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Odds ratio estimation in a steady-state population.

S C Newman1

  • 1Department of Psychiatry, University of Alberta, Edmonton, Canada.

Journal of Clinical Epidemiology
|January 1, 1988
PubMed
Summary
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This study models steady-state populations using a Markov process. Findings show odds ratios from prevalence-case-control studies may bias incidence density ratio estimates, particularly for high ratios.

Area of Science:

  • Epidemiology
  • Biostatistics
  • Population Dynamics

Background:

  • Epidemiological studies often use case-control designs to estimate disease risk.
  • Steady-state populations are common in epidemiological research.
  • The relationship between odds ratios and incidence density ratios requires careful consideration.

Purpose of the Study:

  • To model a steady-state population using a three-state Markov process.
  • To derive and examine the P = ID equation and related identities.
  • To assess the bias in odds ratio estimates from prevalence-prevalence case-control studies compared to incidence density ratios.

Main Methods:

  • Utilized a three-state Markov process for population modeling.
  • Derived the classical P = ID equation.

Related Experiment Videos

  • Employed theoretical and empirical methods to analyze bias.

    • Main Results:

    • The odds ratio from prevalence-prevalence case-control studies is generally a biased estimate of the incidence density ratio in steady-state populations.
    • Bias is typically not practically significant unless the incidence density ratio is 10 or greater.
    • Identities reported by Miettinen and Freeman & Hutchison were derived.

    Conclusions:

    • Prevalence-prevalence case-control studies can provide biased incidence density ratio estimates.
    • The magnitude of bias is dependent on the incidence density ratio.
    • Markov process modeling offers a framework for understanding these epidemiological relationships.