Background Recent communications have argued that often it may not be appropriate to analyse cross-sectional studies of prevalent outcomes with logistic regression models. The purpose of this communication is to compare three methods that have been proposed for application to cross sectional studies: (1) a multiplicative generalized linear model, which we will call the log-binomial model, (2) a method based on logistic regression and robust estimation of standard errors, which we will call the GEE-logistic model, and (3) a Cox regression model. MethodsFive sets of simulations representing fourteen separate simulation conditions were used to test the performance of the methods. ResultsAll three models produced point estimates close to the true parameter, i.e. the estimators of the parameter associated with exposure had negligible bias. The Cox regression produced standard errors that were too large, especially when the prevalence of the disease was high, whereas the log-binomial model and the GEElogistic model had the correct type I error probabilities. It was shown by example that the GEE-logistic model could produce prevalences greater than one, whereas it was proven that this could not happen with the log-binomial model. The logbinomial model should be preferred.

Our results confirm previous findings of an elevated metarelative risk for multiple myeloma among firefighters. In addition, a probable association with non-Hodgkin lymphoma, prostate, and testicular cancer was demonstrated.

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Our analyses suggest that high TCDD exposure results in an excess of all cancers combined, without any marked specificity. However, excess cancer was limited to the highest exposed workers, with exposures that were likely to have been 100-1000 times higher than those experienced by the general population and similar to the TCDD levels used in animal studies.