In cancer research, dealing with missing data is a common challenge. Data may be missing due to various reasons such as patient dropout, non-response, or incomplete records. MAR is crucial because it allows researchers to use available data to make inferences about the missing data, thereby reducing biases and improving the validity of the study results.
