Exact(1)
In case of a small number of missing data, we will use a method of multiple imputations using a mixed effects linear regression method, and we will perform a standard analysis for each imputation cycle.
Similar(59)
We performed 50 imputation cycles and generated 20 imputations.
The final analysis will take into consideration the variability across the imputation cycles.
We restricted the analysis of the associations of interest only to subjects with one or more measures of alcohol available (n = 7921), including early alcohol onset, as outcome imputations across cycles out with this sample were unstable [ 44].
For the imputation procedure a cycle length of 200 was chosen.
Linear regression was used as the type of imputation, and five cycles of imputation were conducted resulting in five imputed data sets.
We generated 10 MICE imputations, each drawn from a separate chain with a different random seed, with 10 cycles of imputation before drawing the imputed data set.
Five imputed data sets were generated using 20 cycles per imputation, and the main analyses were repeated using the imputed data.
aMultiple Tobit regression with marginal effects for the unconditional expected value E[y] of the dependent variable; bsquare root equivalence scale for 3 months; * p <0.05; ** p < 0.01; *** p < 0.001; SE = standard error; cthe variable was mean centered; N = 3,124 with multiple imputation, imputation steps 100, cycle length 200.
A total of 10 imputed datasets were created using 20 cycles per imputation.
We employed multiple imputations to handle missing data using the multivariate normal model procedure in Stata 12, with 20 cycles of imputation.
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