Comparison of several multiple imputation strategies for repeated measures analysis of clinical scales: to truncate or not to?
We evaluated via a simulation study several strategies for imputing missing ordinal outcomes in a longitudinal clinical trial, contrasting methods that involve truncation of imputed values outside plausible ranges with those that do not. Our aim was to identify a preferred imputation strategy for estimating treatment difference at study endpoint. Plausible data were simulated via resampling of existing placebo data sets and adding treatment effect; then different imputation strategies were evaluated under missingness at random (MAR) and varying dropout rates. Our conclusion is that imputation methods based on rounding and truncation lead to larger bias than strategies based on simple methods based on (nontruncated) multivariate normal distribution.
a Quintiles , Durham , North Carolina , USA.
Ilya
I
Lipkovich
Zbigniew
Z
Kadziola
Lei
L
Xu
Tomoko
T
Sugihara
Craig H
CH
Mallinckrodt
D015982:Bias; D003627:Data Interpretation, Statistical; D006801:Humans
Multiple imputation; Ordinal clinical scales; Repeated measures analysis; Rounding and truncation