Sentence examples for backwards stepwise logistic regression from inspiring English sources

Backwards stepwise logistic regression was used to control for confounding and determine the most parsimonious model for the likelihood of using any type of CM, any CM treatment, and any CM practitioner.

This was confirmed using forwards and backwards stepwise logistic regression.

Univariate and multivariate analyses were undertaken using chi square and backwards stepwise logistic regression respectively.

Backwards stepwise logistic regression was employed to detect determinants of a capillary MFI < 2.6.

Table 5 shows the results of the backwards stepwise logistic regression.

Data were analysed using descriptive statistics and backwards stepwise logistic regression.

All covariates presented as crude ORs (Tables  1 and 2) were included into backwards-stepwise logistic regression to construct a multivariate model defining independent risk factors for poor HRQoL.

Entering all potential predictive sociodemographic and symptom-related factors into a backwards elimination stepwise logistic regression model, along with potential confounding factors such as recent injuries and co-morbidities (which could affect observed MMP9 concentrations) resulted in a final list of factors predictive of neoplasia in the study population (Table 4).

Stepwise logistic regression (backwards elimination) was used to select variables for the final multivariable model.

After checking for multicollinearity (Tolerance > 0.10), all significant predictors (p < 0.10) were included in a stepwise logistic regression model (backwards procedure; Likelihood Ratio).

Stepwise logistic regression analyses, using backwards elimination, with a significance level of 0.2 were conducted for all possible transition probabilities here [ 18].