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Unformatted text preview: partial-proportional odds model6 was to relax the strong
assumption of identical log-odds ratio for the Y by x1
association, in the proportional odds model. Violation
of the assumption of identical log-odds could lead to
the formulation of an incorrect or misspecified model.
A situation under which this assumption does not hold
is illustrated below.
Analgesic trial data . For purposes of illustration,
consider the analgesic trial data2 described in Table 1.
The estimated log-odds ratios [β ], and their estimated
ˆ )], for the logits are presented in
standard errors [se(β
Table 1, for comparisons between the drugs Z100 and
EC4 versus C15 and C60. The results indicate that the
log-odds ratio is largest (β = 2.6384) when the rating of
the drug is dichotomized at Y = 4 ‘ less than very good’
( 3) versus ‘very good’ (4); the dichtomization for the
next largest (β = 1.5476) being at Y = 3, ‘poor or fair’
versus ‘good or very good’ (Y 3), and the log-odds
ratio is smallest (β = 0.7013), when the dichtomization
is made at Y = 2, ‘poor’ ver...
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This document was uploaded on 02/25/2014.
- Spring '11