regression models for ordinal responses a review of methods

The cumulative logit and the continuationratio models

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Unformatted text preview: dds model performs better than the constrained model. Similarly, the fit of a continuation-ratio model was compared to the observed data (Figure 2). Once again, the continuation ratio model does not adequately fit the data for the first continuation-ratio (relative risk = 2.7), whereas the model fits well for the other three continuation-ratios. On the other hand, when the ordering of the response variable is subjective, then a polytomous logistic model is more preferable than fitting (k – 1) simple logistic models. If the ordering is valid, then an appropriate ordinal model must be chosen from the class of ordinal models, the choice made based on the goals of statistical analyses. Finally, Koch et al.20 developed a two-stage procedure called as Functional Assymptotic Regression Methodology (FARM) for fitting a partial proportional odds model based on the weighted least squares estimation procedure. Although we did not consider fitting this model, the interested reader is referred to the papers by Koch et al.,20 and Peterson and Harrell6 for thorough review and discussion. CONCLUSIONS This paper presents a synthesized review of generalized linear regression models for analysing ordered responses. The cumulative logit and the continuationratio models for ordinal responses have been the primary focus in epidemiological and biomedical applications,1–10,15,19–23 while other models for the analysis of ordinal...
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This document was uploaded on 02/25/2014.

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