A versatile method for confirmatory evaluation of the effects of a covariate in multiple models

. Modern epidemiology often requires testing of the effect of a covariate on multiple end points from the same study. However, popular state of the art methods for multiple testing require the tests to be evaluated within the framework of a single model unifying all end points. This severely limits their use in applications where there are different types of end point, e.g. binary, continuous or time to event. We use an asymptotic representation of parameter estimates to combine multiple models without additional constraints. This result enables the use of established tools for multiple testing to provide a fine-tuned control of the overall type I error in a wide range of epidemiological experiments where in reality no other useful alternative exists. The methodology proposed is applied to a multiple-end-point study of the effect of neonatal bacterial colonization on development of childhood asthma.