Multicollinearity refers to a situation in statistical modeling where two or more predictor variables are highly correlated, making it difficult to determine the individual effect of each predictor on the outcome variable. In cancer research, this can lead to unreliable estimates and inflate the variance of coefficient estimates, which can make it challenging to draw valid inferences.
