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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
PubMed
Summary
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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.

Related Experiment Videos

  • 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.