<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Simpson, Douglas G</style></author><author><style face="normal" font="default" size="100%">Carroll, Raymond</style></author><author><style face="normal" font="default" size="100%">Xie, Minge</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Interval Censoring And Marginal Analysis In Ordinal Regression</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Agricultural Biological and Environmental Statistics</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">categorical data</style></keyword><keyword><style  face="normal" font="default" size="100%">categorical response</style></keyword><keyword><style  face="normal" font="default" size="100%">environmental statistics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1996</style></year></dates><volume><style face="normal" font="default" size="100%">4</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This paper develops methodology for regression analysis of ordinal response data subject to interval censoring. This work is motivated by the need to analyze data from multiple studies in toxicological risk assessment. Responses are scored on an ordinal severity scale, but not all responses can be scored completely. For instance, in a mortality study, information on nonfatal but adverse outcomes may be missing. In order to address possible within–study correlations we develop a generalized estimating approach to the problem, with appropriate adjustments to uncertainty statements. We develop expressions relating parameters of the implied marginal model to the parameters of a conditional model with random effects, and, in a special case, we note an interesting equivalence between conditional and marginal modeling of ordinal responses. We illustrate the methodology in an analysis of a toxicological data-base.&lt;/p&gt;
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