Guidelines for Multiple Imputations in Repeated Measurements with Time-dependent Covariates: a Case Study

Objectives: We provide guidelines for handling the most common missing data problems in repeated measurements in observational studies and deal with practicalities in producing imputations when there are many partly missing time-varying variables and repeated measurements.

Study Design And Setting: The Maastricht Study on long-term dementia care environments was used as a case study. The data contain 84 momentary assessments for each of 115 participants. A continuous outcome and several time-varying covariates were involved containing missing observations varying from 4% to 25% per time point. A multiple imputation procedure is advocated with restrictions imposed on the relation within and between partially missing variables over time.

Results: Multiple imputation is a better approach to deal with missing observations in both outcome and independent variables. Furthermore, using the statistical package R-MICE, it is possible to deal with the limitations of current statistical software in imputation of missing observations in more complex data.

Conclusion: In observational studies, the direct likelihood approach (i.e., the standard longitudinal data methods) is sufficient to obtain valid inferences in the presence of missing data only in the outcome. In contrast, multiple imputation is required when dealing with partly missing time-varying covariates and repeated measurements.