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.

September 2020 | Publication

June 2020 | Publication

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).

 

 

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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

Projects are coded by colour to the themes listed above.

Embedding Patient Values in Randomized Control Trials: A Case Study

Project Leads: Joel Singer and Nick Bansback 

It isn't easy for to figure out which treatment is best for a medical condition or illness. Usually, researchers compare different options for treatment by looking at the results of the treatments on more than one factor or outcome (e.g. pain and stiffness and fatigue). When these outcomes are grouped ... read more

Developing & Evaluating Causal Inference Methods for Pragmatic Trials

Project Lead: Mohammad Ehsanul Karim 

In medical research, to find out whether a treatment works for a disease typically depends on comparing the results of two groups of people; those who get the treatment versus those who do not, ideally in a clinical trial. To avoid bias in results, researchers who design clinical trials make ... read more

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How to analyze and present work productivity loss due to health problems in clinical trials?

Project Lead: Wei Zhang

Health problems can have an adverse impact on work productivity of patents and their caregivers. Patients and caregivers might have to stop working, reduce their routine work hours, miss work days, or not able be perform their work at their full capacity. Work productivity loss has been considered as an ... read more

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Ethical Design and Data Integrity for Cluster Trials: A Framework

Project Lead: Anita Ho & Kip Kramer

Summary coming soon!

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Evidence Synthesis of Pragmatic Clinical Trial Methodology

Project Lead: Hubert Wong

Clinical trials are used to test new treatments, interventions, or tests on participants in order to determine their effects. The beginning of every clinical trial is its design, which involves making decisions about how participants will be assigned to treatment groups, how many participants are needed, and how the ... read more

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Increasing statistical efficiency in RWCTs

Project Lead : Hubert Wong

The goal of this project is to develop and test new ways of designing and analyzing medical research studies so that they are more efficient by needing fewer participants and less resources. Two different methods of increasing efficiency will be used. The first method will be to improve how research teams ... read more

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Improving the efficiency and robustness of statistical inference for patient-oriented treatment effect in real-world clinical trials

Research Lead: Hui Xie 

The randomized clinical trials (RCT) is the preferred study design for assessing causal effects of medical interventions. A patient and their treatment decision makers are often interested in intervention efficacy that informs what to expect when the patient actually complies with treatment. In many real-world ... read more

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).