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Reliable risk models can greatly facilitate patient-centered inferences and decisions. Herein we summarize key considerations related to risk modeling in clinical oncology. Often overlooked challenges include data quality, missing data, effective sample size estimation, and selecting the variables to be included in the risk model. The stability and quality of the model should be carefully interrogated with particular emphasis on rigorous internal validation.

Risk Model Development and Validation in Clinical Oncology: Lessons Learned