Real-World Clinical Trials (RWCT) Methods Cluster

What is a Real-World Clinical Trial? 



Real-World Clinical Trials Planning Event, September, 2017

Summaries of Cluster Projects

1. Embedding Patient Values in Randomized Control Trials: A Case Study

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 together as a single ‘outcome’, scientists call this a ‘composite outcome’. Although composite outcomes help researchers get an ‘overall’ picture of what is important to patients, the picture is based on a belief that all the factors are equally important to all patients– for example, that pain is just as important as stiffness and fatigue. However, it is possible that some patients may feel fatigue is more important and for others, pain could be more important. This project will develop a way for patients to let researchers know which outcomes, in a composite outcome, are most important. The team will develop and test this new method by asking pregnant women who have high blood pressure to identify, and then rank, a list of things that affect their decision which treatment they prefer – for example, whether the risks to the baby are more important than the risks to their own health. The results of this project can then be used by researchers studying other diseases or illnesses, to more easily include patients’ ranking of what is important to their decisions about treatment options.

2. Developing & Evaluating Causal Inference Methods for Pragmatic Trials

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 sure that the people in both groups are very similar (e.g., same age, seriousness of the disease, equal length of time with the disease, etc.). Unfortunately, this type of research design often does not include patients who are the sickest, of older age, or are from different ethnic groups, and thus it is impossible to know whether the drug will actually work on these types of patients. Pragmatic trials are new kind of trial design, which aims to include these more vulnerable groups of patients. However, because these patients are less similar, it is difficult to analyze the data. Also, some patients may not be able to continue with the treatment, need to take less of the drug or have to drop out of the study.  The current ways to analyze the data ignore most of these details, and therefore the results are not very useful to a patient or a doctor in making treatment decisions. We need to know the effect of a treatment on patients who actually took the right amount of the drug for the entire study. This research project will explore new ways to analyze the results of such studies so that patients and doctors will know if a treatment will work for a patient who takes the drug exactly as prescribed by the doctor.

3. Increasing statistical efficiency in Real-World Clinical Trials

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 put patients into groups to test treatments in cluster-randomized trials. This means that, for example, patients in one hospital will all get the same way to prevent infection while all those in another hospital will get a different way to prevent infection. The second method will to improve the way used to help doctors summarize the information that is collected from other sources (e.g. from hospital records, other studies and from their experiences with their own patients) about how well the treatment works and then to combine these results with those from the research study. This will help doctors get better information about how well a treatment can work for their patient.


Dr. 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). His research focuses on clinical trial design and foundational issues in statistics, and he collaborates extensively with fellow researchers in diverse areas, including HIV/AIDS, mental health, intensive care, emergency, neurology, orthopaedics, and rheumatology. He received two degrees at UBC: a BASc in Engineering Physics in 1992, and a PhD in Statistics in 2000.


  • Methods Cluster Update: June 8th, 2018: