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A versatile method for confirmatory evaluation of the effects of a covariate in multiple models
Author(s) -
Pipper Christian Bressen,
Ritz Christian,
Bisgaard Hans
Publication year - 2012
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/j.1467-9876.2011.01005.x
Subject(s) - covariate , computer science , representation (politics) , point process , binary number , event (particle physics) , statistics , econometrics , data mining , machine learning , mathematics , politics , political science , law , physics , arithmetic , quantum mechanics
Summary. 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.