Imputing missing longitudinal data in SPSS:

9. Down side using 2, 3, 4 is, what happen if the non-missing observation at dose = 400 is due to measurement error? Down side using 1 and 5 are that imputed values may be too conservative (biased toward null), and individual characteristic is not reflected. Let’s look at the figure on page 31, we clearly see some difference between Whites and Blacks. Can we use this difference for imputation? This is way multiple imputation comes in. We can impute missing values separately for Whites and Blacks. SPSS does not have automated procedure to do this, but my recommendation is to select cases for each race group first, then use Linear Trend method for missing. Down side using 2, 3, 4 is, what happen if the non-missing observation at dose = 400 is due to measurement error? Down side using 1 and 5 are that imputed values may be too conservative (biased toward null), and individual characteristic is not reflected. Let’s look at the figure on page 31, we clearly see some difference between Whites and Blacks. Can we use this difference for imputation? This is way multiple imputation comes in. We can impute missing values separately for Whites and Blacks. SPSS does not have automated procedure to do this, but my recommendation is to select cases for each race group first, then use Linear Trend method for missing.