CANSSI’s Collaborating Centres Train Future Statisticians to Make Sense of Big Health Data

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Data is playing an increasingly central role in creating new knowledge and tools in medicine and health. Whether it’s being used to help emergency doctors make smart split-second decisions or finding links between genes and life-threatening diseases, data can be a vital asset – but only if we know how to make sense of it.

To help aspiring statisticians and data scientists to do exactly that, the Canadian Institute for Statistical Sciences (CANSSI) has launched a network of training centres across Canada, called Health Science Collaborating Centres (HSCCs).

HSCCs are designed to help graduate students build the skills they need to analyze data – a skill that is especially important in health sciences where mistakes in interpreting data can have devastating consequences for people’s lives.

“CANSSI created a network of statisticians, working in health science,” says CANSSI Scientific Director Nancy Reid. “Our network told us that they would love to create experiential learning experiences for their graduate students. We thought that’s a great idea and decided to support that in any way we can.”

Tony Fu, a recent graduate of the University of Calgary, is one of the young researchers who jumped at the opportunity to hone his research skills through one of CANSSI’s Health Science Collaborating Centres, the Rocky Mountain Data Science Network (RMDSN) at the University of Calgary Biostatistics Centre.

Fu’s journey to medical research and biostatistics – applying statistical methods in medicine, biology and public health – is a personal one. After suffering from chronic headaches for years without doctors being able to help him, he decided to become part of the solution and transitioned from psychology to statistics and medicine.

“When you deal with any type of chronic pain disorders, navigating the health care system can become a really frustrating situation for both sides. Patients want an answer and doctors want to help but don’t know how,” Fu says. “That’s why I think medical research is so important. It can help find those answers and give people hope.”

Soon after starting his statistics program, he began looking for opportunities to put his data analysis knowledge into practice. Through the RMDSN’s internship program, he was offered a four-month summer internship at the University of Calgary Glans-Look Lung Cancer Research program.

The internship allowed him to work on real-world data for the first time and – thanks to his interest in artificial intelligence – apply machine learning techniques to large sets of health data.

“Training and mentorship are especially important when you’re just starting out,” Fu says. “The internship really gave me a kickstart. I feel more confident applying my skills in my work, and I’m confident now that I can find a job.”

In addition to teaching technical skills, the Data Science Network also makes sure that employees teach their interns other essential skills when working in a collaborative, multi-disciplinary environment.

“We want interns to be research-ready, but we also want them to employment-ready,“ says Karen Kopciuk, a senior member at the centre, who has played an instrumental role in carefully matching interns with organizations that place value on mentorship and experiential learning.

“The hard skills are important, but so are the soft skills. Especially when working in a collaborative environment, researchers have to learn how to communicate well. They have to be able to translate complex results and ideas to people with different backgrounds.”

Osvaldo Espin-Garcia, a PhD student in biostatistics, confirms that working in a multi-disciplinary environment was an eye-opening experience for him. He joined the training program at the Collaborating Centre for Statistical Omics at the Lunenfeld-Tanenbaum Research Institute, a leading biomedical research Institute at Mount Sinai Hospital, and another one of CANSSI’s Health Science Collaborating Centres.

“The program was an amazing opportunity to meet top-notch international researchers,” says Espin-Garcia.

“As trainees, we were encouraged to dabble in areas other than just statistics. I took courses in genetics and epidemiology to learn about how researchers in those fields think and what problems they face. It’s really given me a more rounded, interdisciplinary perspective.”

Collaborative research is at the core of CANSSI’s work, says Reid, adding that the explosion of available data – and the need to make sense of it – has created tremendous opportunity for collaboration between statisticians and scientists. The overwhelmingly positive response to CANSSI’s call for proposals for HSCC’s confirms her theory.

“This pilot project has succeeded beyond expectations,” says Reid. “We were able to quickly establish 11 HSCC’s and the care and hard work with which people developed this network just blew us away.”

Through the recently established CANSSI-Ontario at the University of Toronto, Reid hopes that CANSSI will continue to expand its leadership role in health data sciences:

“With the explosion of health data science, the sky is the limit.”

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