All samples were assigned to one of the five PsychoAge or SubjAge groups (25-34, 35-44, 45-54, 55-64, 65-74 years predicted)
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 Gjennomsnittsalder for ekteskap i Texas 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.