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Conditional Poisson models: a flexible alternative to conditional logistic case cross-over analysis.

Ben G Armstrong1, Antonio Gasparrini, Aurelio Tobias

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Conditional Poisson regression offers an improved method for analyzing environmental exposures and health outcomes compared to traditional case crossover analyses. This approach simplifies computation and allows for adjustments for overdispersion and autocorrelation, enhancing the analysis of time series data.

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Area of Science:

  • Environmental Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Time-stratified case-crossover designs are common for analyzing environmental exposures and health outcomes.
  • Conventional analysis uses conditional logistic regression, which has limitations in adjusting for overdispersion and autocorrelation.
  • A Poisson model with stratum indicators offers an alternative but is computationally intensive.

Purpose of the Study:

  • To introduce and evaluate the conditional Poisson model as an alternative to conditional logistic regression for time series analysis of health outcomes.
  • To demonstrate the advantages of the conditional Poisson model in terms of computational efficiency and flexibility.

Main Methods:

  • The conditional Poisson model conditions on the total event count per stratum, avoiding the estimation of stratum-specific parameters.
  • This method does not require data expansion, unlike the case-crossover format.
  • The model can accommodate overdispersion and autocorrelation in count data.

Main Results:

  • Conditional Poisson models were simpler to code and faster to run than conditional logistic analyses in simulations and real data.
  • The conditional Poisson model can be applied to larger datasets compared to standard Poisson models.
  • It accurately adjusted for overdispersion and autocorrelation, yielding identical estimates to conditional logistic regression when these adjustments were not needed.

Conclusions:

  • Conditional Poisson regression models present a computationally efficient and flexible alternative to case-crossover analysis for stratified time series data.
  • This model is advantageous for handling overdispersion and autocorrelation.
  • It is applicable in other scenarios requiring fine stratification for confounding control.