ライフサイエンスコーパス: logistic regression

1 ptoms was obtained through bivariate ordered logistic regression.

2 statistics and multivariate random intercept logistic regression.

3 Frequencies were compared using logistic regression.

4 ile and RHOA was modeled using multivariable logistic regression.

5 al HIV transmission for each biomarker using logistic regression.

6 with second opinion use were evaluated using logistic regression.

7 experiences were studied using multivariate logistic regression.

8 ression and multilevel mixed-effects ordinal logistic regression.

9 zed cutoff points was estimated with ordinal logistic regression.

10 isk types, and any HPV were calculated using logistic regression.

11 struction within asthmatics via multivariate logistic regression.

12 tor common data elements were explored using logistic regression.

13 modality were identified using multivariable logistic regression.

14 n with and without CHD by using multivariate logistic regression.

15 ith the chi(2) test, the Student t test, and logistic regression.

16 ed care-were assessed by using multivariable logistic regression.

17 e identified using multivariable conditional logistic regression.

18 ignancy were evaluated by using multivariate logistic regression.

19 residual cancer cells) were evaluated using logistic regression.

20 score </=2 was estimated using multivariable logistic regression.

21 outcome was investigated with multivariable logistic regression.

22 dentified trajectory classes was assessed by logistic regression.

23 ) using Cox proportional hazard analyses and logistic regression.

24 ) concentrations at place of residence using logistic regression.

25 ormance of fitted models, was estimated from logistic regression.

26 shrinkage and selection operator regularized logistic regression.

27 mperature were estimated using multivariable logistic regression.

28 ide polymorphisms, and myopia estimated from logistic regression.

29 s ratios were calculated using multivariable logistic regression.

30 e categories were examined using multinomial logistic regression.

31 ce/escape were identified using multivariate logistic regression.

32 unvaccinated participants via multivariable logistic regression.

33 ing interventions to SOC using multivariable logistic regression.

34 and multivariate analysis was performed with logistic regression.

35 ith other potential predictors in a stepwise logistic regression.

36 14 days of LVAD were assessed with stepwise logistic regression.

37 tors for erosive tooth wear were assessed by logistic regression.

38 endently associated with PNF on multivariate logistic regression.

39 odds of cancer of two consecutive scores by logistic regression.

40 ma or death or until December 31, 2010, with logistic regression.

41 eographically matched controls by univariate logistic regression.

42 rgic disease were estimated with multinomial logistic regressions.

43 Inverse probability-weighted logistic regression accounting for age, sex, emergency m

44 We used logistic regression adjusted by age, sex, and study desi

45 nfidence intervals (CIs) using unconditional logistic regression adjusted for confounders.

46 matched odds ratios (mORs) using conditional logistic regression adjusted for maternal age and educat

47 We used logistic regression, adjusted for age, sex, race, state

48 athway abundances and risk using conditional logistic regression adjusting for BMI, smoking, and alco

49 h uptake in the non-incentivised group using logistic regression, adjusting for community and number

50 -month culture conversion using multivariate logistic regression after adjusting for MIC.

51 were then tested with the use of multinomial logistic regression.An ED, HF, and LFD dietary pattern h

52 We conducted conditional logistic regression analyses adjusted for body mass inde

53 Logistic regression analyses based on a conceptual model

54 Logistic regression analyses examined the association be

55 Logistic regression analyses examined the number of past

56 Using multivariate logistic regression analyses four SNPs were significantl

57 Logistic regression analyses revealed a strong/independe

58 Bivariable logistic regression analyses suggested that high viral l

59 We performed univariate logistic regression analyses to assess the association b

60 We used conditional logistic regression analyses to estimate odds ratios for

61 We used logistic regression analyses to estimate the strength of

62 (univariate and bivariate) and multivariable logistic regression analyses to longitudinal health insu

63 Multivariable logistic regression analyses were applied to determine w

64 Multivariable logistic regression analyses were conducted.

65 Logistic regression analyses were performed to evaluate

66 tion and regression tree (CART) analysis and logistic regression analyses were performed to identify

67 Univariate tests and logistic regression analyses were performed, studying th

68 Multivariable logistic regression analyses were undertaken.

69 Logistic regression analyses were used to identify deter

70 e compared between groups using multivariate logistic regression analyses, adjusting for maternal age

71 breast cancer were assessed in multivariable logistic regression analyses.

