ライフサイエンスコーパス: logistic regression

1 olume but not low-volume aSAH (multivariable logistic regression).

2 as appropriate) and multivariable analyses (logistic regressions).

3 equency (%) was compared using unconditional logistic regression.

4 c associations with multivariable polytomous logistic regression.

5 study arms were modeled using multivariable logistic regression.

6 tors of CICU mortality were identified using logistic regression.

7 kness loss were identified with multivariate logistic regression.

8 through univariate analysis and multivariate logistic regression.

9 ng linear regression and with glaucoma using logistic regression.

10 ng non-parametric bivariate or multivariable logistic regression.

11 Conventional approaches used logistic regression.

12 for confounders, were estimated by means of logistic regression.

13 se with results generated from multivariable logistic regression.

14 vel between 1-1,000 U/ml was estimated using logistic regression.

15 ons and FTR was evaluated with multivariable logistic regression.

16 d adjusted risk differences (ARDs) following logistic regression.

17 sion (<1000 copies per mL) at 6 months using logistic regression.

18 R, which absorbs the merits of both SCCA and logistic regression.

19 s associated with eGFR <90 mL/min/1.73 m2 by logistic regression.

20 of women using contraception with fractional logistic regression.

21 ment and severity was examined using ordinal logistic regression.

22 ons and mortality were assessed using binary logistic regression.

23 tal mortality were assessed in multivariable logistic regression.

24 II (2012-2013; N=36,309) were analyzed using logistic regression.

25 tors associated with PIR were assessed using logistic regression.

26 able predictors of cPR were identified using logistic regression.

27 mphetamine using bivariate and multivariable logistic regression.

28 score matching and multilevel, multivariable logistic regression.

29 iminate between phenotypes was assessed with logistic regression.

30 idence intervals (CIs) were determined using logistic regression.

31 redicting mortality using backwards stepwise logistic regression.

32 d interactions were determined by linear and logistic regressions.

33 d marbling values were verified by linear or logistic regressions.

34 t, were determined using univariate Bayesian logistic regressions.

35 l (0.780), and [Formula: see text] penalized logistic regression (0.780).

36 In binary logistic regression, a cICA-PO was independently associa

37 for association with disease severity using logistic regression adjusted for age and gender.

38 We used unconditional logistic regression adjusted for the matching factors to

39 ol/mol]), were estimated using multivariable logistic regression adjusted for the same hypothesised c

40 er first-ever intracerebral hemorrhage using logistic regression, adjusting for known predictors of o

41 by ancestry was assessed using multivariate logistic regression, adjusting for parity, and maternal

42 Logistic regression analyses adjusting for age, all the

43 Adjusted logistic regression analyses and generalized estimating

44 GBD) super-regions, with adjusted linear and logistic regression analyses examining associations with

45 -19 after their stroke, were included in two logistic regression analyses examining which features we

46 Univariate and multivariate logistic regression analyses have been performed for bot

47 Logistic regression analyses showed that the clip use di

48 We performed multiple linear and logistic regression analyses to determine whether HIV/HC

49 escriptive analyses and multivariable binary logistic regression analyses were conducted on weighted

50 Logistic regression analyses were performed and adjusted

51 Multivariable logistic regression analyses were performed to assess th

52 Univariable and multivariable logistic regression analyses were performed to identify

53 Multivariate logistic regression analyses were undertaken.

54 Multivariable logistic regression analyses were used.

55 e obtained for the entire lung, and multiple logistic regression analyses with areas under the curve

56 (descriptive, sequence pattern analyses, and logistic regression analyses) aimed to detect any combin

57 In multivariable-adjusted conditional logistic regression analyses, better adherence to the Me

58 ng novel intracountry risk-adjusted UR trend logistic regression analyses, can be translated to other

59 es, LSI, and FLD were assessed in linear and logistic regression analyses.

60 le odds ratios (OR) for CAD from conditional logistic regression analyses.

61 etric MRI signal was compared with NWU using logistic regression analyses.

62 f PnR success at 12 months in a multivariate logistic regression analysis (P = 0.006).

