Search Results

You are looking at 1 - 10 of 52 items for :

  • "logistic regression" x
Clear All
Open access

Marieke S Velema, Evie J M Linssen, Ad R M M Hermus, Hans J M M Groenewoud, Gert-Jan van der Wilt, Antonius E van Herwaarden, Jacques W M Lenders, Henri J L M Timmers and Jaap Deinum

used a CT scan for this purpose. Statistical analysis In order to identify factors that could predict the presence of PA, we performed multivariable binary logistic regression analysis with a diagnosis of PA as dependent variable. All candidate

Open access

June Young Choi, Jin Wook Yi, Jun Hyup Lee, Ra-Yeong Song, Hyeongwon Yu, Hyungju Kwon, Young Jun Chai, Su-jin Kim and Kyu Eun Lee

univariable and multivariable logistic regression analyses to assess the relationship between VDR expression and clinicopathologic variables. Backward selection method was used in both linear and logistic regression for multiple model fitting. Kaplan

Open access

Akinori Sairaku, Yukiko Nakano, Yuko Uchimura, Takehito Tokuyama, Hiroshi Kawazoe, Yoshikazu Watanabe, Hiroya Matsumura and Yasuki Kihara

were adjusted for potential confounders by using logistic regression models. Factors with an independent association with a mean LA pressure of >18 mmHg was also determined using multiple logistic analyses. All statistical analyses were performed using

Open access

Mabel E Bohórquez, Ana P Estrada, Jacob Stultz, Ruta Sahasrabudhe, John Williamson, Paul Lott, Carlos S Duque, Jorge Donado, Gilbert Mateus, Fernando Bolaños, Alejandro Vélez, Magdalena Echeverry and Luis G Carvajal-Carmona

E (rs10787491, rs932650) and two downstream of G534E (rs10885478, rs1885434), were genotyped in five G534E heterozygous individuals for haplotype analysis. Statistical analysis All genotype frequencies and association testing using logistic

Open access

Karim Gariani, Pedro Marques-Vidal, Gérard Waeber, Peter Vollenweider and François R Jornayvaz

T2DM or IR was modeled using logistic regression. Results were expressed as odds ratio (OR) and (95% confidence interval) using the first quartile as reference. Two multivariable models were applied: the first one adjusted on age and gender; the

Open access

A V Dreval, I V Trigolosova, I V Misnikova, Y A Kovalyova, R S Tishenina, I A Barsukov, A V Vinogradova and B H R Wolffenbuttel

-test or Mann–Whitney U test was used. The differences were considered statistically significant at P <0.05. Logistic regression analysis using a multinomial logit model was used to identify risk factors for diabetes or ECMDs. The independent

Open access

Sanna Mustaniemi, Marja Vääräsmäki, Johan G Eriksson, Mika Gissler, Hannele Laivuori, Hilkka Ijäs, Aini Bloigu, Eero Kajantie and Laure Morin-Papunen

significance was set at a two-sided P value of <0.05. Linear regression (mean differences with 95% CI) was used for continuous variables, and logistic regression (odds ratios (ORs) with 95% CI) was used for categorical variables. Multiple regression models

Open access

Changjiao Yan, Meiling Huang, Xin Li, Ting Wang and Rui Ling

-risk clinicopathologic characteristics, such as extrathyroidal invasion ( P  = 0.732), vascular invasion ( P  = 0.660), distant metastatic ( P  = 1.000) and PTC persistence or recurrence ( P  = 0.134). Multivariate logistic regression analysis of BRAF V600E

Open access

Lena-Maria Levin, Henry Völzke, Markus M Lerch, Jens-Peter Kühn, Matthias Nauck, Nele Friedrich and Stephanie Zylla

, logistic regression models were performed to analyze the associations of circulating chemerin and adiponectin with the two different definitions of hepatic steatosis. To avoid very small coefficients in the regression analyses, we always modeled the change

Open access

Xiujuan Su, Yan Zhao, Zhijuan Cao, Yingying Yang, Tony Duan and Jing Hua

logistic regression models to evaluate the odds ratios (ORs) of women with an IMH diagnosis, compared with euthyroid women. According to the previous studies, we adjusted for the following confounders in multiple-regression models: BMI (<18.5, 18