Abstract
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.
Disclosure statement
G.H.L. reports institutional research funding from Amgen, honoraria for lectures from Merck, Partners Healthcare, ER Squibb, Samsung, Sandoz, Seattle Genetics and TEVA, and honoraria for consulting from BeyondSpring, G1 Therapeutics and Jazz Pharm. P.M. reports honoraria for scientific advisory boards membership for Mirati Therapeutics, Bristol Myers Squibb, and Exelixis; consulting fees from Axiom Healthcare; non-branded educational programs supported by Exelixis and Pfizer; leadership or fiduciary roles as a Medical Steering Committee member for the Kidney Cancer Association and a Kidney Cancer Scientific Advisory Board member for KCCure; and research funding from Takeda, Bristol Myers Squibb, Mirati Therapeutics, and Gateway for Cancer Research. N.M.K. reports personal fees from BeyondSpring, BMS, G1 Therapeutics, Invitae, Sandoz, Seattle Genetics, Spectrum and Total Health, all outside the submitted work.