Omitting impact of race, ethnicity results in less accurate cancer risk prediction models

Prognostic models for risk for postoperative cancer recurrence that omit race and ethnicity as predictors may be less accurate for individuals from historically underserved populations, retrospective study results showed.Removing race and ethnicity from risk prediction algorithms for colorectal cancer recurrence has a deleterious effect on model fairness that could result in “inappropriate care recommendations” for historically underserved patient populations, investigators wrote in JAMA Network Open.“It is important to evaluate subgroup-specific performances of risk modelsRead More