In biomedical studies, researchers are often interested in assessing the association between one or more ordinal explanatoryvariables and an outcome variable, at the same time adjusting for covariates of any type. Th...
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In biomedical studies, researchers are often interested in assessing the association between one or more ordinal explanatoryvariables and an outcome variable, at the same time adjusting for covariates of any type. The outcome variable may be continuous, binary, or represent censored survival times. In the absence of precise knowledge of the response function, using monotonicity constraints on the ordinal variables improves efficiency in estimating parameters, especially when sample sizes are small. An active set algorithm that can efficiently compute such estimators is proposed, and a characterization of the solution is provided. Flaying an efficient algorithm at hand is especially relevant when applying likelihood ratio tests in restricted generalized linear models, where one needs the value of the likelihood at the restricted maximizer. The algorithm is illustrated on a real life data set from oncology. (C) 2010 Elsevier B.V. All rights reserved.
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