63 Multiple logistic regression analysis demonstrated that increased

64 Logistic regression analysis identified cervical spinal

65 Binary logistic regression analysis proposed a mid-trimester bi

66 However, ordinal logistic regression analysis revealed that a higher abun

67 Univariate logistic regression analysis revealed that an adenoma co

68 Multiple logistic regression analysis showed that associated fact

69 A logistic regression analysis showed that the following v

70 Multivariate logistic regression analysis showed women with hydrosalp

71 umab under local sedation using multivariate logistic regression analysis to control for potentially

72 Logistic regression analysis was applied to estimate the

73 Weighted multivariable logistic regression analysis was then used to develop a

74 Logistic regression analysis was undertaken to identify

75 Logistic regression analysis was used to evaluate the di

76 Multivariate ordinal logistic regression analysis was used to predict the pre

77 Univariate and multivariate logistic regression analysis were performed to identify

78 oth a post hoc Bayesian analysis and a mixed logistic regression analysis were performed.

79 values of less than 0.1 were considered for logistic regression analysis which identified predictors

80 Using multivariate logistic regression analysis with leave-1-out cross vali

81 In a multivariable logistic regression analysis, an overlap between the abl

82 aracteristic analysis, time-series analysis, logistic regression analysis, and multilevel modeling fo

83 In multivariable logistic regression analysis, higher baseline IOP predic

84 In logistic regression analysis, independent factors associ

85 In the multivariate logistic regression analysis, individuals living in area

86 In multivariable logistic regression analysis, risk factors for severe in

87 In a multivariable logistic regression analysis, we investigated the risk o

88 Multivariable logistic regression analysis, with synthetic minority ov

89 ratios (ORs) were calculated as part of the logistic regression analysis.

90 ated cirrhosis and HCC were determined using logistic regression analysis.

91 nd risk factors for EE were identified using logistic regression analysis.

92 cular factors, and included in multivariable logistic regression analysis.

93 te AMD patients and control individuals with logistic regression analysis.

94 We used ordinal logistic regression and applied generalized estimating e

95 Ordinal logistic regression and bootstrapped backwards selection

96 Multivariable logistic regression and Cox proportional hazards models

97 postoperative mortality was evaluated using logistic regression and Cox proportional hazards models.

98 Multivariate stepwise logistic regression and Cox proportional-hazard models w

99 We used multiple logistic regression and difference-in-differences method

100 into development and validation cohorts: the logistic regression and gradient boosting machine models

101 tions between ESW and AR using multivariable logistic regression and interval-censored survival analy

102 under the curve for viral RNA shedding using logistic regression and Kaplan-Meier analyses.

103 Multinomial logistic regression and linear regression were used to r

104 Binary logistic regression and multivariate analysis were condu

105 hbor, support vector machine, random forest, logistic regression and Naive Bayes.

106 To this end, both linear logistic regression and nonlinear Random Forest classifi

107 Logistic regression and random forests using diagnostic

108 Performance of the ANN was evaluated against logistic regression and the standard grading system by a

109 patients with active TB were compared using logistic regression and time-to-event analyses.

110 btype specific risks were estimated by using logistic regression, and absolute risks were calculated.

111 tion, and delayed graft function (DGF) using logistic regression, and length of stay (LOS) using nega

112 expression prediction methods and two novel logistic regression approaches across five GTEx v8 tissu

113 Logistic regression assessed the relationship between in

114 In the multiple logistic regressions, BMI >=27.0 kg/m(2) , WC >=90.0 cm

115 Odds ratios were estimated using conditional logistic regression by comparing the occurrence of switc

116 The logistic regression coefficients were identical between

117 Multivariable logistic regression controlling for demographic and clin

118 Multivariable logistic regression found corneal profile and IOL type t

119 s, of which 38 loci would be missed within a logistic regression framework with a binary phenotype de

120 Using logistic regression, higher fruit and high vegetable den

121 ure to cure was analyzed using multivariable logistic regression in the total population and in salva

