Sentence examples for bivariate analyses with the from inspiring English sources

Model 2 included covariates that achieved a p-value <0.15 on bivariate analyses with the goal to improve the cross-validation results.

They were also analysed in a series of bivariate analyses with the equivalent recorded trait.

Using Igenity scores in bivariate analyses with the recorded data was more successful in increasing EBV accuracy.

In these analyses, independent variables were chosen on the basis of associations in bivariate analyses with the dependent variable and/or of biological plausibility.

As a first step in the data analysis, we conducted simple linear regressions (bivariate analyses) with the thymic index as the outcome.

Logistic regression analyses were then performed including all independent variables that were associated in bivariate analyses with the respective dependent variable at P≤0.10 and a priori, the variable site.

We selected patient mix and admission characteristics for the second model based on the bivariate analyses with study group and the variable of interest in the model (age, gender and all admission characteristics that could potentially have caused the outcome such as LOS, urgent admission, intensive/intermediate care stay and surgery during admission).

The bivariate analyses (with age and sex included in the model) showed all the potential predictor variables to be statistically significantly associated with persistent LBP at follow-up, with the exception of atopic disease.

We implemented a multivariate logistic regression model adjusted for variables statistically associated with the outcome in bivariate analyses with a risk alpha of 20%%, except for the first-line chemotherapy regimen which was forced in the model.

The majority of those variables were also identified in the bivariate analyses with frequency of daily teeth cleaning (p<0.001), occupational sun exposure (p = 0.001), use of facial cleanser/soap (p = 0.075) and number of household members (p = 0.091), all included in the final multivariate model.

Multivariate Cox proportional hazards regression models were used to estimate the association between predictive variables (those variables that were associated with the outcome in bivariate analyses with P <0.10) and premature AI discontinuation.