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Correlation between response and variables was analyzed using the logisticregressionmodel.
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Association analyses were conducted applying Fisher's exact test and logisticregressionmodel.
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A multivariable logisticregressionmodel was used to identify factors associated with CKD.
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Analyses of efficacy were carried out with multilevel mixed effect logisticregressionmodel.
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Genetic risk factors for suicidality were examined in a multinomial logisticregressionmodel.
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Study design: This is a cross-sectional survey study using multivariate ordered logitregression models.
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A stepwise logitregression 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 logitregression 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 logitregression analysis.
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A logitregression was used to estimate the association between hospital characteristics (bed size, location, teaching status, ownership) and having any hospital-affiliated cessation program.
1
A multivariate logisticmodel was used to identify predictors of fetal complications.
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Conclusion: The logisticmodel that we developed meets high standards for discrimination and calibration.
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The propensity score was used as a covariate in a logisticmodel for mortality.
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The model is a modification of the Olson conditional logisticmodel for affected relative pairs.
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We developed a multivariable logisticmodel to identify factors associated with candidaemia in patients with candiduria.
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Attributes were analyzed by conditional logitmodel to determine significant predictors of patient preferences.
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A multinomial logit and a latent class logitmodel was used for the data analyses.
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The analysis relies on a conditional logitmodel to identify the relationship between price and brand choice.
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An ordered logitmodel 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 logitmodel.
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Polytomous logisticregression was used to compare each case group with controls.
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Multivariate logisticregression analysis was used to define risk factors for infection.
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Multivariable logisticregression was used to investigate risk factors associated with ADRs.
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Independent risk factors for early mortality were calculated using multivariate logisticregression.
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Multivariable logisticregression was used to examine factors related to HIV infection.
Usage of logistic models in anglès
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Two multivariable logisticmodels were evaluated to predict the likelihood of PSMs.
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Multivariable three-level logisticmodels with random intercepts were run separately by sex.
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Multivariable logisticmodels were used after adjusting for potentially confounding factors.
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Strength of associations was assessed using odds ratios derived from conditional logisticmodels.
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Results: Five logisticmodels were fitted for the resistance to each of the antibiotic drugs.
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We developed simple logisticmodels and multinomial logisticmodels.
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Case-control differences in self-reporting were analyzed with logisticmodels.
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Based on multivariate logisticmodels, the nomogram was generated.
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Risk factors were confirmed in multivariate logisticmodels.
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Multilevel linear and logisticmodels were constructed adjusting for covariates and accounting for clustering within worksites.
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Ordinal logisticmodels were applied with three groups of response corresponding to three ordered levels of HIV-risk perception.
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Multivariate logisticmodels were used to estimate the association between homeless experience and housed patients with readmission following surgery.
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The concordance indices and the Hosmer- Lemeshow statistics of both logisticmodels indicated a good fit to the observed data.
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The shape of dose-response relationships was assessed using a continuous exposure variable in generalised additive logisticmodels with penalised splines.
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Backward feature selection was used to identify variables to be included in logisticmodels for moderate-severe (scores⩾4) urinary symptoms.
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The Baranyi model appeared to fit the overall experimental data better than did the modified Gompertz and the modified logisticmodels.