver interventions in usual care settings using minimal extra resources, and evaluate multiple outcomes that are important to patients.
Consulting with researchers, policy makers, and practitioners, this Cluster:
Browse the priorities and projects below, or use the interactive diagram to the right.
Browse the priorities and projects below.
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What statistical methods methods are available for designing and analyzing pragmatic trials?
Who should be required to provide consent for cluster randomized controlled trials? Clinic or hospital administrators? Doctors and other care team members involved? Patients?
Pragmatic clinical trials, by their nature, capture complicated real-world settings. We are still developing methods to analyze this complex data accurately.
This project studied ways to account for data analysis where patients only receive part of the treatment—such as “partial treatment,” when a patient has to stop receiving treatment part way through the study.
Measuring patient-oriented intervention effects can be difficult due to limited sample sizes, low compliance rates, small to moderate effect sizes.
This project aimed to help with the science of how we efficiently and robustly measure these interventions effects, by developing a novel CACE approach—the MCACE model.
To measure work productivity loss data, we often use complicated statistical methods.
What are the best methods to use? And how can we communicate the results of these methods to patients and caregivers in more understandable ways?
The project, Increasing Statistical Efficiency in RWCTs asked: can we make medical research studies more efficient?
The team has published a paper exploring some of their findings in Computer Methods and Programs in Biomedicine: “CRTpowerdist: An R package to calculate attained power and construct the power distribution for cross-sectional stepped-wedge and parallel cluster randomized trials” via ScienceDirect.
The team behind Evidence Synthesis of Pragmatic Clinical Trial Methodology, including Cluster leads Hubert Wong and Rick Sawatzky, have published a paper based on their work: a scoping review that focuses on methodology for unequal cluster size CRTs.
Read about “Methods for dealing with unequal cluster sizes in cluster randomized trials: A scoping review” in PLOS ONE.