ライフサイエンスコーパス: Cox regression

1 tic, and 53% in the rescue TDM arm, p = 0.6, Cox regression).

2 yptophan and time to ESKD was assessed using Cox regression.

3 atment outcomes were analyzed by logistic or Cox regression.

4 is) among HCV+ recipients using logistic and Cox regression.

5 CSF or serum measures were determined using Cox regression.

6 nt success were assessed using multivariable Cox regression.

7 all patients were derived from multivariable Cox regression.

8 azard ratios and 95% CI were estimated using Cox regression.

9 Risks of death were analyzed using Cox regression.

10 pared with cumulative incidence analysis and Cox regression.

11 eath) by HIV and weight change status, using Cox regression.

12 xamined via log-rank tests and multivariable Cox regression.

13 associated with survival were analyzed using Cox regression.

14 to Cox proportional hazards model and kernel Cox regression.

15 ldhood was then assessed using multivariable Cox regression.

16 R 0.67, 95%CI: 0.45-0.98) only by univariate Cox regression.

17 as time-varying covariates in time-dependent Cox regression.

18 after dropout were assessed by multivariable Cox regression.

19 mortality were analysed using multivariable Cox regression.

20 is included Kaplan-Meier survival curves and Cox regression.

21 with the matched cohort were estimated using Cox regression.

22 cardiovascular event (MACE) were analyzed by Cox regression.

23 lyzed using Kaplan-Meier curves and multiple Cox regression.

24 Meier survival curves with log-rank test and Cox regression.

25 th acute kidney injury or hyperkalemia using Cox regression.

26 rvival (DFS) were calculated by log-rank and Cox regression.

27 Models were tested with use of Cox regression.

28 d psychiatric diagnoses) were analyzed using Cox regression.

29 rs significantly associated with outcomes by Cox regression.

30 for prognostic variables and analyzed using Cox regression.

31 preferred and nonpreferred recipients using Cox regression.

32 on between PMI and mortality was analyzed by Cox regression.

33 CC) using Kaplan-Meier curves and stratified Cox regression.

34 f follow-up were investigated using adjusted Cox regressions.

35 6 months were compared between groups using Cox regressions.

36 By penalized Cox regression, a prognostic GE signature could not be i

37 lity and death-censored graft survival using Cox regression, acute rejection, and delayed graft funct

38 of covariance (ANCOVA) adjusted for age and Cox regression adjusted for age and sex were used to com

39 ween genotype and outcomes were tested using Cox regression adjusted for age, assessment center, geno

40 In multiple Cox regression adjusted for age, sex, primary tumor site

41 We used Cox regressions adjusted for age, sex, 3-month disabilit

42 ere compared with the no-adenoma group using Cox regression adjusting for confounders.

43 Multivariate Cox regression adjusting for demographics and clinical m

44 ll as with graft failure and mortality using Cox regression, adjusting for donor, recipient, and immu

45 fter starting treatment were investigated by Cox regression analyses according to an a priori analysi

46 Cox regression analyses adjusted for Charlson comorbidit

47 Cox regression analyses adjusted for Charlson comorbidit

48 In Cox regression analyses adjusted for risk factors, highe

49 ET radiomics features were selected by Lasso-Cox regression analyses and a separate radiomics signatu

50 LnCeVar-Survival performs COX regression analyses and produces survival curves for

51 infertility and overall infertility through Cox regression analyses comparing the firefighters with

52 Kaplan-Meier and Cox regression analyses for the overall risk of nAMD dev

53 d univariable Kaplan-Meier and multivariable Cox regression analyses in the unmatched consecutive coh

54 We performed univariate analyses and Cox regression analyses including important predictors o

55 Post hoc Cox regression analyses of outcomes by baseline HF histo

56 Results of multilevel Cox regression analyses revealed a statistically signifi

57 3 months after ICU admission, multivariable Cox regression analyses showed that case-mix adjusted ha

58 Cox regression analyses were employed to evaluate associ

59 Cox regression analyses were performed to examine the as

60 Cox regression analyses were performed to generate a wei

61 Cox regression analyses were used to estimate crude and

62 Competing risk and Cox regression analyses were used to investigate the ass

63 Kaplan-Meier and Cox regression analyses were used.

64 Multivariable Cox regression analyses, adjusted for potential confound

65 In Cox regression analyses, CEC was significantly associate

66 In univariate Cox regression analyses, estimated glomerular filtration

67 by the HR, were assessed using Logistic and Cox regression analyses, respectively.

