Joint Models for Estimating Determinants of Cognitive Decline in the Presence of Survival Bias

Abstract

Background: Identifying determinants of cognitive decline is crucial for developing strategies to prevent Alzheimer’s disease and related dementias. However, determinants of cognitive decline remain elusive, with inconsistent results across studies. One reason could be differential survival. Cognitive decline and many exposures of interest are associated with mortality making survival a collider. Not accounting for informative attrition can result in survival bias. Generalized estimating equations (GEE) and linear mixed-effects model (LME) are commonly used to estimate effects of exposures on cognitive decline, but both assume mortality is not informative. Joint models combine LME with Cox proportional hazards models to simultaneously estimate cognitive decline and the hazard of mortality. Methods: Using simulations, we compared estimates of the effect of a binary exposure on rate of cognitive decline from GEE, weighted GEE using inverse-probability-of-attrition weights, and LME to joint models under several causal structures of survival bias. Results: We found that joint models with correctly specified relationship between survival and cognition performed best, producing unbiased estimates and appropriate coverage. Even those with misspecified relationship between survival and cognition showed advantage under causal structures consistent with survival bias. We also compared these models in estimating the effect of education on cognitive decline after dementia diagnosis using Framingham Heart Study data. Estimates of the effect of education on cognitive decline from joint models were slightly attenuated with similar precision compared with LME. Conclusions: In our study, joint models were more robust than LME, GEE, and weighted GEE models when evaluating determinants of cognitive decline.