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aTuberculosis only group used as reference for multinomial model analysis (i.e. comparing influenza only to tuberculosis only and tuberculosis influenza co-infection to tuberculosis only group).
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Multinomial logistic regression model analysis showed no significant associations of improved outcomes with any individual CMs identified, but the inclusion of Acorus gramineus in CM treatment regimens significantly reduced odds of worse outcomes compared to improved outcomes (OR = 0.11, 95% CI 0.01 0.88, p = 0.037).
An ordered multinomial model makes more efficient use of categorical data than a binomial analysis of each category separately.
A preliminary analysis was used to select variables for inclusion in the multinomial model.
This method is similar to the case-case analysis approach widely used for dichotomous tumor characteristics [ 30], extended to the five subtype categories via the multinomial model.
In this context, the Bayesian model is a multinomial model with a uniform Dirichlet prior.
In the sensitivity analysis, the inclusion of additional measures of SES, including individual- and ecological-level variables, had no effect on any of the multinomial model estimates.
The multinomial model controlled for possible within-household influences.
In this section, we present the coarsened multinomial model.
Results from the multinomial model are presented in Table 3.
The multinomial model had excellent fit to the observed results.
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