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Simple comparisons and mixed models of multiple ordinal regression.
The multiple ordinal regression model was adjusted for the objectively (GIS-based) assessed number of green neighborhood qualities.
Multifactor analysis was carried out in a third step based on the categorical dependent variable -objective walking distance- using multiple ordinal regression analysis.
Differences in absolute counts (log-transformed) and relative counts of the test taxa between the chewers and nonchewers, for the chronic periodontitis and healthy periodontium groups separately, were tested using multiple ordinal regression, including demographic variables, other oral habits, and mean plaque index as covariates.
The multiple ordinal regression analysis (Table 2) confirms a statistically significant association between grip strength and both muscle scapular strength (P 0.001, odds ratio [OR] =1.52 of being in a higher category per 1 kg increase in grip strength) and pelvic waist strength (P=0.002, OR =1.16 of being in a higher category per 1 kg increase in grip strength) at the baseline of the study.
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We used multiple ordinal logistic regression analyses to predict categories of SES and identify explanatory genetic and geographic variables.
Simple and multiple ordinal logistic regression models were used for the analyses.
Then, all factors were included in a multiple ordinal logistic regression analysis (proportional odds model) [ 12].
The final full-scale model was based on multiple ordinal logistic regression with backward elimination of non-significant covariates.
Multiple ordinal logistic regression was conducted to determine factors associated with increasing level of HIV related stigma.
Univariate and multiple ordinal logistic regression analyses were performed with the degree of fibrosis as dependent variable, and outcomes were expressed as OR with a 95% CI.
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