68 s were investigated through Kaplan-Meier and Cox regression analyses, respectively.

69 k factors were investigated by multivariable Cox regression analyses.

70 istribution of PRS(313) was quantified using Cox regression analyses.

71 In unadjusted Cox regressions analyses, very low BMD was association w

72 ociation with overall survival (OS) based on Cox-regression analyses.

73 with better graft outcome in a multivariate Cox regression analysis (hazard ratio, 0.260; 95% CI, 0.

74 d with all-cause mortality in the univariate Cox regression analysis (hazard ratio, 1.09 [95% CI, 1.0

75 Cox regression analysis (time-dependent) was used to eva

76 In multivariable Cox regression analysis accounting for established progn

77 Cox regression analysis adjusted for patients' age, sex,

78 Cause-specific Cox regression analysis adjusted for stroke, head trauma

79 e assessed using Kaplan-Meir methodology and Cox regression analysis adjusting for demographics, como

80 Cox regression analysis and Kaplan-Meier curves were use

81 Multivariate Cox regression analysis and propensity score matching we

82 In a Cox regression analysis controlling for age, gender, and

83 Cox regression analysis controlling for potential confou

84 The time-dependent Cox regression analysis demonstrated a higher mortality

85 etween CT and MRI guidance, with univariable Cox regression analysis hazard ratios of 0.97 (95% CI: 0

86 A lasso-based model and multivariate cox regression analysis identified a chromosome 17p loss

87 A Cox regression analysis identified age and diagnoses oth

88 Multivariate Cox regression analysis of a propensity score-matched sa

89 A multivariate Cox regression analysis of the miR-21 expression in the

90 Cox regression analysis revealed a hazard ratio incomple

91 Multivariable Cox regression analysis revealed plasma cell dyscrasia (

92 Multivariate Cox regression analysis showed higher all-cause mortalit

93 Multivariate Cox regression analysis showed that age and pneumonia we

94 as confirmed in sex- and risk-group-adjusted Cox regression analysis stratified by age (>= 10 and < 1

95 We further used multivariate Cox regression analysis to assess miR-21 expression in t

96 We used Cox regression analysis to determine clinically signific

97 hierarchical cluster analysis, and also used Cox regression analysis to identify associations with ea

98 Multivariable Cox regression analysis was performed to determine predi

99 A Cox regression analysis was performed to evaluate the ri

100 Cox regression analysis was performed to investigate the

101 Multivariate Cox regression analysis was used to determine the predic

102 Multivariate Cox regression analysis was used to estimate risk-adjust

103 Univariate and multivariate Cox regression analysis were performed, and predictive m

104 The Kaplan-Meier estimator, U test, and Cox regression analysis were used for statistics.

105 he six clinical outcomes were analyzed using Cox regression analysis with rivaroxaban as the referenc

106 In multivariate Cox regression analysis, absence of postoperative smokin

107 After multivariable Cox regression analysis, age >70 (HR, 4.16; 95% CI, 1.78

108 vival (OS) was evaluated using multivariable Cox regression analysis, before and after propensity-sco

109 In the Cox regression analysis, bladder drained pancreas was as

110 By multivariable Cox regression analysis, GLS remained independently asso

111 Cox regression analysis, Kaplan-Meier curves, and cross-

112 In Cox regression analysis, r (voxelwise) between nSUV and

113 On Cox regression analysis, receiving G-CSF (hazard ratio,

114 In multivariable Cox regression analysis, SVR was associated with a reduc

115 were used as covariates in the multivariate Cox regression analysis.

116 uation and prognosis for breast cancer using Cox regression analysis.

117 g Kaplan-Meier analysis, log-rank tests, and Cox regression analysis.

118 erall survival (OS) evaluated using adjusted Cox regression analysis.

119 Hazard ratios (HRs) were calculated using Cox regression analysis.

120 death/transplantation) were assessed, using Cox regression analysis.

121 and MACE was assessed by using multivariable Cox regression analysis.

122 ls/muL outcome for deletion allele carriers (Cox regression analysis: hazard ratio, 2.4 [95% confiden

123 A cox-regression analysis for post-liver transplant HCC re

124 Cox-regression analysis of rejection-free survival revea

125 We used multivariable Cox-regression analysis to determine whether surgical ap

126 eralized-linear models (GLM) and multi-level Cox-regression analysis were applied.

