regression models for ordinal responses a review of methods

Hence a constrained partial proportional 1327

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Unformatted text preview: gh γ is not dependent on j, it is multiplied by the fixed scalar constant j in the computation of the jth cumulative logit. Results of fitting the unconstrained (model 6) and constrained partial-proportional odds (model 7) models to the analgesic trial data (Table 1) are contrasted in Table 2. It was demonstrated earlier that the data did not satisfy the proportional odds assumption, and that a monotonically increasing trend in the log-odds ratios was observed in the different logits. Hence, a constrained (partial-proportional 1327 REGRESSION MODELS FOR ORDINAL RESPONSES odds) model was fit, with the specification of the following constraints: 1 = 0, 2 = 1, and 3 = 2. The log-odds ratio when the response is dichoˆ tomized at (yj = 1) is β (0.6899), while the log-odds ratios associated with the second and third cumulative ˆ ˆ ˆ ˆ logits are β + ˆ2γ (0.6899 + 0.9216) and β + ˆ3γ (0.6899 + 2 * 0.9216), respectively. A simultaneous two degrees of freedom test of H0 : β = 0, γ = 0, was rejected (χ2 =...
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