Several techniques are used to address MNAR in cancer research, including:
Multiple Imputation: This method involves creating multiple complete datasets by filling in the missing values based on the observed data and then combining the results. Inverse Probability Weighting: This technique assigns weights to observed data to account for the missing data, balancing the dataset as if the missing data were present. Pattern-Mixture Models: These models explicitly model the process leading to missing data, allowing for more accurate imputation. Bayesian Methods: Bayesian approaches can incorporate prior knowledge and provide probabilistic estimates that account for the uncertainty due to missing data.
