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 often are interested in examining the relationship among host, tumor-related, and environmental variables in predicting clinical outcomes. We distinguish between static and dynamic prediction models. In static prediction modeling, variables collected at baseline typically are used 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 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 limitations of these methods. Although 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. A framework for developing and validating dynamic tools in oncology seems to still be needed. One of the limitations in oncology that may constrain modelers is the lack of access to longitudinal biomarker data. It is highly recommended that the next generation of risk assessments consider longitudinal biomarker data and outcomes so that prediction can be continually updated.
A combined efficient design for biomarker data subject to a limit of detection due to measuring instrument sensitivity