72 I, -0.29 to 0.14; P = .49) nor by univariate logistic regression analysis (odds ratio, 0.64; 95% CI,

73 95% CI, 0.22-1.67; P = .37) or multivariate logistic regression analysis (odds ratio, 1.09; 95% CI,

74 ic and non-atopic children were estimated by logistic regression analysis adjusting for potential con

75 Univariate and mixed-effects logistic regression analysis controlling for center effe

76 In mixed-effects logistic regression analysis controlling for patient, in

77 Logistic regression analysis determined the predictors o

78 analysis for clinical outcome parameter and logistic regression analysis for postsurgical complicati

79 We used logistic regression analysis for remission and zero-infl

80 We did a logistic regression analysis of cannabis use from retros

81 n in any of the LS genes by using polytomous logistic regression analysis of clinical and germline da

82 Introducing these variables to a logistic regression analysis showed areas under the rece

83 Logistic regression analysis showed that a decrease in O

84 Multiple logistic regression analysis showed that female gender,

85 Results from stepwise logistic regression analysis showed that five biomarkers

86 Multivariable logistic regression analysis showed that neither contras

87 We performed multinomial logistic regression analysis to assess the weighting of

88 values of less than 0.1 were considered for logistic regression analysis to identify predictors of m

89 Multiple logistic regression analysis was conducted to determine

90 Multivariate logistic regression analysis was performed for associati

91 Logistic regression analysis was performed to assess pot

92 Multivariable logistic regression analysis was performed to determine

93 A conditional logistic regression analysis was performed to evaluate t

94 Multiple logistic regression analysis was used to estimate adjust

95 Multivariable ordinal logistic regression analysis with an interaction term wa

96 urgery: risk factors at baseline (univariate logistic regression analysis) included longer total dura

97 In a multivariable logistic regression analysis, only moderate to severe br

98 On multivariate logistic regression analysis, PSMA and serum PSA signifi

99 In a logistic regression analysis, ulcers were identified to

100 Multivariable logistic regression analysis, using the calculated prope

101 tinuation of therapy were identified using a logistic regression analysis.

102 ing robotic surgery or CR-POPF occurrence on logistic regression analysis.

103 readmission risk was evaluated by multilevel logistic regression analysis.

104 within the following 24 hours, we performed logistic regression analysis.

105 factors associated with 30-day mortality in logistic regression analysis.

106 r unbalanced covariates, we used conditional logistic regression and a repeated measures model to com

107 Results of logistic regression and areas under the curve were compa

108 rimination was similar for the multivariable logistic regression and CHAID tree models, with both bei

109 nd 95% confidence intervals for asthma using logistic regression and correcting for the known samplin

110 nd overall survival (OS), was assessed using logistic regression and Cox models, respectively.

111 Logistic regression and generalized estimating equations

112 Conditional logistic regression and matched concordance indices (mC)

113 were examined using multilevel mixed-effects logistic regression and multilevel mixed-effects ordinal

114 to predict MDD, using area under the curve, logistic regression, and linear mixed model analyses, wa

115 This analysis demonstrates a simple logistic regression approach for testing a priori hypoth

116 Although standard logistic regression approaches were predictive, they wer

117 Multivariate logistic regression assessed sociodemographic, medical,

118 In multivariate logistic regression, cerebral abscess was associated wit

119 In logistic regression, consistent acute exacerbations (>/=

120 Logistic regressions controlled for sociodemographic, cl

121 ntion-to-treat analysis, using multivariable logistic regression controlling for potential confounder

