Sentence examples for multiple conditional regression from inspiring English sources

We adjusted for covariables by performing multiple conditional regression analysis.

In a multiple conditional regression model adjusting for exposure to abacavir, NNRTI, and PI sCD163 remained non significant (OR 1.14, CI 0.92 – 1.40, p = 0.22).

Univariate conditional logistic regression resulted in significant differences in the mean bone conduction hearing loss and in the total score of HHIE-S, in the multiple conditional regression model, only smoking (p<0.0001) remained significant.

In multiple conditional regression analyses adjusted for HLA-SE, PTPN22 1858T variant and smoking, respectively, only the odds ratio for IgM anti-CCP increased to 3.89, 95% CI 1.36 to 11.10, P < 0.05 when adjusted for the PTPN22 1858T variant.

In a multiple conditional regression model adjusting for HIV RNA sCD163 remained non significant (OR 1.20, CI 0.96 – 1.51, p = 0.11) To evaluate the possible role of sCD163 in sample 4 as a predictor of MI we constructed a ROC curve for sCD163 which gave an area under the curve of 0.57 (CI 95% 0.48-0.65 p = 0.14), Figure  2.

In a multiple conditional backwards regression analysis including all of the significant variables from the simple regression analyses, IgG ACPAs (OR 3.46, 95% CI 1.94 to 6.18, p<0.0001) and IgM RF (OR 1.71, 95% CI 1.06 to 2.76, p<0.05) were the remaining significant variables that distinguished patients with RA from first-degree relatives within the families.

Odds ratio (OR) and 95% confidence interval (CI) were estimated using multiple conditional logistic regression models.

Multiple conditional logistic regression, including robust standard errors to account for relatedness by sibship, was used to identify covariates associated with being core antibody positive.

+ ; Multiple conditional logistic regression with Bonferroni's correction.

Multiple conditional logistic regression analyses were performed for the significant variables.

A nested case-control design, using a multiple conditional logistic regression model, was used in order to overcome those limitations.