A Comparison Of Multiple-imputation Methods For Handling Missing Data In Repeated Measurements Observational Studies

Multiple-imputation (MI) methods for imputing missing data in observational health
studies with repeated measurements were evaluated with particular focus on incomplete time
varying explanatory variables. Standard and random-effects imputation by chained equations,
multivariate normal imputation and Bayesian MI were compared regarding bias and efficiency
of regression coefficient estimates by using simulation studies. Flexibility of the methods in
handling different types of variables (binary, categorical, skewed and normally distributed) and
correlations between the repeated measurements of the incomplete variables were also compared.
Multivariate normal imputation produced the least bias in most situations, is theoretically
well justified and allows flexible correlation for the repeated measurements. It can be recommended
for imputing continuous variables. Bayesian MI is efficient and may be preferable in
the presence of categorical and non-normally distributed continuous variables. Imputation by
chained equations approaches were sensitive to the correlation between the repeated measurements.
The moving time window approach may be used for normally distributed continuous
variables with auto-regressive correlation.