Real-World Clinical Trials


A clinical trial is research study that prospectively assigns humans to one or more intervention(s) to evaluate the effects on health outcomes (World Health Organization, 2020).  Traditionally conducted in an idealized settings to give an intervention its best chance to demonstrate a beneficial effect; often involving: narrow patient populations; well-controlled settings, interventions delivered by experts; close monitoring during study follow-up; emphasizes one primary outcome (often clinical efficiency).

A real world clinical trial is a trial intended to answer how well interventions work beyond the confines of a clinical trial setting. They seek to include broad patient populations; deliver interventions in usual care settings using minimal extra resources; evaluate multiple outcomes that are important to patients.

In reality, there isn’t a defined set of things we can do to make a clinical trial representative of everyday settings. Real world clinical trials try their best to represent everyday care settings the best they can. However, the degree to which they accurately represent everyday settings will vary depending on what they are compared to.

Latest Updates

September 2021 Publication

A screenshot of a paper in ScienceDirect.

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.

July 2021 Publication

Screenshot of article in PLOS One

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.

April 2021 Publication

Publication Alert!

 A screenshot of the paper, "Incorporating partial adherence into the principal stratification analysis framework."

Eric Sanders, Paul Gustafson and Mohammad Ehsanul Karim have published a paper based on their work on Developing & Evaluating Causal Inference Methods for Pragmatic Trials.

Read about "Incorporating partial adherence into the principal stratification analysis framework" in Statistics in Medicine.


Cluster Themes

  • Addressing real world limitations: making trials feasible and efficient in real world settings (constraints on blinding, randomization, sample size, operational procedures, ethical considerations).
  • Enhancing generalizability and individualized treatment: ensuring treatment needs in the broad population are addressed but with a focus on individual patient priorities (PROMs) and needs (precision medicine).
  • Leveraging external information sources: making use of non-trial information (published literature, health databases/medical records, expert opinion) to get answers more quickly and enhance the value of a trial.

Projects in this Cluster


Cluster Lead | Hubert Wong

Dr. Wong is seconded to the Unit from the University of British Columbia (UBC), where he is an Associate Professor at the School of Population and Public Health, Program Head of Biostatistics at the Centre for Health Evaluation and Outcome Sciences (CHÉOS), and Associate Head of Methodology and Statistics at the Canadian Institutes of Health Research (CIHR) Canadian HIV Trials Network (CTN).