For longitudinal data where a summary exposure is of interest, we recommend practitioners adopting the passive imputation strategy.

Simulations show that all methods performed comparably when the proportion of missingness was small, indicating that in such instances, the gain over using any imputation model is minimal.

But as the proportion of missingness increases, the passive imputation approach provides efficient and less biased estimates under the missingness at random and missingness completely at random mechanism.

We compare the following approaches to imputing and summarizing partially missing longitudinal data: 1) active imputation, where we impute the summary; 2) passive imputation, where we impute the daily missing data, and then compute the summary; 3) ad hoc methods where we assume all missing time points have the a) most or the b) least extreme value; and 4) complete case analysis where only participants with complete data are analyzed.

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