127 ed along with independent risk factors using Cox-regression analysis.

128 Cox regression and ANCOVA were used for the analyses.

129 Prognostic markers were evaluated using Cox regression and competing risks analysis.

130 M extensions found in the literature, kernel Cox regression and Cox model.

131 is, advanced statistics such as the extended Cox regression and dimensional analyses including partia

132 g parameters was assessed with multivariable Cox regression and formal interaction testing.

133 survival (PFS; by RECIST) were evaluated by Cox regression and Kaplan-Meier statistics.

134 terative propensity score-matched, survival (Cox regression and Kaplan-Meier), and center effects ana

135 on diagnostic and prognostic models built on Cox regression and machine learning.

136 Cox regression and mediation analyses were conducted to

137 Risk prediction models were developed using Cox regression and multivariable fractional polynomials

138 Results were analyzed by multivariate Cox regression and network analysis.Measurements and Mai

139 We compared outcomes using Cox regression and non-inferiority analyses (25% margin,

140 ween outcomes and concentration of TSH using Cox regression and outcomes and free thyroxine (FT4) con

141 We estimated risk of each definition using Cox regression and overall predictability (area under th

142 Extended Cox regression and Poisson regression were used for stat

143 cer mortality for cancer were estimated with Cox regression and standardised incidence rates.

144 Cox regression and the Bliss independence model were use

145 on each end point, followed by multivariate Cox regressions and logistic regressions to analyze the

146 sed weighted traditional (i.e., multivariate Cox regressions) and machine-learning (i.e., lasso, rand

147 n NAS and all-cause maternal mortality using Cox regression, and the cumulative incidence of cause-sp

148 Methods based on univariate Cox regression are often used to select genomic features

149 Using Cox regression at an ELF threshold of 10.51 hazard ratio

150 learning models outperformed those applying Cox regression by 10% sensitivity.

151 ding disease conversion was determined using Cox regression (cloglog link function).

152 stimated hazard ratios (HRs) from stratified Cox regression comparing risk of major osteoporotic (hip

153 d outperformed a conventional method such as Cox regression (concordance index 0.769 vs 0.745).

154 Cox regression determined the association between resusc

155 In multivariate Cox-regression, duration of shock-to-first device (hours

156 We used Cox regression for patient/graft survival and sequential

157 Multivariable Cox regression found no association with ICH location (H

158 Multivariable Cox regression found that lobar ICH was associated with

159 stic nets/principal components analysis) and Cox regression generated parsimonious, metabolite-based

160 istical methods such as machine learning and Cox regression have provided the methodological basis fo

161 2.60; 95% CI, 2.02 to 3.35) and time-varying Cox regression (HR, 1.84; 95% CI, 1.33 to 2.55) demonstr

162 In multivariable Cox regression, inducible ischemia was an independent pr

163 elationships were evaluated using linear and Cox regression, Kaplan-Meier survival, and mediation ana

164 rn Cooperative Oncology Group scale and used Cox regression methods to estimate hazard ratios (HRs) t

165 l (OS) and relapse incidence was tested in a Cox regression model adjusted for patient age, a modifie

166 was retransplant-free survival, analyzed via Cox regression model adjusted for recipient age, gender,

167 was retransplant-free survival, analyzed via Cox regression model adjusted for recipient age, gender,

168 A Cox regression model adjusting for patient demographics,

169 First, we fitted a Cox regression model and estimated the 10-year predicted

170 compared risks of SPM using a cause-specific Cox regression model considering death as a competing ri

171 Cox regression model for recurrent time-to-event data an

172 In a multivariate Cox regression model including each of the clinical and

173 We used a multivariate Cox regression model to estimate the effect of risk fact

174 adjusted hazard ratio from the multivariable Cox regression model was 0.99 (Wald test, P = .93) for O

175 A Cox regression model was fitted to ascertain the all-cau

176 A Cox regression model was used for statistical analyses.

177 To examine susceptibility to COVID-19, a Cox regression model with a nested case-control framewor

178 In a multivariable Cox regression model, postthrombotic femoral veins at ba

179 survival (PFS) by fitting an L1-regularized Cox regression model.

