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Discriminant function and multinomial regression analyses allowed us to convert this mathematical construction into a clinically useful tool in which the two major contributors to the classification were bulbar or limb onset and diagnostic delay.
In step one, crude multilevel multinomial regression analyses were performed with no covariates taken into account.
Table 3 shows the results of the multilevel multinomial regression analyses for each diagnosis group.
Associations between the healthcare pathways and appropriateness of referral were assessed using multinomial regression analyses.
Finally, the linear and multinomial regression analyses described above were repeated for each subgroup.
For this reason, we combined the classes for two visits with three and over in the multinomial regression analyses.
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68 In study 1, 62/79 questions for medical students had sufficient responses to examine the influence of gender and frequency of occurrence on moral distress intensity using multinomial regression (MNR) analyses.
This led to a reduced sample size for the multinomial logistic regression analyses.
In multinomial logistic regression analyses the influence of several predictors on the class membership was examined.
Multinomial logistic regression analyses were conducted to assess the association between genotype and athletic status/competition level.
Concerning multinomial logistic regression analyses, we again used the 'no pain class' as our reference class (Table 2).
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