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Autoregressive age-period-cohort models

W C Lee¹, R S Lin

¹Graduate Institute of Epidemiology, College of Public Health, National Taiwan University, Taipei, Republic of China.

Statistics in Medicine

|February 15, 1996

Summary

This study introduces a new model for age-period-cohort analysis, treating cohort effects as stochastic. This approach resolves non-identifiability issues, enabling stable estimation of trends in vital data.

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Area of Science:

Epidemiology
Biostatistics
Demography

Background:

Age-period-cohort (APC) analysis faces non-identifiability due to linear dependencies among time factors.
Previous research indicates curvature terms are estimable, but linear trends are not.
The potential for stochastic, rather than deterministic, trends in cohort or period effects remains underexplored.

Purpose of the Study:

To propose and validate a novel APC model incorporating stochastic cohort effects.
To address the non-identifiability problem in traditional APC analysis.
To demonstrate the model's applicability using lung cancer mortality data.

Main Methods:

Modeling cohort effects using an autoregressive process of order 1 (AR(1)).
Applying the proposed model to male lung cancer mortality data from Taiwan (1966-1990).
Assessing parameter identifiability and estimate stability.

Main Results:

The proposed stochastic cohort effect model achieves parameter identifiability.
Estimates derived from the model were found to be stable.
The methodology provides a viable alternative for analyzing time-related trends in vital statistics.

Conclusions:

The stochastic trend model offers a solution to the identifiability problem in APC analysis.
The model's parameters are identifiable and yield stable estimates.
Careful consideration of model assumptions is crucial prior to application.

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