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Analysis of count data using poisson regression.

M Katherine Hutchinson1, Matthew C Holtman

  • 1University of Pennsylvania School of Nursing, 420 Guardian Drive, Philadelphia, Pennsylvania 19104-6096, USA.

Research in Nursing & Health
|September 16, 2005
PubMed
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Health researchers analyzing infrequent count data should consider Poisson or negative binomial regression over ordinary least squares (OLS) regression due to data skewness. These methods offer more appropriate statistical analysis for events like hospitalizations or healthcare visits.

Area of Science:

  • Health Services Research
  • Biostatistics
  • Epidemiology

Background:

  • Health researchers frequently analyze discrete, repeatable events such as hospitalizations or healthcare visits.
  • These events generate count data, which are often non-normally distributed and positively skewed.
  • Standard ordinary least squares (OLS) regression may be unsuitable for analyzing such skewed count data.

Purpose of the Study:

  • To compare and contrast three statistical methods for analyzing infrequently occurring count data.
  • To discuss the strengths, limitations, and considerations of each analytical approach.
  • To illustrate the application of these methods using data from the National Longitudinal Survey of Adolescent Health (AddHealth).

Main Methods:

  • Comparison of ordinary least squares (OLS) regression, Poisson regression, and negative binomial regression.

Related Experiment Videos

  • Evaluation of suitability for non-normally distributed, positively skewed count data.
  • Application of methods to real-world health event data.

    • Main Results:

    • Ordinary least squares (OLS) regression has limitations with skewed count data.
    • Poisson and negative binomial regression offer more appropriate alternatives for analyzing infrequent health events.
    • The choice of method depends on the specific characteristics of the count data.

    Conclusions:

    • Poisson and negative binomial regression are recommended for analyzing infrequent health-related count data.
    • Understanding the distribution of count data is crucial for selecting the appropriate statistical model.
    • These advanced regression techniques improve the accuracy of health event analysis.