122 were estimated using mixed-effects linear or logistic regression, including a random effect to adjust

123 Logistic regression incorporating respondent-driven samp

124 For this study, traditional multivariate logistic regression (LR) identified seven predictors of

125 Multivariable logistic regression (LR) was used to estimate the risk a

126 Using multinomial logistic regression (MLR), we compared the 3 panels on t

127 tic curve (0.78 [95% CI 0.77-0.78]) than the logistic regression model (0.73 [0.72-0.74]) (p < 0.001)

128 brillation discrimination in a multivariable logistic regression model (C-statistic 0.82 vs 0.78; p =

129 e advantage of the fact that the conditional logistic regression model (i.e. the SSF) is likelihood-e

130 There was no difference in logistic regression model accuracy comparing the data by

131 A logistic regression model after the intention-to-treat p

132 A multivariable logistic regression model calculated the odds ratio (OR)

133 The logistic regression model combining T2-weighted SI ratio

134 The binary logistic regression model included the minimal corneal t

135 ccuracy can be achieved using a multivariate logistic regression model of MRI parameters after thresh

136 We evaluated a multivariable logistic regression model predicting 5-year survival der

137 A logistic regression model revealed that CCC was associat

138 A logistic regression model showed that non-obese patients

139 ed as 1 minus the adjusted odds ratio from a logistic regression model that compared vaccination hist

140 In a multiple logistic regression model the factor wound irrigation wi

141 We used logistic regression model to develop Model 1 by retainin

142 ayesian multivariate response random effects logistic regression model to simultaneously examine vari

143 A logistic regression model trained using samples collecte

144 In this study, a logistic regression model was developed to quantify the

145 Descriptive statistics and a binary logistic regression model were used to analyze the data.

146 opulation was calculated, and a multivariate logistic regression model with random intercepts was use

147 ortality was estimated using a multivariable logistic regression model, adjusting for age, sex, indig

148 In a binary logistic regression model, young age (P = 0.033), histor

149 [WSS]) to identify relevant parameters for a logistic regression model.

150 tive score were obtained using multivariable logistic regression model.

151 ar versus surgical revascularization using a logistic regression model.

152 ortality, was analyzed using a multivariable logistic regression model.

153 fier were 94.2%, 96.9%, 97%, and 94% for the logistic regression model; 92.7%, 100%, 100%, and 92.9%

154 Multivariable conditional logistic regression modeling compared the odds of underg

155 onse were assessed using a linear and binary logistic regression modeling for the continuous and cate

156 Multinomial logistic regression modeling indicated that Drymarchon c

157 By fitting three multiple logistic regression models (one for each delivery mode),

158 We computed logistic regression models adjusted for age, sex, BMI, s

159 We used conditional logistic regression models adjusted for HDL cholesterol

160 Logistic regression models and accuracy performance test

161 We used interrupted time series logistic regression models and estimated marginal effect

162 eloping a series of multilevel multivariable logistic regression models and geospatially visualising

163 nd nephrectomy type (partial/radical)-to fit logistic regression models and grouped patients accordin

164 Adjusted logistic regression models and meta-analyses were perfor

165 Logistic regression models and random forest models clas

166 Logistic regression models assessed correlates of non-RT

167 Logistic regression models combining T2-weighted SI and

168 ated multivariate ordinary least squares and logistic regression models controlling for a wide range

169 In both multivariable logistic regression models correcting for propensity sco

170 Logistic regression models estimated ORs of scoring low

171 y body mass index (BMI) <25 or >=25 kg/m(2); logistic regression models evaluated preconception lipid

172 Logistic regression models evaluated the relation of bas

173 ared by transition status, and multivariable logistic regression models examined factors associated w

174 Multivariable logistic regression models tested associations of geogra

175 postoperative outcomes, we used multivariate logistic regression models to adjust for clinical and de

176 an index encounter, and we used multivariate logistic regression models to assess demographic and cli

177 models, and, in a post-hoc analysis, we used logistic regression models to assess the association bet

