A competing risk joint model for dealing with different types of missing data in an intervention trial in prodromal Alzheimer’s disease

Background: Missing data can complicate the interpretability of a clinical trial, especially if the proportion is substantial and if there are different, potentially outcome-dependent causes. Methods: We aimed to obtain unbiased estimates, in the presence of a high level of missing data, for the intervention effects in a prodromal Alzheimer’s disease trial: the LipiDiDiet study. We used a competing risk joint model that can simultaneously model each patient’s longitudinal outcome trajectory in combination with the timing and type of missingness. Results: Using the competing risk joint model, we were able to provide unbiased estimates of the intervention effects in the presence of the different types of missingness. For the LipiDiDiet study, the intervention effects remained statistically significant after this correction for the timing and type of missingness. Conclusion: Missing data is a common problem in (Alzheimer) clinical trials. It is important to realize that statistical techniques make specific assumptions about the missing data mechanisms. When there are different missing data sources, a competing risk joint model is a powerful method because it can explicitly model the association between the longitudinal data and each type of missingness. Trial registration: Dutch Trial Register, NTR1705. Registered on 9 March 2009