Binary logistic regression suggests that nutritional deficiency (OR 0.53 (95% CI 0.41 to 0.70); p<0.001) and diabetes (OR 0.65 (95% CI 0.50 to 0.85); p<0.001) were independent risk factors for non-healing during the study period.
Results from binary logistic regression analysis suggested no significant effect of several clinical parameters in predicting a positive outcome to treatment, although certain conditions, such as chronic migraine, were linked to an increased likelihood of developing an effect from the injection.
In the binary logistic regression analysis, the Hosmer-Lemeshow index suggested discrepancies between the observed and predicted classifications, meaning that 15percentt of the cases were classified wrongly by the model.
Multivariate binary logistic regression was performed for protein panels suggested by PanelComposer, and the respective ROC curves were generated using the R package 'EpiCalc'EpiCalc
However, in a binary logistic regression analysis including all confounders, as suggested by Banasik et al., the association between the level of the seven most prevalent PBDEs (BDEs 28, 47, 66, 99, 100, 153, and 154) and congenital cryptorchidism remained significant (p < 0.039).
Binary logistic regression of UGT1A1*28 with febrile neutropenia suggested that a performance status of 2 or a rectosigmoid tumour origin both significantly increased the risk of febrile neutropenia in the first cycle of IRI treatment (odds: 41.7, P=0.010 and odds: 6.5, P=0.030, respectively) in addition to the association of UGT1A1*28.
Table 4 Binary logistic regression results.
Binary logistic regression was applied [18].
Binary logistic regression analysis confirmed intervention effects exceeded confounding effects.
