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Poisson regression
  1. Pamela Warner
  1. Reader in Medical Statistics, Centre for Population Health Sciences, University of Edinburgh, Edinburgh, UK
  1. Correspondence to Dr Pamela Warner, Centre for Population Health Sciences, University of Edinburgh, Medical School, Teviot Place, Edinburgh EH8 9AG, UK; p.warner{at}ed.ac.uk

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Background

The Rashed et al.1 article in this issue uses Poisson regression as a statistical tool. These notes are intended to provide some supplementary explanation of this method (see Box 1 for a glossary of terms used in this article).

Box 1

Glossary of statistical terms used in this article*

Confidence interval (95%)This defines a range of values within which we are 95% confident the true population value lies (e.g. for an estimated population mean, rate, difference in proportions).
Cross-classified/Cross-tabulationSubdividing individuals studied in terms of two (or more) categorical variables, jointly. If two variables then an R×C table of counts is produced, where R=number of rows and C=number of columns. Each cell of the table is termed a frequency count of individuals with that combination of row and column values.
Estimation (inference)This involves use of a summary value calculated from the sample data (e.g. mean, rate) to make an inference about the true value for that entity (parameter) in the background population from which the sample was drawn. The single parameter is a ‘point estimate’, but it is good practice to provide also an interval estimate. See Confidence interval.
Explanatory variableA feature potentially associated with outcome (e.g. calendar year). …

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