# Concepts of MCAR, MAR, MNAR

### Concepts of MCAR, MAR, MNAR

• Missing completely at random (MCAR): the probability of being missing is the same for all cases
• Cause of missing is unrelated to the data
• Missing at random (MAR): the probability of being missing only depends on the observed data
• Cause of missing is unrelated to the missing values
• Missing not at random (MNAR): probability of being missing depends on the missing values themselves

### Listwise deletion and pairwise deletion

• Listwise deletion (also called complete-case analysis): delete rows which contain one or more missing values
• If data is MCAR, listwise deletion produces unbiased estimates of means, variances, and regression weights (if need to train a predictive model)
• If data is not MCAR, listwise deletion can severely bias the above estimates.
• Pairwise deletion (also called available-case analysis)
• Mean and variance of variable $$X$$ are based on all cases with observed data on $$X$$
• Covariance and correlation of $$X$$ and $$Y$$ is based on all data which both $$X$$ and $$Y$$ have non-missing values

### Mean imputation

• Compared with the observed data, in the imputed data (observed + imputed values)
• Standard deviations decrease
• Correlation decreases
• Means can be biased if the data is not MCAR.

### Regression imputation

1. Build a regression model from the observed data
2. Impute the missing values in the response variable with the predicted values from the fitted regression
• The impute values are the most likely values under the model
• However, it decreases the variance of the target variable
• And it increases the correlations between the target and covariates
• Regression imputation, and its modern incarnations in machine learning is probably the most dangerous of all ad-hoc methods

### Stochastic regression imputation

1. Build a regression model from the observed data
2. Impute a missing value in the response variable with the predicted value plus a random draw from the residual
• Preserves variance and correlation.
• Imputed values can exceed the range (e.g., a negative Ozone level). A more suitable model may resolve this.

### LOCF and BOCF

• Last observation carried forward (LOCF) and baseline observation carried forward (BOCF) are for longitudinal data.

• LOCF can yield biased estimation even under MCAR.

### Indicator method

• Not for imputation, but for building predictive models
• Only works for missing in covariates, not the target variables

### Summary of ad-hoc imputation methods

• Note: the unbiasness of regression coefficients are assess with the variable containing missing values as the target variable

# Multiple Imputation in a Nutshell

### Multiple imputation creates $$m>1$$ complete datasets

• Three steps of multiple imputation
1. Imputation
2. Analysis: train separate models
3. Pooling: variance among $$m$$ parameter estimates combines the conventional sampling variance (within-imputation variance) and the extra variance caused by the missing data (between-imputation variance)

### Why using multiple imputation?

• It provides a mechanism to deal with the inherent uncertainty of the imputations
• It separate the solution of the missing data problem from the solution of the complete-data problem (train predictive models on complete data)

### Multiple imputation example using the mice package

## Load the mice package
library(mice);
## Impute 20 times, using preditive mean matching
imp <- mice(airquality, seed = 1, m = 20, print = FALSE)
## Fit linear regressions
fit <- with(imp, lm(Ozone ~ Wind + Temp + Solar.R))
## Pooled regression estimates
pander(summary(pool(fit)))
term estimate std.error statistic df p.value
(Intercept) -60.21 21.57 -2.791 100.3 0.006
Wind -3.174 0.644 -4.927 83.29 0
Temp 1.584 0.228 6.959 125.7 0
Solar.R 0.058 0.023 2.454 79.63 0.016

### References

• Van Buuren, S. (2018). Flexible Imputation of Missing Data, 2nd Edition. CRC press.