We analyzed US TB surveillance data, 1993 2010, and used multinomial logistic regression to calculate the association between TST result (0 4 mm [negative], 5 9 mm, 10 14 mm, and ≥ 15 mm) and clinical presentation of disease (miliary, combined pulmonary and extrapulmonary, extrapulmonary only, non-cavitary pulmonary, and cavitary pulmonary).
We used multinomial logistic regression models30 to test whether litter size at den emergence can be predicted from maternal age, storage energy at den entry, energy density at den entry, or certain combinations of these variables (Table 1).
We used multinomial logistic regression models30 on data from 28 pregnant females with known litter sizes to test whether litter size at den emergence can be predicted from maternal age (A), storage energy29 at den entry (E), energy density29 at den entry (E/LBM), or certain combinations of these variables (see Table 1 and Methods for details).
We used multinomial logistic regression models to estimate propensity scores for initiating each antihypertensive drug class.
Next, we used multinomial logistic regression models to estimate odds ratios (OR) for the association between obesity and the selected maternal and neonatal outcomes, either with all obese women as the exposed group or with obese women divided into three categories according to severity.
For the analysis we used multinomial logistic regression.
Risk factors for infection were analyzed using multinomial logistic regression.
Using multinomial logistic regression, we found that the effect estimates for rs715285 in PsA and psoriasis (odds ratio (OR)=1.25 and 0.99, respectively) are significantly different (P=7.05 × 10−7) providing support that this is a PsA-specific risk locus (Table 2).
We compared two sampling techniques (systematic sampling and random sampling) and four intensities for each technique to investigate the driving patterns underlying the changes using multinomial logistic regression.
The Material Identification and Characterization Algorithm (MICA) was used to identify the spectrally-dominant minerals in field samples; these results were combined with ASTER data using multinomial logistic regression to map mineral distributions.
The study examines a digital soil mapping approach for the production of soil maps by using multinomial logistic regression on soil and terrain information at pilot sites in the Northwestern Coastal region of Egypt.