122 In multivariable logistic regression, cortical superficial siderosis burd

123 Multivariable logistic regression examined sociodemographic and clinic

124 Logistic regression explored predictors of depression fr

125 Using multivariate logistic regression, factors independently associated wi

126 ical analysis was performed with conditional logistic regression for binary outcomes and the stratifi

127 eviously developed a network-based penalized logistic regression for correlated methylation data, but

128 Exploratory analysis via binary logistic regression found a potential association betwee

129 In multivariable logistic regression, high safe patient handling behavior

130 Logistic regression identified that dwell time was the o

131 ctors on >/=2-step DRSS score improvement by logistic regression in an integrated VISTA and VIVID dat

132 ty selection algorithm with a L1-regularized logistic regression kernel and were then fitted with log

133 On multivariate logistic regression, lower baseline GDF-15 was associate

134 ance with models built with state-of-the-art logistic regression methods.

135 A multiple logistic regression model incorporating oxygenation inde

136 A multiple logistic regression model predicting odds of successful

137 A multivariate logistic regression model predicting referral to PC was

138 alysis was used to identify covariates for a logistic regression model predictive of severe ADAMTS13

139 We used a multivariable logistic regression model to compute the conditional pro

140 cific transcripts, we used a cross-validated logistic regression model to identify the presence of HC

141 A Bayesian hierarchical logistic regression model was applied to estimate the no

142 A multivariable logistic regression model was constructed to quantify th

143 To adjust for selection bias, a logistic regression model was created to estimate odds r

144 A multiple logistic regression model was estimated at implant and p

145 A propensity score-weighted logistic regression model was used to adjust for confoun

146 A hierarchical logistic regression model was used to identify predictor

147 In a hierarchical logistic regression model, a routine of early discharge

148 ected mortality was obtained from multilevel logistic regression model, adjusting for demographics, m

149 Next, we created a logistic regression model, controlling for comorbidity a

150 In a logistic regression model, more catatonia signs were ass

151 In fully-adjusted logistic regression model, the odds ratio (OR) per 10 un

152 e to RSV were assessed using a hierarchical, logistic regression model.

153 Results were analyzed using a logistic regression model.

154 prior melanomas was analyzed using an exact logistic regression model.

155 Multivariable logistic regression modeling assessed the independent ef

156 test, the t test, the Mann-Whitney test, and logistic regression modeling of sample adequacy were per

157 a pooled dataset and then used mixed-effects logistic regression modeling to determine the effect of

158 Logistic regression modeling was used to examine associa

159 Multivariable logistic regression modelling was used to identify predi

160 stical methods using 2 independently derived logistic regression models (a de novo model and an a pri

161 nt amelanotic melanomas were evaluated using logistic regression models adjusted for age, sex, study

162 For each outcome, we estimated conditional logistic regression models adjusting for race/ethnicity,

163 Conditional logistic regression models adjusting for risk factors ev

164 Conditional logistic regression models adjusting for serum cotinine

165 e extracted and analyzed to fit multivariate logistic regression models and build a risk calculator.

166 Bivariate and multivariate logistic regression models and Odds ratio with 95% inter

167 Multivariable logistic regression models assessed independent associat

168 periodontal severity with linear and ordinal logistic regression models before and after adjusting fo

169 f the radiosensitive variable improved lasso logistic regression models compared to model performance

170 We used published data to create logistic regression models comparing annual trends in th

171 Multivariable logistic regression models fitted the association of age

172 nd postguideline periods in the hierarchical logistic regression models for all of the risk groups.

