ライフサイエンスコーパス: Cox model

1 independent predictors of improved survival (Cox model).

2 t analyses, such as fitting a time-dependent Cox model.

3 ing an inverse probability weighted marginal Cox model.

4 CI, 0.61 to 1.12; P = .214) from an adjusted Cox model.

5 s and 3-year survival was determined using a Cox model.

6 rrhage were identified using a multivariable Cox model.

7 e interval, 1.02-1.55) in the cause-specific Cox model.

8 lar results were found using the traditional Cox model.

9 us CRC were evaluated using a time-dependent Cox model.

10 hazard model and a flexible extension of the Cox model.

11 arametric models were better fitted than the Cox model.

12 and survival outcomes using a multivariable Cox model.

13 lity were examined in a comorbidity-adjusted Cox model.

14 tation-related covariates using a stratified Cox model.

15 d with the Log-rank test and a multivariable Cox model.

16 ulated by the Andersen-Gill extension of the Cox model.

17 he predicted survival curve estimated in the Cox model.

18 to relapse were evaluated by a mixed effects Cox model.

19 s and stayers were compared using a marginal Cox model.

20 and conducted multivariate analysis using a Cox model.

21 ause mortality with the use of multivariable Cox models.

22 bserved in time-dependent or fixed-covariate Cox models.

23 ent AF by trajectory group was examined with Cox models.

24 ectable viral load (VL, >/=400 cps/mL) using Cox models.

25 clinical events with logistic regression and Cox models.

26 al failure were assessed using multivariable Cox models.

27 ART due to presumed treatment failure, using Cox models.

28 and 95% CIs were computed by using adjusted Cox models.

29 nalyzed using Kaplan-Meier and multivariable Cox models.

30 n-recipients (n=131,358) using multivariable Cox models.

31 sted for several potential confounders using Cox models.

32 al red meat consumption and diabetes risk in Cox models.

33 and stratified univariable and multivariable Cox models.

34 t cancer and 95% CIs were estimated by using Cox models.

35 phthalmitis were examined using multivariate Cox models.

36 -rank; hazard ratios (HRs) were estimated by Cox models.

37 their confidence intervals were derived from Cox models.

38 or (PR) status were calculated with standard Cox models.

39 examined all-cause mortality using adjusted Cox models.

40 with mortality through day 365 post-HCT with Cox models.

41 d inappropriate ICD therapy by multivariable Cox models.

42 nd antithrombotic therapy, assessed by using Cox models.

43 ase-free survival (DFS) was determined using Cox models.

44 djusted hazard ratios (HR) were derived from Cox models.

45 OS determined by bivariate and multivariable Cox models.

46 assessed using time-dependent multivariable Cox models.

47 malaria infection obtained through different Cox models.

48 n outcomes were analyzed using multivariable Cox models.

49 ted adjusted hazard ratios with time-varying Cox models.

50 Survival predictors were tested in Cox models.

51 were estimated using adjusted discrete-time Cox models.

52 r periods were estimated from time-dependent Cox models.

53 re assessed using Kaplan-Meier estimates and Cox models.

54 and required the use of marginal structural Cox models.

55 RP, and D-dimer levels were calculated using Cox models.

56 tios (HRs) and 95% CIs derived from adjusted Cox models.

57 ion between STILs and RFS was evaluated with Cox models.

58 1.50; 95% CI, 1.07-2.12) in fixed-covariate Cox models.

59 dictors of ASCVD events in the multivariable Cox models.

60 ized estimating equations (GEE) and extended Cox models.

61 ney transplant on the basis of multivariable Cox modeling.

62 In sequential Cox models, a model based on clinical data (chi(2), 20.4

63 In sequential Cox models, a model based on clinical data and left vent

64 In a multivariable Cox model adjusted for age, coronary artery disease, pro

65 cant and of similar magnitude (HR, 2.0) in a Cox model adjusted for clinical and biologic prognostica

66 In Cox models adjusted for age and sex, factors significant

67 erminal peptide (PINP)) were estimated using Cox models adjusted for age at diagnosis, diagnostic cer

68 rd ratios and 95% confidence intervals using Cox models adjusted for confounders.

69 n of TMAO with cardiovascular outcomes using Cox models adjusted for potential confounders (demograph

70 In Cox models adjusted for risk factors for coronary artery

71 e (CHD), stroke, and ESRD was examined using Cox models adjusted for sociodemographic characteristics

72 rm (hazard ratio, 0.6; 95% CI, 0.43 to 0.75; Cox model-adjusted P < .001).

