Risk-period-cohort approach for averting identification problems in longitudinal models

Area of Science:

• Epidemiology
• Gerontology
• Human Development
• Social Sciences

Background:

• Traditional age-period-cohort (APC) models face collinearity issues, limiting simultaneous estimation of age, period, and birth cohort effects.
• The inherent relationship age = period - cohort means only two effects can be estimated at once, hindering comprehensive analysis of change over time.

Purpose of the Study:

• Introduce an alternative framework, the risk-period-cohort (RPC) approach, to overcome APC model collinearity.
• Characterize age-related risk by modeling risk indices instead of direct age effects.
• Demonstrate the utility and advantages of the RPC approach in longitudinal studies.

Main Methods:

• Developed the risk-period-cohort (RPC) framework, modeling age-related risk as a hybrid of biological and sociological influences.
• Conducted simulations to evaluate the properties of the RPC approach.
• Applied the RPC method to analyze longitudinal depression screening data from 27,496 individuals in the NHANES survey (2005-2016).

Main Results:

• The RPC approach effectively obviates the collinearity problem inherent in APC models, except in specific pathological cases.
• The magnitude of the chronological age effect in RPC models correlates with the association between risk indices and chronological age.
• RPC models satisfactorily recover cohort and period effects in most scenarios, outperforming traditional APC analysis.

Conclusions:

• The risk-period-cohort (RPC) method provides a robust alternative to traditional APC analysis for studying trajectories of change over time.
• RPC offers broader implications for examining developmental processes in various longitudinal research settings.
• This framework enhances the ability to disentangle complex age, period, and cohort influences in population studies.