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To determine the independent variables correlated with sclerostin (dependent variable), the parameters that correlate significantly in univariate analysis and others that are biologically linked to sclerostin were tested in multiple backward model linear regression analysis.
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Variables with a univariate P < 0.10 were then entered into a multivariate backward stepwise linear regression model for each outcome of interest.
A backward multiple linear regression model was constructed to establish the relationship between each quality of life domain and the variables related to adherence, controlling for covariates.
The variables of education, spouse's occupation, sufficiency of income for expenses, crowding index, primary support source, and ethnicity entered the backward multivariable linear regression model.
We conducted a multivariable analysis in which independent variables associated with health rating scores at a P < 0.20 level in univariate analyses were entered into a backward elimination linear regression model.
Groups were compared by using t tests, and associations were tested by using univariate linear regression (p value and standardized coefficient shown) and in a backward multiple linear regression model with the least significant variables sequentially removed, according to the log-likelihood test.
Indeed, in our backward stepwise linear regression models, we found that DM loses its predictivity on AD when BMI was introduced as cofactor.
The final backward multivariate linear regression models for the relationship between selection factors and demographic characteristics with academic performance throughout the course are outlined in Table 5.
In order to elucidate the predictive value of each variable on AD level we drew different backward stepwise linear regression models (Table 3).
In the last step, we tested different multivariate backward stepwise linear regression models with AD as dependent variable and age, gender, ACC/AHA HF stage, BMI, NT-proBNP, HDL cholesterol and DM as independent variables, to test their independent predictive value.
To investigate whether ADIPOQ single nucleotide polymorphisms (SNPs) were independently associated with plasma adiponectin, we applied a backward linear regression model containing all SNPs that were associated with plasma adiponectin at a false discovery rate <0.05.
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