Multiple Imputation of Missing Data Using SAS

Index

A

algorithms, for multiple imputation of missing values 17–25

Allison, P.D. 40, 83

analysis

comparative 113–120

complete case 4–5, 113–120

of completed data sets 25–26

examples of 7, 8–9t

of longitudinal seizure data 120–128

for subpopulations of complex sample design data sets 57–58

arbitrary missing data patterns

methods for 23–25, 23f

transforming to a monotonic missing data structure 24–25

attributes, of multiple imputation methods 13

B

bar graphs 43

Barnard, J. 28, 54–55

Bayesian Posterior Simulation methods 23–24

BINOMIAL option 95

Bodner, T.E. 40

bounding 82

Buck, S.F. 11

BY statement

imputation of classification variables 92, 97

incorporating complex sample design in MI analysis and inference steps 54

logistic regression analysis ...