Current Research Projects
Our research encompasses a diverse array of topics:
CAR-T
Paediatric Oncology Group of Ontario Seed Grant
Economic Evaluation of Chimeric Antigen Receptor T-cells (CART) Therapy for High-Risk Relapsed Acute Lymphoblastic Leukemia in Children
Acute Lymphoblastic leukemia (ALL) is the most common childhood cancer. Although most children with ALL can be cured, those that relapse will have much lower chances to survive. Stem cell transplant (SCT) is the standard therapy for these children but is associated with high death rates and long-term complications. An alternative to SCT was developed recently: chimeric antigen receptor T-cells (CAR-T) therapy. CART therapy is very effective but also very expensive; one course of therapy costs approximately $500,000 and is currently available only in the US. We propose a study to evaluate the “value-for-money” of CART therapy compared to SCT for children with high-risk ALL who have relapsed.
PLGG
Paediatric low-grade glioma (PLGG) is the most common childhood brain cancer. The personalization of treatment through genomic analysis of individual tumours has the potential to improve outcomes for PLGG patients. In this project, we are evaluating the cost-effectiveness of using tumour molecular analysis to guide treatment decisions for PLGG patients. More specifically, the avoidance of radiation therapy for a subset of patients who stand to benefit.
MIPOGG
Cancer predisposition syndromes are genetic diseases that increase the risk of developing one or more cancers over a lifetime. More than 10% of children who are diagnosed with cancer have and underlying cancer predisposition syndrome. Cancer predisposition syndromes are very difficult to recognize in children with over 125 currently known types. These patients need to be referred to Genetics in order to make the diagnosis. Our team has developed an eHealth decision-support tool, called MIPOGG (McGill Interactive Paediatric OncoGenetic Guidelines), that helps doctors recognize their patients with cancer who need to be referred to Genetics because they are at increased risk of having a cancer predisposition syndrome. This study aims to evaluate the performance of MIPOGG in detecting children with and without a cancer predisposition syndrome in a large childhood cancer patient population as well as to determine MIPOGG’s costs and effects compared with other existing cancer predisposition syndrome detection strategies. When a cancer predisposition syndrome is diagnosed, doctors can initiate tumor surveillance protocols and sometimes change the treatment of their patients’ in order to improve their quality of life and chances of survival. All children and families should have an equal chance at being identified with a cancer predisposition syndrome, independently of where they live and by whom they are treated. MIPOGG is a simple and accessible clinical decision-support tool made for doctors, benefiting paediatric oncology patients and their families.
CIHR Preterm Birth Network
Improving Outcomes for Preterm Infants and their Families: A Canadian Collaborative Network
Over the next five years, we aim to improve the delivery of care to extremely preterm infants during the antenatal, perinatal, and postnatal periods, and to increase the rate of survival without morbidity from 47% to 61%. The Network will work with families and parents who have undergone such experiences to understand what matters most to parents and where further research is needed. The Network will build a national database for the ongoing collection of maternal and infant characteristics related to preterm birth, so that we can understand who is at risk of preterm birth and what can be done to improve outcomes for babies born preterm. We will investigate the impact of some promising interventions via this Network and improve the transmission of knowledge of activities that can lead to better care and improvements for infants, families and societies at large.
SPOR Innovative Clinical Trial Multi-Year Grant
Innovation in Paediatric Clinical Trials
The aim of this project is to create resources for trial design, management and analysis that can be used by researchers across Canada to make clinical trials for children easier, less expensive and more informative. These resources will be known as KIDSCAN, a Canadian effort for safe drugs in kids. These new methods and resources will be tested in four clinical trials conducted in six paediatric emergency departments across four provinces. The medical conditions and treatments to test were chosen by researchers in paediatric emergency medicine because they are common, and include breathing problems, stomach upset, and pain from an injury or procedure. Statistical methods will be employed that will determine what information already exists that a trial can build on, and what information is still needed in order to change what doctors decide to do. We hope to show that these new methods and resources will mean doctors will have more and better information on which to base their decisions.
CIHR Catalyst Grant: Health Services and Economics Research in Cancer Control
Estimating the cost-effectiveness threshold for cancer care in Alberta
Health budgets across Canada are under pressure because of the demand for better and more care, and the introduction of new, expensive treatments. This is particularly true for cancer care. To invest in new treatments, governments may need to cut from currently provided services, since it is not sustainable to keep increasing health budgets. Some patients will likely gain health by using the new treatment, but others will lose health as their current health services are reduced to pay for the new treatment. It is important for policy makers to be able to judge whether new cancer treatments will produce more health than they displace. The cost-effectiveness threshold allows decision makers to estimate how much health is lost by reallocating money from healthcare services that are currently being paid for. Currently, no good estimates for such thresholds exist for Canada. As a result, decision makers cannot know if their funding decisions improve or harm the health of Canadians. We propose estimating a threshold for cancer care in Alberta. We will do this by looking at the effect of health spending on changes in mortality and quality of life. This will be estimated by applying advanced statistical methods to healthcare administrative data from Alberta. The results of this study will help decision makers to make investment and disinvestment decisions in cancer care that improve health.
Canadian Respiratory Research Network
Creating a Platform for Prediction of Real-World Health and Cost Consequences of COPD
Chronic Obstructive Pulmonary Disease (COPD) is a chronic disease with significant societal burden. Patients with COPD have poorer quality of life, higher comorbidity index, and shorter life expectancy, compared to those without COPD. COPD patients are also more frequent users of healthcare resources. While COPD incidence is expected to decrease, COPD prevalence is expected to increase considerably over the next 35 years in Ontario as a result of population ageing. There is little evidence on real-world costs for individual patients with a COPD diagnosis across different phases of COPD care, from diagnosis to death and no COPD phase-based cost prediction model exists in Canada. A similar gap exists on understanding the personalized risk of mortality and morbidity post-COPD diagnosis. Such knowledge is of utmost importance for policy makers and healthcare professionals to understand the present and future of COPD burden across the country. In addition, this information is essential for construction of accurate, individual-level decision models that can guide policy. We aim to create a platform to predict real-world mortality, hospitalization risk and costs across the COPD pathway for patients with a COPD diagnosis in a way that enables accurate, evidence-informed, personalized projections of the future burden of the disease.
Decision Analysis in R for Technologies and Health (DARTH)
The DARTH workgroup is a multi-institutional, multi-university collaborative effort comprised of researchers who have a passion for transparent and open-source solutions to decision analysis in health. The aim of this collaboration is to expand knowledge in decision analysis using R and develop educational material that empowers people to construct R-based decision models.