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For completeness we also report the results of risk differences, also estimated using the glm command in Stata.
We analyzed data by fitting generalized linear models to spore count data in R v2.15.3 using the glm command.
Excess mortality rate ratios were modelled using the glm command as per the 'grouped' methodology of Dickman et al (2004) with sex, age band, income deprivation quintile, SCN and stage as independent variables.
To obtain relative risks and 95% confidence intervals for the difference in proportions of births that were preterm compared with those that were term, we used relative risk regression (an alternative to logistic regression for cohort studies) using the glm command in Stata.
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We used the glm command in Stata with link function = cloglog, family = binomial to estimate the rate ratios.
We used the glm command of Stata 9.2 to define the linear regression of all the variables with a P value ≤ 0.15.
Data were analyzed using the GLM or Kruskal Wallis and Dwass-Steel-Critchlow-Fligner tests.
Data were fitted using the glm function of the R package [24].
Data were then evaluated using the GLM in a random effects analysis.
All GLM analyses were performed using the glm procedure in R (version 2.10.1, [ 58]).
Using the GLM, we assessed complementary expression patterns between paralogs.
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