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A Bayesian binary regression analysis was used to generate a predictor of oxaliplatin sensitivity from the gene expression data.
Bayesian probit binary regression analysis was then performed on the datasets, and the resultant probability values generated.
Prediction analysis of the expression data based on the supervised binary regression analysis was performed using MATLAB software as previously described [9].
Univariate and multivariate binary regression analysis was applied to identify predictors of AKI and WRF.
A binary regression analysis was applied and the pooled RRs and 95% CIs were estimated.
Linear and binary regression analysis was performed to determine univariate predictors (independent variable) of the SEVR (dependent variable) in patients with RA (see Table 2).
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The result from the binary regression analysis is shown in Table 6.
For categorical variables, χ test, Fisher exact test, or binary logistic regression analysis was used; for continuous variables, binary logistic regression analysis was used.
Binary logistic regression analysis was used to identify the predictors of nursing students' teaching patients about sleep hygiene.
A multivariable binary logistic regression analysis was conducted to evaluate the relations between hostility and telomere length, while controlling for sociodemographic, military, and clinical variables.
A binary logistic regression analysis was used to investigate the factors thought to influence immunocompetence.
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