Risk Model Development and Validation in Clinical Oncology: Lessons Learned

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.

Automated summary

This article summarizes key considerations related to risk modeling in clinical oncology, including data quality, missing data, sample size estimation, and variable selection. The importance of rigorous internal validation and careful examination of model stability and quality is emphasized.