What are the Consequences of Ignoring Multicollinearity?
If multicollinearity is ignored, the resulting statistical models may produce inaccurate estimates of the relationship between predictors and the outcome. This can lead to misleading conclusions about which factors are truly significant in influencing cancer risk or progression. Moreover, high multicollinearity can inflate the standard errors of the coefficient estimates, making it difficult to assess the significance of individual predictors.