Using joint models to disentangle intervention effect types and baseline confounding: an application within an intervention study in prodromal Alzheimer’s disease with Fortasyn Connect

• Main Authors: Floor M. van Oudenhoven, Sophie H.N. Swinkels, Tobias Hartmann, Hilkka Soininen, Anneke M.J. van Hees, Dimitris Rizopoulos
• Format: Article
• Language: English
• Published: BMC 2019-07-01
• Series: BMC Medical Research Methodology
• Subjects: Joint model; Intervention effect; Baseline imbalance; Fortasyn; Alzheimer’s disease
• Online Access: http://link.springer.com/article/10.1186/s12874-019-0791-z
• ISSN: 1471-2288

Abstract Background Many prodromal Alzheimer’s disease trials collect two types of data: the time until clinical diagnosis of dementia and longitudinal patient information. These data are often analysed separately, although they are strongly associated. By combining the longitudinal and survival data into a single statistical model, joint models can account for the dependencies between the two types of data. Methods We illustrate the major steps in a joint modelling approach, motivated by data from a prodromal Alzheimer’s disease study: the LipiDiDiet trial. Results By using joint models we are able to disentangle baseline confounding from the intervention effect and moreover, to investigate the association between longitudinal patient information and the time until clinical dementia diagnosis. Conclusions Joint models provide a valuable tool in the statistical analysis of clinical studies with longitudinal and survival data, such as in prodromal Alzheimer’s disease trials, and have several added values compared to separate analyses.