73 ted mortality/graft loss was analyzed by the Cox model adjusting for HCV-Donor Risk Index, warm ische

74 ted for interquartile pollutant changes from Cox models adjusting for age, sex, smoking, body mass in

75 In multivariable Cox models adjusting for patient demographics and establ

76 ) with risk of dementia (until 2015) using a Cox model, adjusting for age, sex, demographics, cardiov

77 A Cox model, adjusting for pack-years of cigarette smoking

78 We used extended Cox models allowing for time-dependent variation of rest

79 inear model or the interval mapping based on Cox model, although it somewhat underestimates QTL effec

80 ysis was performed by using the conventional Cox model, an artificial survival benefit of metformin w

81 e (Kaplan and Meier plots) and multivariate (Cox model) analyses were carried out.

82 In the multivariate Cox model analysis adjusted for age and sex, PASP increa

83 Forest plots were created based on Cox model analysis.

84 d survival were analyzed using multivariable Cox modeling and restricted cubic spline function.

85 electrocardiogram vs. no AF) using adjusted Cox models and explored an interaction with exercise tra

86 Nomograms were created from Cox models and internally validated by use of bootstrap

87 Using unadjusted and adjusted Cox models and Kaplan-Meier analysis, there was no signi

88 We used Cox models and mixed-effects models to, first, estimate

89 ere assessed by using multivariable adjusted Cox models and restricted cubic splines.

90 d graft survival was analyzed using extended Cox models and retransplantation using competing risks r

91 Based on multivariable Cox models and stepwise selection, the 3 most discrimina

92 Time to HF (Cox model) and Deltaepsiloncc and DeltaEF (multiple line

93 ias: conditional landmark analysis, extended Cox model, and inverse probability weighting.

94 eier survival function, 687 for multivariate Cox models, and 576 and 132 for matching on the propensi

95 e log rank test, univariate and multivariate Cox models, and propensity score-matched analyses.

96 Hazard ratios (HRs) were estimated from Cox models, and survival curves were estimated by the Ka

97 hronic kidney disease (CKD) were examined in Cox models, and with the slopes of eGFR in linear and lo

98 000 to 2009 were analyzed for ITT-OS using a Cox model; and tumor recurrence using 2 competitive risk

99 BP was analyzed in Cox models as the cumulative average of serially measure

100 A novel 2-stage Cox model assessed heterogeneity in risk for individual

101 Cox models assessed the association between smoking hist

102 djustment for transplant in a time-dependent Cox model attenuated the higher risk of death in obese b

103 In multivariate Cox models, average and superior RNFL losses were associ

104 ndicating higher accuracy) and compared with Cox models based on clinical (age and Karnofsky performa

105 Cox model-based SMRs were computed with and without adju

106 In a multivariable Cox model, compared with unexposed patients, the risk of

107 end points were evaluated using conditional Cox models comparing new SGLT2i users with other antihyp

108 In the multivariate Cox models comparing patients with an improved LVEF with

109 In multivariable Cox models comparing risk in the first quartile with tha

110 In separate Cox models, controlling for relevant prognostic and pred

111 ip with 1-year mortality was evaluated using Cox modeling, correcting for 15 clinical variables from

112 With the use of the Cox model, DCD was a significant risk factor for kidney

113 Only the 1-year deceased donor recipient Cox model demonstrated significantly improved calibratio

114 Only the 1-year deceased donor recipient Cox model demonstrated significantly improved calibratio

115 Cox modeling demonstrated that PSC patients have a posit

116 In addition, multivariable Cox modeling demonstrated that treatment with adjuvant c

117 Additionally, adjusted Cox models determined that every additional carbapenem d

118 tality (primary end point) in a multivariate Cox model (ejection fraction hazard ratio [HR], 0.97 [95

119 Adjusted Cox models evaluated the independent and combined effect

120 vent by using marginal structural models and Cox models extended to accommodate time-dependent variab

121 Using multivariable Cox model for both PFS and OS, the patient age was not s

122 ear regression for length of gestation and a Cox model for preterm birth.

123 Prediction models were developed using Cox models for (a) mortality, (b) allograft loss (death

124 ained risk scores on the basis of GCE versus Cox models for cancer-specific mortality and all-cause m

125 Using Cox models for discrete time, we estimated fecundability

126 formed time-to-event analysis using separate Cox models for risk to develop delayed and recurrent sei

127 Cox models for the primary composite cardiovascular outc

128 Cox models for three metrics landmarked at 12 weeks and

129 ciations were examined in naive and adjusted Cox models (for time-to-event analyses) and logistic reg

130 ackward selection to derive the best-fitting Cox model, from which we derived a multivariable fractio

131 of hip fracture in a multivariable-adjusted Cox model (hazard ratio, 0.35; 95% CI, 0.22-0.54).

