Strategies for Multiple Imputation in Longitudinal Studies

Multiple imputation is increasingly recommended in epidemiology to adjust for the bias and loss of information that may occur in analyses restricted to study participants with complete data (“complete-case analyses”). However, little guidance is available on applying the method, including which variables to include in the imputation model and the number of imputations needed. Here, the authors used multiple imputation to analyze the prevalence of wheeze among 81-month-old children in the Avon Longitudinal Study of Parents and Children (Avon, United Kingdom; 1991–1999) and the association of wheeze with gender, maternal asthma, and maternal smoking. The authors examined how inclusion of different types of variables in the imputation model affected point estimates and precision, and assessed the impact of number of imputations on Monte Carlo variability. Inclusion of variables associated with the outcome in the imputation model increased odds ratios and reduced standard errors. When only 5 or 10 imputations were used, variability due to the imputation procedure was substantial enough to affect conclusions. Careful preliminary analysis identified the scope for multiple imputation to reduce bias and improve efficiency and provided guidance for building the imputation model. When data are missing, such preliminary analyses should be routinely undertaken and reported, regardless of whether multiple imputation is used in the final analysis.