Towards useful clinical risk prediction models
In this workshop, we provide a brief, non-technical introduction to the basic steps of clinical risk prediction model research, from gathering data, developing the model, internally and externally validating the predictive performance of the model, to assessing the clinical utility of the model and implementation in clinical practice.
Next, we discuss some common pitfalls and myths regarding risk prediction models. We pay particular attention to calculating the sample size required to develop or validate a model, to handling missing data, and to measuring model performance. We address the role of shrinkage and machine learning in modern clinical prediction modeling.
To conclude, we provide a birds-eye overview of the current clinical risk prediction modeling landscape in the medical literature.
Next, we discuss some common pitfalls and myths regarding risk prediction models. We pay particular attention to calculating the sample size required to develop or validate a model, to handling missing data, and to measuring model performance. We address the role of shrinkage and machine learning in modern clinical prediction modeling.
To conclude, we provide a birds-eye overview of the current clinical risk prediction modeling landscape in the medical literature.
For further information see our website: https://www.vicbiostat.org.au/event/towards-useful-clinical-risk-prediction-models
Location
NB. NEW ROOM: Seminar Room 515, Melbourne School of Population and Global Health
University of Melbourne, 207 Bouverie St, Carlton VIC 3053
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Towards useful clinical risk prediction models
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