132 ed with the primary end point in univariable Cox model (hazard ratio, 1.84; 95% confidence interval,

133 interval, 0.997-1.002; P=0.856) and adjusted Cox models (hazards ratio, 1.000; 95% confidence interva

134 en treatment and ejection fraction (p = 0.10 Cox model); however, pre-specified subgroup analysis sug

135 rom approximation approaches to the weighted Cox model (i.e., MSCM) extend confidence in the findings

136 The Cox model identified 12 separate risk factors for mortal

137 A Cox model identified receipt of radiation therapy plus c

138 Further assessment by multivariate Cox models identified significant risk for death associa

139 h TLI were estimated through a multivariable Cox model in both sets.

140 TDM approach with that of the time-dependent Cox model in the presence of immortal time.

141 ity scores using the Kaplan-Meier method and Cox models in "intention-to-treat" analyses and in gener

142 ischemic stroke using multivariable-adjusted Cox models in a nationwide cohort of 547 441 black and 2

143 9, and 0.007, respectively) in multivariable Cox models in AA TNBCs but not White TNBCs.

144 The multivariate Cox model included clinical covariates.

145 computing Harrell's C statistics, we used a Cox model including the prognostics factors gender, age

146 risk for each patient was determined from a Cox model incorporating age, nodal status, tumor size an

147 of HF/death was evaluated using multivariate Cox models incorporating the presence of, respectively,

148 Results from the Cox model indicated that the hazard of graft failure dur

149 In multivariable Cox models, individuals with increased risk for suicide

150 iovascular disease events were assessed with Cox models, log-rank tests, and mediation (path) analyse

151 sion (in or out of hospital), analysed using Cox models measuring time from admission.

152 In multivariate Cox models, metabolically healthy overweight women, defi

153 Marginal structural Cox models (MSCMs) can provide distinct advantages over

154 Compared with separate Cox models, multistate models allow for dissection of pr

155 In multivariable Cox models, neither injectable (adjusted hazard ratio [a

156 Furthermore, in a Cox model of 3976 propensity-matched pairs, patients who

157 l variables were included in a multivariable Cox model of OS validated by bootstrapping.

158 Multivariate Cox models of 10-year survival and overall indicated tha

159 Comparing the GCE model to Cox models of cause-specific mortality or all-cause mort

160 6 with group 4 tumours) in our multivariable Cox models of progression-free and overall survival.

161 HF events were estimated from competing risk Cox models of time-dependent covariates.

162 In a Cox-model of predefined variables, age, FLT3-ITD and >1

163 In a multivariate Cox model, only pretransplant histological score was sig

164 In unadjusted Cox models over a maximum follow-up of 8.9 years, the ha

165 grade, and performance status (multivariate Cox model P < .05, log-rank P < .001).

166 ng a backward procedure in the multivariable Cox model (patient's age, tumour size, Federation Franca

167 Risk-adjusted Cox models predicting hazard of mortality by LN count sh

168 en enrollment and the 9-month vaccination in Cox models, providing admission hazard rate ratios (HRRs

169 Time-dependent frailty Cox models quantified the association between RPM use an

170 survival in all LVAD patients (n=111) using Cox modeling, receiver-operator characteristic (ROC) cur

171 the accuracy of survival prediction over the Cox models regularized by L(2) or L(1).

172 , was assessed using logistic regression and Cox models, respectively.

173 e transplant using multivariate logistic and Cox models, respectively.

174 dian follow-up of 55 months, a multivariable Cox model revealed no significant differences for distan

175 Cox models revealed that men with circulating antibodies

176 hin-subject correlation (ignored in a simple Cox model, robust standard errors in a variance-correcti

177 Multivariate Cox modelling showed that HALT-HCC was significantly ass

178 Propensity score-matched Cox models showed an independent and protective associat

179 Adjusted Cox models showed that each additional point in the Acut

180 We used a Cox model stepwise selection procedure to identify subgr

181 Hazard ratios (HRs) were estimated from Cox models stratified by matched set and adjusted for po

182 95% CIs were estimated through multivariable Cox models stratified by trial.