178 Logistic regression models to determine covariate risk c

179 We built logistic regression models to test for associations betw

180 ted AUC for glaucoma versus nonglaucoma from logistic regression models using MRW-BMO values from all

181 Individual univariable and multivariable logistic regression models were assessed for each time-w

182 Hierarchical multivariable logistic regression models were constructed to evaluate

183 Univariate and multivariate logistic regression models were created.

184 Logistic regression models were developed using lncRNAs

185 Logistic regression models were fitted to determine the

186 Conditional logistic regression models were used to adjust for poten

187 Logistic regression models were used to analyze risk fac

188 Univariable and multivariable logistic regression models were used to assess predictor

189 Multivariable Cox and logistic regression models were used to assess the indep

190 Adjusted path analysis logistic regression models were used to assess the role

191 Unconditional logistic regression models were used to calculate lung c

192 Hierarchical logistic regression models were used to determine associ

193 Unconditional logistic regression models were used to estimate odds ra

194 Polytomous logistic regression models were used to estimate ORs and

195 Multivariable logistic regression models were used to estimate the odd

196 Logistic regression models were used to estimate the odd

197 Logistic regression models were used to evaluate associa

198 Logistic regression models were used to examine the effe

199 Multivariable logistic regression models were used to provide adjusted

200 Logistic regression models were utilized to estimate the

201 We used multivariable logistic regression models with medical school-specific

202 ere computed using multivariable conditional logistic regression models, according to center, sex, ag

203 Using multivariable-adjusted conditional logistic regression models, caffeic acid (ORlog2: 0.55;

204 tics associated with results reporting using logistic regression models, described sponsor-level repo

205 estigated using three-level random-intercept logistic regression models, showing no differences in ne

206 In logistic regression models, the likelihood that RGS was

207 socioeconomic disadvantage with hierarchical logistic regression models, using practices serving the

208 ion criterion in a stepwise fashion to build logistic regression models, which were then translated i

209 ith in-hospital mortality was analyzed using logistic regression models.

210 eralized estimation equations and multilevel logistic regression models.

211 a infection and symptomatic infection we use logistic regression models.

212 diagnosis were calculated using multivariate logistic regression models.

213 risk factors with incident PD using adjusted logistic regression models.

214 ociations were estimated using multivariable logistic regression models.

215 efore age 1 were compared between groups via logistic regression models.

216 the groups were compared using multivariable logistic regression models.

217 ession and predicted response to ICS through logistic regression models.Measurements and Main Results

218 Conditional logistic regression odds ratios (ORs) accounting for ind

219 Multivariate logistic regression of the retrospective cohort demonstr

220 pendently, (ROC analysis, followed by binary logistic regression) only Ultrasound depth is a signific

221 Using logistic regression, our study further demonstrated that

222 After adjustment using multivariable logistic regression, patients in the high-risk group wer

223 ity of illness and should be dosed "enough," logistic regression, propensity score matching, and inve

224 ate analyses using traditional multivariable logistic regression, propensity score matching, propensi

225 Logistic regression, random forest, and support vector m

226 redictive algorithms were developed based on logistic regression, random forests, gradient boosted tr

227 Leveraging logistic regression-, random forest- and gradient boosti

228 dress the problem: sparse label-noise-robust logistic regression (Rlogreg), robust elastic net based

229 Multivariate logistic regression selected combined serum alpha-fetopr

230 Stepwise logistic regression selected four features from PSPR as

231 Logistic regression showed increasing odds of respirator

232 Multiple logistic regression showed that all algorithm parameters

233 n: Despite the heterogeneous patient cohort, logistic regression TCP models showed a strong associati

234 (n = 8,327), we used adjusted multivariable logistic regression to assess the associations of each c

235 We used multivariable conditional logistic regression to calculate odds ratios (ORs).

236 outcomes using random effects multivariable logistic regression to control for confounding.

237 We used mixed-effects logistic regression to estimate associations between eac

238 ffects models to estimate tree densities and logistic regression to estimate mortality by size class.