180 A multivariate cox-regression model was performed which showed tumor st

181 Using the discovery cohort, multivariate Cox regression modeling defined a minimal model includin

182 The hazard ratio for RNFLT slope in Cox regression modeling for time to incident VF progress

183 We fit mixed-effects Cox regression models (center as random effect) to evalu

184 y was significant in unadjusted and adjusted Cox regression models (P <= 0.001 for all models), and i

185 GINA steps was analyzed using multivariable Cox regression models adjusted for age and sex.

186 5% CIs for incident AF were calculated using Cox regression models adjusted for age, sex, height, wei

187 th cardiovascular risk were determined using Cox regression models adjusted for cardiovascular risk f

188 s in the Swiss HIV Cohort Study.We performed Cox regression models adjusted for demographic factors,

189 d cystatin C) and ACR with cancer risk using Cox regression models adjusted for potential confounders

190 rvival (DFS) and overall survival (OS) using Cox regression models adjusted for treatment assignment,

191 We fitted mixed-effects Cox regression models adjusting for multiple pregnancies

192 ites with subsequent T2D risk using weighted Cox regression models and adjusting for potential confou

193 d using the concordance index for univariate Cox regression models determined from the training cohor

194 Cox regression models estimated hazard ratios (HR) and 9

195 Multivariate Cox regression models identified other predictors of dis

196 loading and 30-day mortality was assessed by Cox regression models in a 1:1 propensity score-matched

197 Multivariate Cox regression models incorporating conventional echocar

198 Weighted Cox regression models related biomarkers to progression

199 corresponding 95% CIs, were estimated using Cox regression models stratified on cohort.

200 probability treatment weighting was used in Cox regression models to adjust for differences in demog

201 We used landmark Kaplan-Meier and Cox regression models to analyse the association of trea

202 We fitted multilevel Cox regression models to analyze the data.

203 individual descriptors and clusters, we used Cox regression models to assess associations with time f

204 We used Cox regression models to calculate overall hazard ratios

205 We fitted multilevel Cox regression models to estimate hazard ratios (HRs) wi

206 We computed the mortality hazard using Cox regression models to estimate survival in multimorbi

207 We used time-varying Cox regression models to examine the association between

208 Cox regression models were applied to analyze the associ

209 Cox regression models were fitted to evaluate the progno

210 Multivariate marginal Cox regression models were generated to identify those f

211 Time-dependent Cox regression models were performed to identify the ind

212 Kaplan-Meier and landmark Cox Regression models were used for survival estimates.

213 Multivariable Cox regression models were used to analyze the relations

214 Cox regression models were used to assess associations b

215 Multivariable Cox regression models were used to assess associations o

216 Stratified Cox regression models were used to estimate associations

217 Cox regression models were used to estimate the associat

218 Multivariable linear and Cox regression models were used to explore the associati

219 nce intervals (CI) estimated from stratified Cox regression models were used to quantify the associat

220 Cox regression models were used.

221 atios for mortality were calculated by using Cox regression models with emphysema as the main predict

222 Cox regression models with inverse probability weighting

223 ctors of HCV infection were identified using Cox regression models with random effects after accounti

224 associated with hospital-onset sepsis using Cox regression models with sepsis as a time-varying cova

225 Analysis used Cox regression models, adjusting for maternal factors, b

226 In multivariate Cox regression models, as compared with never smokers of

227 associations were evaluated with linear and Cox regression models, comparing fit of models with and

228 In Cox regression models, factors associated with a trend f

229 ed using multivariable-adjusted logistic and Cox regression models, respectively.

230 isk factors in the univariate analysis using Cox regression models, whereas only weight <15 kg (P = 0

231 mined risk factors for DSA development using Cox regression models.

232 each patient and tested using multivariable Cox regression models.

233 cardiac event (MACE) was investigated using Cox regression models.

234 ion-free survival were estimated by means of Cox regression models.

235 otype on arrhythmic risk was evaluated using Cox regression models.

236 hazard rate ratios (HRs) were computed using Cox regression models.

237 ted using time-dependent and time-sequential Cox regression models.

238 cal event were determined using multivariate Cox regression models.

239 en the two study periods were assessed using Cox regression models.

240 nd SSI risk by use of uni- and multivariable Cox regression models.

241 and recurrent infections were identified by Cox regression models.

242 sk factors were identified and validated via Cox regression models.

