{"id":16,"date":"2021-06-09T21:36:59","date_gmt":"2021-06-09T21:36:59","guid":{"rendered":"https:\/\/lab.research.sickkids.ca\/heath\/?page_id=16"},"modified":"2025-12-03T21:06:53","modified_gmt":"2025-12-03T21:06:53","slug":"our-research","status":"publish","type":"page","link":"https:\/\/lab.research.sickkids.ca\/heath\/our-research\/","title":{"rendered":"Our Research"},"content":{"rendered":"

Statistical Design of Bayesian Adaptive Clinical Trials<\/h2><\/div>
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The EMBaRC lab is developing novel statistical methodology to improve the design of Bayesian clinical trials. We focus on a range of different trial designs and goals including, dose finding studies, which aim to determine the optimal dose of use in future randomised trials, and platform trials, w aim to minimize patient burden by efficiently answering multiple research questions within a single framework. We are also developing methodology to address the computational barriers to Bayesian adaptive trial design.<\/p>\n

This methodological work is supported by CANSSI. CANSSI is Canada\u2019s catalyst for discovery and innovation in statistical sciences and for advances in collaborative research and training. CANSSI programs support the pursuit of cutting-edge collaborative research involving statistical sciences along with the communication and application of the results to science, engineering, and society. CANSSI is supported under the Discovery Institutes Support program of the Natural Sciences and Engineering Research Council of Canada.<\/p>\n

The EMBaRC lab also supports a range of applied Bayesian clinical trials to implement these methodological developments in practice.<\/p>\n

Learn more about Statistical Design of Bayesian Adaptive Clinical Trials<\/a><\/p>\n<\/div><\/div><\/div>

PRACTICAL Platform Trial<\/h2><\/div>
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PRACTICAL is a platform trial that enables phase II and III evaluation of a range of potential interventions and strategies to improve outcomes for patients with acute hypoxemic respiratory failure (AHRF). AHRF is a common, life-threatening condition that leaves patients vulnerable to lung and diaphragm injury associated with mechanical ventilation along with other nosocomial complications of critical care. The platform enrols adult patients with AHRF to evaluate various related interventional strategies. Dr. Heath leads the statistical analysis committee for the PRACTICAL trial and the IMPaCT lab contributes to the design and analysis of different research questions within this trial.<\/p>\n

Learn more about PRACTICAL Randomised Controlled Trial<\/a><\/p>\n<\/div><\/div><\/div>

Canadian Network for Statistical Training in Trials<\/h2><\/div>
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The Canadian Network for Statistical Training in Trials (CANSTAT) is a pan-Canadian, multi-institutional and multidisciplinary training platform that will provide participants with the technical skills and practical experience needed to become leaders in their field and to ensure that clinical trials generate the highest-quality evidence to improve the health of Canadians. Dr. Heath leads the education and workshops committee within CANSTAT and the EMBaRC team welcomes CANSTAT fellows to complete their training within the EMBaRC lab.<\/p>\n

The CANSTAT fellows work together with clinical and statistical experts in clinical trials and to learn through a comprehensive experiential learning program. Formal education will also be provided through workshops led by clinical trial experts from around the world, and though in-person capacity-building meetings.<\/p>\n

Learn more about CANSTAT project<\/a><\/p>\n<\/div><\/div><\/div>

Statistical Design for Paediatric Rare Diseases<\/h2><\/div>
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Out of the 7,000 known rare diseases, only about 5% have specific treatments available. Furthermore, rare diseases provide a specific challenge for clinical trial design as there only a small number of patients can be recruited into clinical trials. Bespoke statistical solutions have been suggested to improve the performance of clinical trials in rare diseases. These solutions include (i) Bayesian statistical methods that allow information from different sources to be formally integrated into the analysis, (ii) enhancing clinical trials through comparator arm data augmentation methods based on external or historical controls in regulatory submissions and (iii) using decision theoretic approaches that account for the population size. The EMBaRC lab is working to develop these methods so that they can be used to design clinical trials for Canadians. This work is performed within the RareKids-CAN network, in which, Dr. Heath co-leads the Biostatistical Methods hub.<\/p>\n

To learn more about RareKids-CAN<\/a> and the EMBaRC lab research<\/a>, click the relevant link.<\/p>\n<\/div><\/div><\/div>

Methodology for Value of Information<\/h2><\/div>
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Value of Information measures can be complex to calculate, especially for adaptive and early phase clinical trial designs. Furthermore, Value of Information measures can be challenging when considering realistic assumptions about the implementation of novel technologies or heterogeneity between different populations. The EMBaRC lab focuses on overcoming these challenging through novel statistical methodology. We also focus on improving communication for Value of Information measures and improving their use in practice.<\/p>\n

Learn more about VoI Calculation Methods<\/a><\/span><\/p>\n<\/div><\/div><\/div>

Expediting Drug Approvals in Oncology<\/h2><\/div>
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Access with Evidence Development schemes (AEDs) can support the timely adoption of novel therapies by allowing patient access to these therapies whilst mandating that additional data be collected. These additional data are analysed to support the revaluation of the therapy and potential reversal of the approval decision. Value of Information methods have been suggested to support the design of AEDs but have rarely been used. This project combines researchers and policymakers to improve the use of VoI in AEDs with the aim of expediting approvals in Canada for promising new drugs.<\/p>\n

Learn more about expediting Drug Approvals in Oncology<\/span><\/a><\/p>\n<\/div><\/div><\/div>

Collaborative Network on Value of Information<\/h2><\/div>
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The Collaborative Network on Value of Information (ConVOI) is an international network working to improve the use of Value of Information methods in clinical and public health research. ConVOI focuses on improving the use of VOI in practice through research, software development and communication. The EMBaRC team works within the ConVOI network to achieve this goal. The ConVOI network has recently published a textbook<\/a> to present definitions and methods for Value of Information measures.<\/p>\n

Learn more about ConVOI<\/span><\/a> or visit the ConVOI website<\/a><\/span><\/p>\n<\/div><\/div><\/div>

Patient Engagement in Statistical Methodology<\/h2><\/div>
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Patient engagement in research leads to improved relevance and impact for research. As such, patient partners in research are often included in health care research. However, patient engagement in methodological work, like the work of the EMBaRC lab, is less common and methods for meaningful engagement are lacking. The EMBaRC lab is working with patient engagement specialists at SickKids Research Institute to develop meaningful engagement in statistical methods research.<\/p>\n<\/div><\/div><\/div>

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