Developing and Validating Risk Assessment Models of Clinical Outcomes in Modern Oncology.

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The identification of prognostic factors and building of risk assessment prognostic models will continue to play a major role in 21st century medicine in patient management and decision making. Investigators are often interested in examining the relationship between host, tumor-related, and environmental variables in predicting clinical outcomes. We make a distinction between static and dynamic prediction models. In static prediction modelling, typically variables collected at baseline are utilized in building models. On the other hand, dynamic predictive models leverage the longitudinal data of covariates collected during treatment or follow-up, and hence provide accurate predictions of patients prognoses. To date, most risk assessment models in oncology have been based on static models. In this article, we cover topics that are related to the analysis of prognostic factors, centering on factors that are both relevant at the time of diagnosis or initial treatment and during treatment. We describe the types of risk prediction and then provide a brief description of the penalized regression methods. We then review the state-of-the art methods for dynamic prediction and compare the strengths and the limitations of these methods. While static models will continue to play an important role in oncology, developing and validating dynamic models of clinical outcomes need to take a higher priority. It is apparent that a framework for developing and validating dynamic tools in oncology is still needed. One of the limitations in oncology that modelers may be constrained by the lack of access to the longitudinal biomarker data. It is highly recommended that the next generation of risk assessments consider the longitudinal biomarker data and outcomes so that prediction can be continually updated.






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Halabi, Susan, Cai Li and Sheng Luo (2019). Developing and Validating Risk Assessment Models of Clinical Outcomes in Modern Oncology. JCO precision oncology, 3(3). pp. 1–12. 10.1200/PO.19.00068 Retrieved from

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Sheng Luo

Professor of Biostatistics & Bioinformatics

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