173 Logistic regression models identified characteristics as

174 Multiple logistic regression models revealed that combining the f

175 In logistic regression models stratified by race, the media

176 Logistic regression models tested any independent relati

177 ommonly used risk metrics have been based on logistic regression models that incorporate aspects of t

178 METHOD: The authors used logistic regression models to assess prospective associa

179 regression kernel and were then fitted with logistic regression models to classify steatosis, that w

180 measures were tested by using multivariable logistic regression models to determine which combinatio

181 We used logistic regression models to estimate associations of P

182 METHODS AND We fit mixed-effects logistic regression models to routine surveillance data

183 We used logistic regression models under a generalized estimatin

184 Multiple-variable logistic regression models were built to compare the dia

185 Pooled multivariate logistic regression models were constructed for each inf

186 Univariate then multivariable logistic regression models were constructed to assess th

187 Logistic regression models were established for both the

188 Multilevel multivariable logistic regression models were fitted, adjusting for pa

189 Multivariate linear and logistic regression models were performed to assess fact

190 Conditional logistic regression models were used to assess associati

191 Multivariable logistic regression models were used to assess the exten

192 ts were pooled in case-control analyses, and logistic regression models were used to compute risks.

193 w-up for >/=3 months, and population average logistic regression models were used to determine risk f

194 Unadjusted and adjusted logistic regression models were used to determine the pr

195 Separate logistic regression models were used to determine the re

196 Conditional logistic regression models were used to estimate odds ra

197 Multivariable unconditional logistic regression models were used to estimate odds ra

198 Multivariable conditional logistic regression models were used to estimate odds ra

199 tify potentially confounding covariates, and logistic regression models were used to estimate the ris

200 Multiple linear and logistic regression models were used to examine relation

201 s (with 95% confidence interval) and ordinal logistic regression models were used.

202 rvals (CI) were calculated using conditional logistic regression models with adjustment for important

203 ces in percent effect changes in conditional logistic regression models with and without additional a

204 Data were analyzed using multiple logistic regression models with backward stepwise elimin

205 Logistic regression models with empirical Bayes factors

206 were tested using multivariable hierarchical logistic regression models, adjusting for important prog

207 We used Cox proportional hazard models, logistic regression models, and Fine-Gray competing risk

208 In logistic regression models, cannabis use at wave 1 was a

209 Based on hierarchical logistic regression models, the likelihood of receiving

210 In the multiple logistic regression models, the median glycemic level wa

211 Using generalised linear and logistic regression models, we examined the effect of 12

212 outcome measures at 35 years (wave 10) using logistic regression models, with progressive adjustment:

213 tment delay on treatment effectiveness using logistic regression models.

214 rts via univariate analysis and multivariate logistic regression models.

215 es and ADHD in offspring were analyzed using logistic regression models.

216 statin and nonstatin LLT use in hierarchical logistic regression models.

217 hters at midlife using quantile, linear, and logistic regression models.

218 to automatically construct knowledge graphs: logistic regression, naive Bayes classifier and a Bayesi

219 se rates by education and income level using logistic regression (odds ratios).

220 Using logistic regression, odds ratios with 95% confidence int

221 On multivariate logistic regression, only age younger than 50 years, bas

222 Logistic regression or Cox proportional hazard models we

223 After logistic regression, prior intravitreal injection was as

224 Firth's logistic regression provides a concise statistical infer

225 ) at transfer were assessed using linear and logistic regression, respectively.

226 Spearman's correlations and logistic regression revealed a general pattern of beech

227 g mixed effects repeated measures models and logistic regression, revealed two independent histologic

228 Furthermore, logistic regression showed that this combination of thes

229 Multivariate logistic regressions showed that participants undertakin

230 ls (CIs) were estimated by using conditional logistic regression stratified according to age and race

231 For both analyses, we used logistic regression, stratified by sex and adjusted for

232 Logistic regression techniques were used to calculate ri

233 ORs were calculated with the use of Cox and logistic regressions.The mean +/- SD plasma 25(OH)D conc

234 Odds ratios were calculated using logistic regression to account for potential confounders

235 We used mixed effects logistic regression to analyze the association of diabet

236 We performed logistic regression to assess correlations between expos

237 We used logistic regression to assess the strength of associatio

238 adjusted odds ratios with exact conditional logistic regression to determine the association between

