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

ABSTRACT:

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 remai

SUBMITTER: van Oudenhoven FM 

PROVIDER: S-EPMC7983401 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature