In this post we’ll look at the deviance goodness of fit test for Poisson regression with individual count data. Many software packages provide this test either in the output when fitting a Poisson regression model or can perform it after fitting such a model (e.g. Stata), which may lead researchers and analysts in to relying on it. In this post we’ll see that often the test will not perform as expected, and therefore, I argue, ought to be used with caution.
Wilcoxon-Mann-Whitney as an alternative to the t-test
The two sample t-test is one of the most used statistical procedures. Its purpose is to test the hypothesis that the means of two groups are the same. The test assumes that the variable in question is normally distributed in the two groups. When this assumption is in doubt, the non-parametric Wilcoxon-Mann-Whitney (or rank sum ) test is sometimes suggested as an alternative to the t-test (e.g. the Wikipedia page on the t-test), which doesn’t rely on distributional assumptions. But is this necessarily a good ‘replacement’?
Interpreting changes in hazard and hazard ratios
I recently attended a great course by Odd Aalen, Ornulf Borgan, and Hakon Gjessing, based on their book ’Survival and Event History Analysis: a process point of view’. Among the many interesting topics covered was the issue of how to interpret changes in estimated hazard functions, and similarly, changes in hazard ratios comparing two groups of subjects.