Table 1.

Missing data approaches in this study

Approach	Description	Missingness Pattern	Type
Complete cases	Only cases without missing observations in analysis	MCARa	Basic, single
Mean imputation	Imputes missing observations with listwise mean for each variable	MCARb	Basic, single
LOCF	Imputes the last available observation in the current data collection wave	-	Basic, single
Regression imputation	Imputes missing observations by prediction based on other variables in a regression model	MAR, MCAR	Advanced, single
EM imputation	Imputes missing observations using expectation maximization algorithm	MAR, MCAR	Advanced, single
NORM	Multiple imputes missing observations under a normal model	MAR, MCAR	Advanced, multiple
MICE	Multiple imputes missing observations using chained equations	MAR, MCAR	Advanced, multiple
SPSS MI	Multiple imputes missing observations under a normal model in SPSS	MAR, MCAR	Advanced, multiple
Amelia II	Multiple imputes missing observations using a bootstrapping-based algorithm	MAR, MCAR	Advanced, multiple

^a This approach will lead to unbiased point estimators (eg, means) under MCAR, but will result in lowered power and sample size.

^b This approach will lead to unbiased point estimators (eg, means) under MCAR, but will result in biased, smaller confidence intervals.