Variable effect estimation
To interpret the available variables in terms of the effect they have on psychological aging, we employed an approach based on linear models with mixed effects.
S u b j A g e ~ V a r i a b l e + ( 1 | P s y c h o A g e g r o u p ) P s y c h o A g e ~ V a r i a b l e + ( 1 | S u b j A g e g r o u p )
The mixed-effects analysis was carried out on the complete MIDUS 1 data set while using the predictions obtained in CV. The implementation was written in R 3.6.2, mixed-effects models were implemented with lme4 package (v1.1.21;
Model validation was carried out using MIDUS 2 and MIDUS Refresher datasets. This pipeline was repeated independently for PsychoAge and SubjAge.
Survival analysis
To investigate the predictive ability of deep psychological aging clocks in terms of all-cause mortality, we employed Cox-regression models for both psychological age and subjective age. To evaluate the association of the predicted age with all-cause mortality, hazard ratios (HR) were calculated. Survival time data (defined as the age at examination until the age of death or last follow-up) was analyzed. For hazard analysis by group, the CoxPHFilter method was used from lifelines for Python (v.0.23.9; Cox models were adjusted for chronological age and sex.
For survival analysis purposes, the rate of aging was expressed as a set of one-hot binary variables representing the sample’s delta – the difference between predicted and the actual age of the samples (either chronological or subjective). (more…)