Objective:
Prior research has identified numerous genetic (including sex),
education, health and lifestyle factors that predict cognitive decline.
Traditional model selection approaches (e.g., backward or stepwise
selection) attempt to find one model that best fits the observed data,
risking interpretations that only the selected predictors are important. In
reality, several predictor combinations may fit similarly well but result in
different conclusions (e.g., about size and significance of parameter
estimates). In this paper we describe an alternative method,
Information-Theoretic (IT) model averaging, and apply it to characterize a
set of complex interactions in a longitudinal study on cognitive
decline.
Method:
Here we used longitudinal cognitive data from 1256 late-middle aged
adults from the Wisconsin Registry for Alzheimer’s Prevention study
to examine the effects of sex, Apolipoprotein E (APOE)
ɛ4 allele (non-modifiable factors), and literacy achievement
(modifiable) on cognitive decline. For each outcome, we applied IT model
averaging to a set of models with different combinations of interactions
among sex, APOE, literacy, and age.
Results:
For a list-learning test, model-averaged results showed better
performance for women vs men, with faster decline among men; increased
literacy was associated with better performance, particularly among men.
APOE had less of an association with cognitive
performance in this age range (~40–70).
Conclusions:
These results illustrate the utility of the IT approach and point to
literacy as a potential modifier of cognitive decline. Whether the
protective effect of literacy is due to educational attainment or intrinsic
verbal intellectual ability is the topic of ongoing work.