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Risk Prediction Models in Public Health

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Level: Intermediate

Intermediate courses typically require some previous training, such as an knowledge of key epidemiologic methods or concepts, experience conducting multivariate regression, or prior programming experience in R. 

Track: Both analyst & non-analyst tracks available

Courses targeted to analysts focus learning new approaches for managing and analyzing data in statistical software such as R or SAS, or learning version control procedures using git. Courses targeted to non-analysts are more conceptual in nature and do not involve in-depth programming. Some courses offer both analyst and non-analyst tracks to provide different options for engaging with course material. 

Audience: Learners pursuing the analyst certificate should be familiar with multivariable logistic regression and basic epidemiologic measures to assess the predictive ability of a diagnostic test (e.g., sensitivity, specificity, positive predictive value, negative predictive value). 

Overview: Learning objectives are understanding of the role of risk prediction models in public health settings, learning to build and evaluate models using logistics regression and techniques from machine learning, and gaining skills in model calibration, discrimination, and external validation.  

Time commitment: Approximately 30 minutes for the non-analyst track and 1.5 hours for the analyst track, which includes 45 minutes of hands-on practice material.  

Certificates: Two tiers of certificates are available to accommodate different levels of expertise and engagement with the material. These two tiers are designed to distinguish between those who engage with the course at a conceptual level (non-analyst) and those who apply the concepts in a hands-on, technical manner (analyst). 

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