PhD thesis - University of Hertfordshire Research Archive

to indigenously acquired infection with those which included all cases. For
each continuous variable (the number of risk factors identified; number of
cases included; number of controls included; number of variables
investigated; the exposure period in days, the overall study sample size; the
ratio of cases to controls) Stata‟s „ladder‟ command was used to determine
the transformation which best converted that variable into a normally or near-
normally distributed variable, then that transformation was performed on that
variable.
The effect of each of these variables on the outcome of interest (the
transformed number of reported risk factors) was investigated initially using
single variable Poisson regression. Variables significantly associated with the
outcome of interest at or above the 90% level were then included in a
multiple variable Poisson regression model which was simplified using the
likelihood ratio test.
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