243 ncidence rates, hazard ratios using adjusted cox-regression models, and standardized mortality/morbid

244 ndardized mortality/morbidity ratio weighted cox-regression models.

245 Univariate (UVA) and multivariable Cox regression (MVA) modeling was used to determine pred

246 f recurrence of 4.5 (HR 95% CI = 1.11-14.57; Cox Regression p = 0.034).

247 Multivariable Cox regression provided hazard ratios (HRs) for mortalit

248 Multivariable Cox regression provided hazard ratios (HRs) with 95% con

249 sk factors were assessed using multivariable Cox regression providing adjusted hazard ratios (HRs) wi

250 onoperated GERD patients using multivariable Cox regression, providing hazard ratios (HR) with 95% CI

251 The Cox regression reflected what was seen in the survival c

252 In a step-up Cox regression, risk factors for myocardial infarction e

253 Statistical methods used were Cox regression, Student t test, and Mann-Whitney U test.

254 We used mixed-effects Cox regression survival analysis to estimate the effects

255 s to analyse clinical features, and survival Cox regression to analyse time to antibody negativity.

256 We further used Kaplan-Meier curves and Cox regression to assess differences in survival between

257 We used multivariable Cox regression to assess whether this "Hispanic paradox"

258 We used Cox regression to calculate adjusted rate ratios (RRs) f

259 We used stratified Cox regression to calculate hazard ratios (HRs) for smal

260 ts and US Census, we performed multivariable Cox regression to compare outcomes among 18 955 women an

261 tural history of candidates who declined and Cox regression to compare postdecision survival after de

262 We used Cox regression to compare the risk of CMV infection and

263 We used Cox regression to compute hazard ratios (HRs) for incide

264 We used Kaplan-Meier methods and Cox regression to describe the association between HIV s

265 We used Cox regression to develop a clinical prognostic index fo

266 We used Cox regression to estimate hazard ratios (HR) and 95% co

267 We applied Cox regression to estimate hazard ratios (HRs) for futur

268 , propensity score-matched cohort study used Cox regression to estimate hazard ratios (HRs) of IS for

269 Cox regression to evaluate the associations of PN with a

270 ping AD/PD followingly was determined by the Cox regression to identify potential confounding factors

271 igration country smoking prevalence, we used Cox regressions to contrast risks of health outcomes for

272 ults in Hong Kong with hypertension, we used Cox regressions to examine associations between all-caus

273 Cox regression was performed to determine the prognostic

274 Multivariable competing-risks Cox regression was performed.

275 Cox regression was used to assess the association of the

276 Cox regression was used to compute adjusted hazard ratio

277 Cox regression was used to compute the hazard ratios (HR

278 Multivariable Cox regression was used to estimate adjusted hazard rati

279 Cox regression was used to estimate adjusted hazard rati

280 Stratified Cox regression was used to estimate hazard ratios (HRs)

281 Cox regression was used to estimate hazard ratios (HRs)

282 Cox regression was used to estimate hazard ratios (HRs)

283 Cox regression was used to estimate hazard ratios and 95

284 Cox regression was used to estimate hazard ratios of dem

285 In a subset of subjects, Cox regression was used to examine the association betwe

286 matching for grade 1 (G1) ACLF, followed by Cox regression, was used to model risk of subsequent gra

287 Multivariable logistic and Cox regression were used to: (1) identify independent pr

288 In a second step, the results of Cox regressions were applied to a validation dataset (n

289 Univariable and multivariable Cox regressions were performed to determine the prognost

290 Competing-risk and Cox regressions were performed to identify predictors of

291 Multivariate Cox regressions were used to predict dementia using base

292 ats per minute) in follow-up was assessed by Cox regression with backward selection.

293 d mortality using multivariable logistic and Cox regression with DGF-ESW interaction terms.

294 eated with other antidiabetic agents using a Cox regression with inverse probability of treatment wei

295 me from discharge to death was modeled using Cox regression with time-varying exposure to pulmonary r

296 h disorder-specific PRS were estimated using Cox regressions with adjustment for the other two PRSs.

297 Cox regression, with adjustment for age (as the underlyi

298 ,085 individuals) as well as a mixed effects Cox regression, with age at last visit to the clinic or

299 Multivariable Cox regression yielded adjusted hazard ratios (HR) and 9

300 Cox regression yielded hazard ratios (HRs) and 95% confi