Sentence examples for logistic regression with multiple variables from inspiring English sources
The main difficulty is the limitation of the classical linear models and logistic regression with multiple variables to handle the multifactorial effect.
Weights were determined using multiple logistic regression with independent demographic variables selected based on a "Super Learner" algorithm using V-fold cross-validation (van der Laan et al. 2007).
Variables with P < 0.1 are included into multiple logistic regression with backward variable selection method.
We computed the propensityscore by using multiple logistic regression with the dependent variable receiving HR or LT.
*Simple logistic regression with individual level variable.
Relative odds were obtained by use of multiple logistic regression with the same independent variables and coding as GAIL-SBSP (Table 2).
We calculated approximate point and interval estimates of the number needed to be vaccinated to prevent one cervical abnormality at first screening round using multiple logistic regression with adjustment for confounding variables, according to the method of Bender and Blettner.
Study 4: Silva et al. [ 23] (Table 4)> -wrap-foot> anumayrs may not add up to total (24061) because of missing values bOdds ratio calculated from multiple logistic regression with adjustment for all variables on the table * P-value calculated by the log-likelihood ratio This study included 24,061 live births from the the "Birth in Brazil" survey database.
Response probability was assessed by a multiple logistic regression with response as dependent variable (yes/no) and eight demographic and practice related variables as predictors.
33 We assumed 30 40 deaths per year among eligible patients, collecting 3 years' worth of data (90 120 estimated deaths) to appropriately conduct multiple logistic regressions with 11 variables and ensure adequate statistical power.
Covariates included in each multiple logistic regression analysis were selected using a logistic regression with backward elimination, retaining variables that were significant at p <.10.
