Identification algorithms and related considerations when using administrative data for epidemiology
This webinar is part of the Advanced Methods Webinar Series
Administrative health data (e.g., healthcare practitioner encounters, hospitalizations, and prescription dispensations) are routinely-collected, population-based sources of health information that hold substantial potential for health services evaluation and epidemiological research.
Regression Diagnostics Workshop
The Division of Biostatistics in the Dalla Lana School of Public Health at the University of Toronto is pleased to host the SORA-TABA Annual Workshop & DLSPH Biostatistics Research Day.
What can the PHDA Program do for you?
Specialized online training and resources to sharpen your career edge
Do you work in the health or social science field and want to improve your data analysis skills?
What can the PHDA Program do for you?
Specialized online training and resources to sharpen your career edge
Do you work in the health or social science field and want to improve your data analysis skills?
What can the PHDA Program do for you?
Specialized online training and resources to sharpen your career edge
Do you work in the health or social science field and want to improve your data analysis skills?
Agreements with Young Adults Outcome Evaluation Project
Project type: DI Program Government project
Status: Open
PopData Researcher Survey: Key findings and action items
Population Data BC recently conducted a survey to better understand user’s thoughts and experiences with the data, accompanying metadata, and method of data provision.
Here we share key findings and our commitments to address researcher feedback and suggestions. The outcomes of this survey will be used in the development of next year’s annual plan and will feed into the strategic priorities for fiscal year 2022-2023.
Regression Discontinuity Design
This webinar is part of the Advanced Methods Webinar Series
Social policy and clinical care are filled with thresholds—people receive or don’t receive interventions or treatments based on factors such as income, test results, year of birth, and others. These thresholds are a potential source of quasi-randomization for strong observational studies.
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