Does pattern mixture modelling reduce bias due to informative attrition compared to fitting a mixed effects model to the available cases or data imputed using multiple imputation?: a simulation study
BACKGROUND: Informative attrition occurs when the reason participants drop out from a study is associated with
the study outcome. Analysing data with informative attrition can bias longitudinal study inferences. Approaches
exist to reduce bias when analysing longitudinal data with monotone missingness (once participants drop out they
do not return). However, findings may differ when using these approaches to analyse longitudinal data with nonmonotone
missingness.
METHODS: Different approaches to reduce bias due to informative attrition in non-monotone longitudinal data were
compared. To achieve this aim, we simulated data from a Whitehall II cohort epidemiological study, which used the
slope coefficients from a linear mixed effects model to investigate the association between smoking status at
baseline and subsequent decline in cognition scores. Participants with lower cognitive scores were thought to be
more likely to drop out. By using a simulation study, a range of scenarios using distributions of variables which exist
in real data were compared.
Informative attrition that would introduce a known bias to the simulated data was specified and the estimates from
a mixed effects model with random intercept and slopes when fitted to: available cases; data imputed using
multiple imputation (MI); imputed data adjusted using pattern mixture modelling (PMM) were compared. The twofold
fully conditional specification MI approach, previously validated for non-monotone longitudinal data under
ignorable missing data assumption, was used. However, MI may not reduce bias because informative attrition is
non-ignorable missing. Therefore, PMM was applied to reduce the bias, usually unknown, by adjusting the values
imputed with MI by a fixed value equal to the introduced bias.
RESULTS: With highly correlated repeated outcome measures, the slope coefficients from a mixed effects model
were found to have least bias when fitted to available cases. However, for moderately correlated outcome
measurements, the slope coefficients from fitting a mixed effects model to data adjusted using PMM were least
biased but still underestimated the true coefficients.
CONCLUSIONS: PMM may potentially reduce bias in studies analysing longitudinal data with suspected informative
attrition and moderately correlated repeated outcome measurements. Including additional auxiliary variables in the
imputation model may also reduce any remaining bias