Sentence examples for multivariable models but from inspiring English sources

Age, gender, CD4+ T cell count, plasma HIV RNA level, and disease stage (acute versus chronic, treated versus not treated) were all considered potential confounders in multivariable models, but were removed in a stepwise manner if their inclusion changed the beta coefficient of the primary predictor by <10%.

Responders with missing values for any of the variables (except occupation) were excluded from multivariable models, but were included when SIRs were estimated.

Assessing mutually exclusive MHC groups, we found that all three condition groups differed from callers with no MHCs in multivariable models but did not differ from one another.

Interactions between the adolescent predictors and sex were examined in the multivariable models but the evidence for differential sex effects was weak.

The association was statistically significant for Arm C in both univariable and multivariable models, but not for Arms A and B in either model.

Therefore we might have missed studies that included an expectation measure in their multivariable models but did not report it in their abstract because findings were non-significant.

Renal dysfunction was retained in the final multivariable model, but its association with anemia at PICU discharge was not statistically significant.

For males, none were statistically significant in the multivariable model, but item ownership has been included for comparison.

Interactions terms were added in the multivariable model but none were statistically significant and they were not included in the final model.

In addition, results are also presented for the same multivariable model but avoiding adjustment for the year of first exposure, as requested by Farioli et al (2014).

Other variables that were not retained in the multivariable model but were significant in the univariable analysis were age, employment, hand washing as prevention.