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логит регрессия Portuguese
regressão logística Spanish
regresión logistica Catalan
regressió logística 1
Study design: This is a cross-sectional survey study using multivariate ordered logit regression models.
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A stepwise logit regression procedure was performed in order to determine the subset of measures which best distinguish ARMS subjects from HC.
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HIV stigma was not found to be a significant predictor when binary logit regression was run with medication adherence at 1 year.
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Our findings are tempered by a small sample size and lack of a direct relationship between stigma and adherence on binary logit regression analysis.
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A logit regression was used to estimate the association between hospital characteristics (bed size, location, teaching status, ownership) and having any hospital-affiliated cessation program.
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A multivariate logistic model was used to identify predictors of fetal complications.
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Conclusion: The logistic model that we developed meets high standards for discrimination and calibration.
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The propensity score was used as a covariate in a logistic model for mortality.
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The model is a modification of the Olson conditional logistic model for affected relative pairs.
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We developed a multivariable logistic model to identify factors associated with candidaemia in patients with candiduria.
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Attributes were analyzed by conditional logit model to determine significant predictors of patient preferences.
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A multinomial logit and a latent class logit model was used for the data analyses.
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The analysis relies on a conditional logit model to identify the relationship between price and brand choice.
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An ordered logit model was used to assess the relationship between sociodemographic factors and the number of DAREs performed.
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The probability of having a pregnancy complicated by pre-eclampsia was also calculated by means of a multivariate logit model.
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Polytomous logistic regression was used to compare each case group with controls.
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Multivariate logistic regression analysis was used to define risk factors for infection.
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Multivariable logistic regression was used to investigate risk factors associated with ADRs.
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Independent risk factors for early mortality were calculated using multivariate logistic regression.
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Multivariable logistic regression was used to examine factors related to HIV infection.
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Two multivariable logistic models were evaluated to predict the likelihood of PSMs.
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Multivariable three-level logistic models with random intercepts were run separately by sex.
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Multivariable logistic models were used after adjusting for potentially confounding factors.
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Strength of associations was assessed using odds ratios derived from conditional logistic models.
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Results: Five logistic models were fitted for the resistance to each of the antibiotic drugs.
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Correlation between response and variables was analyzed using the logistic regression model.
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Association analyses were conducted applying Fisher's exact test and logistic regression model.
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A multivariable logistic regression model was used to identify factors associated with CKD.
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Analyses of efficacy were carried out with multilevel mixed effect logistic regression model.
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Genetic risk factors for suicidality were examined in a multinomial logistic regression model.
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Factors associated with ID were analysed using multivariate logistic regression model.
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Factors associated with DM were analyzed using a logistic regression model.
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Statistical analysis employed univariate analysis and a multivariate logistic regression model.
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Bivariate analysis was conducted using Pearson's Chi-square, and logistic regression model was run.
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A multiple logistic regression model with generalized estimation equation was used.
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Patients were matched on a propensity score generated from a logistic regression model.
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In the logistic regression model, erythrocyte SOD1 activity was associated with ICU mortality.
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Independent predictors of disease were studied by a logistic regression model.
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Differences were measured with a logistic regression model considering random effects.
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A multivariable logistic regression model was used to identify factors associated with pneumococcal vaccination.
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Risk factors for the primary outcome were assessed in a multivariable logistic regression model.