Re: Poisson regression models
« **Reply #2 on:** 28 June, 2007, 07:59:52 AM »

Hi gvandekaa,

It seems to me that you would interpret poisson regression coefficients exactly as they are. I obtained the following from:

http://www.oxfordjournals.org/tropej/online/ma_chap13.pdfPoisson regression distribution

The assumptions include:

1. Logarithm of the disease rate changes linearly with equal increment increases in the

exposure variable.

2. Changes in the rate from combined effects of different exposures or risk factors are

multiplicative.

3. At each level of the covariates the number of cases has variance equal to the mean.

4. Observations are independent.

Methods to identify violations of assumption (3) i.e. to determine whether variances are too

large or too small include plots of residuals versus the mean at different levels of the predictor

variable. Recall that in the case of normal linear regression, diagnostics of the model used

plots of residuals against fits (fitted values). This means that the same diagnostics can be used

in the case of Poisson Regression.