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Age-period-cohort models: a comparative study of available methodologies
C Robertson1, S Gandini, P Boyle
1Division of Epidemiology and Biostatistics, European Institute of Oncology, Milano, Italy.
Journal of Clinical Epidemiology
|July 17, 1999
Summary
For age-period-cohort models, only methods using estimable functions like curvatures are reliable. Other common approaches can introduce bias in disease rate trend analysis.
Area of Science:
- Epidemiology
- Biostatistics
- Demography
Background:
- Age-period-cohort (APC) models are crucial for analyzing disease rates.
- The identifiability problem in APC models complicates accurate trend estimation.
- Various statistical methods attempt to resolve APC identifiability issues.
Purpose of the Study:
- To compare the performance of different solutions to the identifiability problem in APC models.
- To evaluate the reliability and potential biases of various APC estimation methods.
- To identify recommended approaches for analyzing disease rates using APC models.
Main Methods:
- Comparison of multiple statistical solutions for APC model identifiability.
- Utilizing disease rates with a known underlying structure for simulation.
- Assessment of bias and accuracy in parameter estimates across different methods.
Main Results:
- Methods based on estimable functions (curvatures) are recommended for general use.
- Approaches minimizing penalty functions are suitable only when rates are stable over time.
- Methods using individual records can introduce bias with strong age and period effects.
- Nonparametric methods show limited power in small datasets but attribute trends to period and cohort effects.
Conclusions:
- Estimable function-based methods provide the most robust solutions for APC identifiability.
- Careful interpretation is necessary, as all methods correctly estimate nonlinear components.
- The choice of method depends on the specific characteristics of the disease rates and data structure.
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