Reporting and methods for missing data
Most outcome variables are derived from combining multiple items from a questionnaire to create a score. Therefore, if one or more of the items within the score has a missing response, this affects the derivation of a score for the participant. The number of items missing within an outcome variable will be examined and reported. Decisions on how to handle the missing data will be based on recommended practice for deriving these outcomes, where available. According to recommended practice, if there are two or less items with missing responses in the QoL-AD, MADRS and PHQ-9 instruments, they should be replaced with the mean of the participant’s responses across the complete items [Following the handling of missing data in outcomes (described above), the primary strategy to handle missing continuous study outcomes will be a likelihood-based approach (i.e. constrained longitudinal data analysis). This approach assumes that the probability of missing data on the study variable is not related to the missing data but to some of the observed measured data in the model (missing at random).