Use of Causal Diagrams in Variable Selection for Causal Observational Studies
This webinar is part of the Advanced Methods Webinar Series
Deciding which variables to adjust for when addressing causal questions in observational studies can be challenging. For example, lack of adjustment for some variables might lead to sub-optimal control for confounding whereas overadjustment for other variables can in fact introduce bias to a study.
Testimonial - PHDA 04 Spatial Epidemiology and Outbreak Detection
"Both the PHDA 03 and PHDA 04 courses have made me confident working in ArcGIS, and applying principles of GIS/mapping to population health data and working on spatial analyses. In particular, what has been most helpful about these courses is applying the background theory to surveillance and research questions and walking through the analysis from start to finish, including interpretation of results."
Testimonial - PHDA 03 Population Health and Geographical Information Systems
"Both the PHDA 03 and PHDA 04 courses have made me confident working in ArcGIS, and applying principles of GIS/mapping to population health data and working on spatial analyses. In particular, what has been most helpful about these courses is applying the background theory to surveillance and research questions and walking through the analysis from start to finish, including interpretation of results."
Testimonial - PHDA overall course
"Strengths of the PHDA courses included hands-on application of concepts (through the labs), responsive and supportive instructors, and reading materials that helped with learning the concepts. For example, the final projects in PHDA 03 and PHDA 04 allowed us to develop a practical research question, clean and analyze the required datasets, produce maps and analyses, and interpret results, which would mirror a typical project in the workplace."
Testimonial - PHDA, Samantha Salter
- How did you learn about the program and what motivated you to enroll in the PHDA course(s) you chose?
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I learned about the program through emails from PopDataBC. I have been following PopDataBC since the organization was introduced to me during my MPH degree at UBC. I was motivated to enroll in the PHDA courses that I chose, because I was not offered the opportunity to take such courses during my MPH.
Testimonial - PHDA 04 Spatial Epidemiology and Outbreak Detection
"The labs and the access to the SRTL were the biggest strengths of these courses. The SRTL had all the software, and all the data, and was really easy to access, and was well maintained and organized. The labs were applied, and had very tangible learning outcomes associated with them. They were practical in purpose, and effective in implementation via the SRLT. The labs were very smooth and impactful."
Testimonial - PHDA 03 Population Health and Geographical Information Systems
"I developed an understanding of not only how descriptive statistics can be geographically interpreted, but also how inferential statistics can be geographically interpreted. It taught me how to begin thinking spatially in-terms of patterns of health outcomes – neighbourhood make up, neighbourhood location, infrastructure, environmental concerns, etc."
Testimonial - PHDA, Allyson Rayner
- How did you learn about the program and what motivated you to enroll in the PHDA course(s) you chose?
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My MSc supervisor recommended them based on the learning goals I had for my Master’s degree. UBC doesn’t offer anything like it, so we needed to look for partner institutions.
Development of a prognostic prediction model to estimate the risk of multiple chronic diseases
This webinar is part of the Power of Population Data Science Series
Methodological complexity may be a barrier to developing tools for multimorbidity risk prediction. Prognostic prediction models estimate patients’ risk of disease incidence, typically for a single disease independent of other chronic diseases incidence.
