 Moreover, selection bias may occur due to non-random selection of participants with complete data from the target study population. Thus, in each mixed-effect regression model, a 2-stage Heckman selection process was conducted, by running a probit model to compute an inverse mills ratio at the first stage derived from the predicted probability of being selected, conditional on the covariates in the probit model, mainly baseline age, sex, race, poverty status and education. At the second stage, this inverse mills ratio was then entered as a covariate in the final mixed-effects regression model, as was done in a previous study.44 In all analyses, a type I error of 0.05 was considered for main effects whereas a p0.10 was deemed significant for interaction terms,45, prior to correcting for multiple testing. A familywise Bonferroni procedure was used to correct for multiple testing by accounting only for cognitive tests and assuming that exposures related to separate substantive hypotheses.46 Therefore, for main effects, p0.004 0.0511 was considered significant. Due to their lower statistical power compared to main effects, 2-way interaction terms had their critical p-values reduced to 0.10110.009, while 3-way and 4-way interaction terms had their critical p-value reduced to 0.05. A similar approach was adopted in two other studies. 47, 48 