LEARNING OBJECTIVES
- When is a prediction model appropriate?
- Process for designing, developing, and validating a prediction model
- Examples of predictive models commonly used in clinical settings
- Methods for assessment and validation
- Caveats and considerations when developing prediction models
PRESENTER(S)
Krithika Suresh, PhD
Research Assistant Professor, Adult and Child Consortium for Health Outcomes Research and Delivery Science
Krithika Suresh, PhD is a research assistant professor in the Department of Biostatistics & Informatics. She works with ACCORDS on the CUAnschutz campus, where she is involved in the design and analysis of health outcomes research studies using pragmatic trials, such as cluster randomized and stepped wedge designs. Her research interests include survival analysis, longitudinal data, joint modeling, and predictive modeling, with applications in cancer research and other health outcomes.
Katie Colborn, PhD, MSPH
Assistant Professor
Department of Surgery, University of Colorado
Katie Colborn, PhD, MSPH is an Assistant Professor in the Department of Surgery and holds a secondary appointment in the Department of Biostatistics and Informatics at the University of Colorado Anschutz Medical Campus. She also co-directs the Surgical Outcomes and Applied Research (SOAR) Program in the Department of Surgery. In her role as SOAR co-director, she collaborates with investigators conducting surgical outcomes and health services research and mentors surgeon faculty, residents, and other graduate students. Her research currently focuses on development and validation of statistical methodologies for clinical prediction models. These lines of inquiry typically involve machine learning and high dimensional model selection. She also leads the Data Informatics and Statistics Core of the Palliative Care Research Cooperative Group. She has received extramural funding for her research and has collaborated on numerous extramural research grants.