239 We used conditional logistic regression to estimate odds ratio (OR) and 95%

240 ied case-crossover analysis with conditional logistic regression to estimate the association between

241 We used conditional logistic regression to estimate unadjusted and multivari

242 We used multivariable logistic regression to examine associations between co-r

243 We used multivariable logistic regression to examine associations between soci

244 We used logistic regression to examine likelihood of second fill

245 time trends during the study period and used logistic regression to examine sociodemographic and clin

246 We used multinomial logistic regression to generate covariates of care and v

247 We also used multinomial logistic regression to identify factors associated with

248 We used multivariate logistic regression to identify factors associated with

249 We used multivariable ordinal logistic regression to identify factors associated with

250 he intervention group, we used multivariable logistic regression to identify patient and medication c

251 chest, or orthopedic and used multivariable logistic regression to model 30-, 90-, and 180-day posto

252 multiple imputation for missing covariates, logistic regression to model the association between PFA

253 ation or marriage), and first birth and used logistic regression to show the change in prevalence of

254 rotective titres were estimated using scaled-logistic-regression to model pre-transmission titre agai

255 54 algorithms, the best performing model was logistic regression, using 1000 features, 100 stop words

256 set of independent variables was selected by logistic regression, using the derivation set to create

257 Multivariate logistic regression was performed controlling for factor

258 Multiple logistic regression was performed on demographic and ana

259 Hierarchical logistic regression was performed to account for cluster

260 Multivariate logistic regression was performed to account for confoun

261 Logistic regression was performed to assess associations

262 Logistic regression was performed to assess the predicti

263 Multivariate logistic regression was performed to define risk factors

264 Logistic regression was performed to determine the signi

265 Univariable and multivariable logistic regression was performed to identify TO predict

266 Stepwise logistic regression was performed to select the optimal

267 association between MetS components and DII Logistic regression was used (P > 0.05).

268 ive medical records review was performed and logistic regression was used to assess OPAT and other ou

269 Logistic regression was used to assess ORs with 95% CIs.

270 Survey-adjusted logistic regression was used to compare the odds for in-

271 Logistic regression was used to compare the odds of preg

272 Conditional logistic regression was used to create models of associa

273 Multivariate logistic regression was used to create odds ratios compa

274 Multivariable logistic regression was used to derive adjusted odds of

275 Conditional logistic regression was used to estimate odds ratios (OR

276 Multinomial logistic regression was used to estimate ORs and 95% CIs

277 Multivariable logistic regression was used to evaluate knowledge of th

278 Logistic regression was used to evaluate the relationshi

279 Multilevel mixed effects logistic regression was used to examine relationships fo

280 Multivariable logistic regression was used to identify factors associa

281 Multivariate logistic regression was used to identify factors predict

282 Multivariable logistic regression was used to identify risk factors fo

283 Multivariable logistic regression was used to investigate patient fact

284 Multivariable logistic regression was utilized to assess the associati

285 sing descriptive statistics and multivariate logistic regression, we examined the association (P < .0

286 te, a case-crossover design, and conditional logistic regression, we examined the association between

287 Backward selection and multivariate logistic regression were conducted to assess risk of GI

288 Single factor analysis and logistic regression were performed, and a composite risk

289 escriptive statistical analysis and multiple logistic regression were performed.

290 Univariate and multivariate logistic regression were used to characterize factors as

291 The Fisher exact test and multivariable logistic regression were used to evaluate association of

292 Individual logistic regressions were performed for 12-month mortali

293 Univariate and multivariate logistic regressions were performed, and population attr

294 evaluated using network analysis; linear and logistic regressions were used to compare groups based o

295 Descriptive statistics and multivariate logistic regressions were used to examine associations b

296 C groups and risk factors for fatal outcome (logistic regression) were evaluated.

297 r combinations to predict GGG 1 vs >1, using logistic regression with a nested leave-pair out cross v

298 ectiveness (VE) was estimated by conditional logistic regression, with adjustment for reported contac

299 h outcomes were determined using conditional logistic regression within surveys, adjusting for prespe

300 Logistic regression yielded adjusted odds ratios (ORs) p