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A novel correction method for modelling parameter-driven autocorrelated time series with count outcome.

Xiao-Han Xu1, Zi-Shu Zhan1, Chen Shi1

  • 1State Key Laboratory of Organ Failure Research, Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou, 510515, China.

BMC Public Health
|March 28, 2024
PubMed
Summary

A new maximum significant ρ correction (MSRC) method reliably models autocorrelated count time series data, improving parameter estimation in environmental health research. Strict drunk driving regulations were shown to significantly reduce road traffic injuries.

Keywords:
Autocorrelated count dataInterrupted time-seriesParameter-driven modelsType I error inflationUnbiased correction

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Area of Science:

  • Environmental health research
  • Biostatistics
  • Epidemiology

Background:

  • Count time series data are common in environmental health but often autocorrelated, violating generalized linear model assumptions.
  • Existing methods for parameter-driven count time series lack reliable standard error estimation, potentially inflating type I error rates.

Purpose of the Study:

  • To propose and evaluate a new maximum significant ρ correction (MSRC) method for modeling autocorrelated count time series.
  • To assess the effectiveness of drunk driving regulations on road traffic injuries (RTIs) using the MSRC method.

Main Methods:

  • Developed the maximum significant ρ correction (MSRC) method using moment estimation of autocorrelation coefficients.
  • Conducted Monte Carlo simulations to compare MSRC with the classical unbiased correction (UB-corrected) method.
  • Applied MSRC to daily RTI data in Shenzhen, China, to analyze time-varying intervention effects.

Main Results:

  • MSRC controlled the type I error rate effectively, especially with larger sample sizes (n=340).
  • MSRC demonstrated increased statistical power with larger sample sizes and effect sizes.
  • The intervention of strict drunk driving regulations was associated with significant reductions in RTIs at 1, 3, and 5 years post-implementation.

Conclusions:

  • The MSRC method offers a reliable and consistent approach for analyzing autocorrelated count time series data in environmental health.
  • The study provides evidence that strict drunk driving regulations effectively reduce the incidence of road traffic injuries.