239 We performed a stepwise logistic regression to develop a multivariate risk predi

240 We used multivariable logistic regression to estimate a difference-in-differen

241 We used polytomous logistic regression to estimate odds ratios (ORs) for as

242 We used conditional logistic regression to estimate odds ratios for incident

243 We used multilevel multivariable ordinal logistic regression to estimate odds ratios.

244 We used logistic regression to estimate the association between

245 from 3 prospective cohorts using conditional logistic regression to evaluate pre-diagnosis levels of

246 We used multivariate logistic regression to evaluate the association between

247 We used logistic regression to examine factors associated with t

248 We used logistic regression to investigate factors associated wi

249 c information and infection status, and used logistic regression to relate those covariates to lamb s

250 We used logistic regression to show the association between visi

251 stages under a fixed-effects model and used logistic regression to test for association in each stag

252 We used case-only multivariable logistic regression to test for heterogeneity in associa

253 We used logistic regression to test the relationship between cha

254 tcome typically entails fitting a polytomous logistic regression via maximum likelihood estimation.

255 Multivariate logistic regression was performed and detailed periabsce

256 Multivariable logistic regression was performed to identify factors as

257 Multivariable logistic regression was performed to identify factors as

258 In-hospital outcomes were recorded, and logistic regression was performed to identify independen

259 Mixed-effects logistic regression was performed to model independent d

260 s (low, moderate, and high) were created and logistic regression was undertaken to evaluate the optim

261 Multivariable logistic regression was used to assess associations betw

262 Logistic regression was used to assess risk factors for

263 Logistic regression was used to calculate crude and adju

264 Multivariable logistic regression was used to calculate odds ratios (O

265 ime trends were identified and multivariable logistic regression was used to determine sociodemograph

266 Logistic regression was used to determine the independen

267 Binary logistic regression was used to develop a multivariable

268 Binary multivariable logistic regression was used to estimate the odds of HPV

269 Logistic regression was used to evaluate the association

270 Logistic regression was used to examine the association

271 Multivariable logistic regression was used to explore the association

272 n a derivation cohort, and backward stepwise logistic regression was used to identify factors indepen

273 Logistic regression was used to identify genetic variant

274 Multinomial logistic regression was used to identify potential facto

275 Logistic regression was used to identify risk factors fo

276 Logistic regression was used to investigate if patient f

277 Logistic regression was used to obtain adjusted odds rat

278 Multivariate logistic regression was used to predict the outcome.

279 Logistic regression was used to test for an interaction

280 Using multivariable logistic regression, we assessed correlates of significa

281 With the use of logistic regression, we characterized the associations o

282 Using multiple logistic regression, we identified significant associati

283 We used logistic regression, weighted for sampling and response

284 Cox proportional hazards model and logistic regression were used to correlate early changes

285 Descriptive statistics and multivariate logistic regressions were conducted to evaluate end-of-l

286 Logistic regressions were performed to assess if their i

287 Multivariable logistic regressions were used to analyze the associatio

288 Logistic regressions were used to determine the associat

289 Conditional fixed-effects logistic regressions were used to examine predictive rel

290 d Rankin Scale score was analyzed by ordinal logistic regression, which yields a common odds ratio (O

291 (ORs) and 95% confidence intervals (CIs) by logistic regression with adjustment for age, gender, and

292 venlafaxine), using multivariable linear and logistic regression with Bonferroni correction.

293 Multivariable logistic regression with generalized estimating equation

294 Logistic regression with generalized estimating equation

295 We used multivariate logistic regression with PCR-confirmed influenza infecti

296 Multivariate logistic regression with restricted cubic splines was ut

297 e patients versus controls using conditional logistic regression with results from the 2 settings poo

298 Logistic regression with stepwise variable selection was

299 Generalized estimating equations for logistic regressions with covariate adjustment were appl

300 retrospective cohort study using multilevel logistic regression, with MAC use modeled as a function