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How are missing data in covariates handled in?
Multiple imputation is an increasingly popular method for handling missing data which involves replicating the original dataset multiple times and in each replication replacing the missing values with plausible observations drawn from the posterior predictive distribution [ 1 ].
How is multiple imputation of missing predictor used?
Multiple imputation (MI) of missing predictor values using all observed information including outcome is advocated to deal with selective missing values. This seems a self-fulfilling prophecy. We tested this hypothesis using data from a study on diagnosis of pulmonary embolism.
Which is the correct approach to missing data?
Complete-case analysis A direct approach to missing data is to exclude them. In the regression context, this usually means complete-case analysis: excluding all units for which the outcome or any of the inputs are missing. In R, this is done automatically for classical regressions (data points with any missingness in the predictors or outcome are
Why does software exclude subjects with missing values?
Most software automatically exclude subjects with missing values. This commonly causes bias because missing values seldom occur completely at random (MCAR) but rather selectively based on other (observed) variables, missing at random (MAR).
How to deal with missing outcome data in randomized trials?
For continuous outcome data, the most likely value based on the multivariable model can be imputed for the missing observation (also known as conditional mean imputation).
When is missing data called Missing at random?
When subjects with missing outcomes are a random subset of the individuals in a particular study, the missing data are called missing completely at random (MCAR) ( 2, 3, 5, 6 ). In that case, complete case analysis yields unbiased estimates of the treatment effect ( 2, 3, 5, 6 ).
When is missing outcome data is not MCAR or Mar?
When missingness of outcome data is not MCAR or MAR, data are said to be missing not at random (MNAR). For example, if missingness is related to unobserved patient data or only to the value of the unobserved outcome, missing data are MNAR, and the aforementioned methods cannot handle data that are MNAR by default.