183 h a hazard ratio and 95% CI estimated from a Cox model, stratified by study.

184 In a multivariate Cox model, subclinical ABMR at 1 year was independently

185 isease comorbidity can have on risk-adjusted Cox models, such as those used by SRTR and CMS.

186 isease comorbidity can have on risk-adjusted Cox models, such as those used by SRTR and CMS.

187 Results from the Cox model suggest a strong effect of adrenalectomy on lo

188 Cox model survival regression analysis was used to model

189 ios (HRs) with 95% CIs derived from adjusted Cox models; survival estimates are reported at 2 and 5 y

190 In multivariable Cox models, T-wave morphology dispersion and total cosin

191 Multivariable Cox models tested the association between time-dependent

192 A hazard ratio (HR) from a stratified Cox model that exceeded 1.18 for TC6 versus TaxAC was pr

193 t list and after LT were modeled by use of a Cox model that incorporated transplantation status as a

194 In Cox models that adjusted for age, race/ethnicity, blood

195 nor recipient 1-year and living donor 3-year Cox models that included all seven covariates demonstrat

196 nor recipient 1-year and living donor 3-year Cox models that included all seven covariates demonstrat

197 In Cox models that included codeletion status, the adjusted

198 higher discrimination for both PFS and OS in Cox models that included MRD (as opposed to CR) for resp

199 rvival analyses were performed with weighted Cox models that used inverse probability of censoring we

200 or TLG42% variable was used for a univariate Cox model, the Akaike information criterion difference o

201 In a Cox model, the association between lower UPSIT score and

202 To address this question, we examined, using Cox models, the predictive effects of school achievement

203 We used the Cox model to estimate adjusted probabilities of primary

204 core and other variables were entered into a Cox model to explore the independent effect of AVR on ou

205 -dependent covariates were entered in: (1) a Cox model to investigate their impact on full-blown PML-

206 Using hierarchical Cox modeling to adjust for imbalanced baseline character

207 We also used multivariable Cox modelling to assess whether GARD was independently a

208 's study center and used marginal structural Cox models to account for time-varying psychological str

209 We used Cox models to assess factors associated with both cancer

210 We used multivariable Cox models to assess mortality risk with adjustment for

211 Time-dependent Cox models to assess risk for NHL in treatment-naive pat

212 Measurements: Time-dependent Cox models to assess risk for NHL in treatment-naive pat

213 We used stratified multivariable Cox models to assess the prognostic associations of peri

214 We used Cox models to assess whether PH (PASP>35 mm Hg) was asso

215 We used Cox models to calculate adjusted hazard ratios (HRs) and

216 We used Cox models to calculate hazard ratios (HRs), adjusted fo

217 We used Cox models to calculate hazard ratios and 95% confidence

218 We used Cox models to determine mortality hazard ratios, control

219 haracteristics at listing and at HT and used Cox models to determine whether myocarditis is independe

220 We used sex-specific Cox models to establish the independent effects of each

221 sentation of cardiovascular disease and used Cox models to estimate cause-specific hazard ratios (HRs

222 We used Cox models to estimate cause-specific hazard ratios (HRs

223 We used multivariable Cox models to estimate hazard ratios (HRs) for the prima

224 We used time-dependent Cox models to estimate the association between current a

225 We used time-varying Cox models to estimate the association between recipient

226 We used stratified Cox models to estimate unadjusted and adjusted (for sex,

227 d 3) construction of Kaplan-Meier curves and Cox models to evaluate sequential acquisition and cleara

228 e Research Datalink with BMI data, we fitted Cox models to investigate associations between BMI and 2

229 We used multivariate Cox models to predict graft and patient survival.

230 We used Cox models to relate BDNF levels to the risk for dementi

231 ty of high-throughput genomic data, existing Cox models trained on any particular dataset usually gen

232 In multivariable Cox models, transplant recipients with an initial room-a

233 n-treatment CRP with subsequent CV events in Cox models using a subset of white subjects with no hist

234 icators, the hazard ratio from a traditional Cox model was 1.34 (95% confidence interval: 0.98, 1.83)

235 The Cox model was compared with parametric survival models,

236 A weighted Cox model was created to determine the effect of SP on t

237 A multivariable Cox model was fitted for each time point, which showed t

238 A time-dependent covariate Cox model was used to determine the effect of donor-reci

239 A Cox model was used to evaluate inpatient mortality.

240 A Cox model was used to examine and identify predictors of

241 Multivariate Cox modeling was used to assess the impact of CARV isola

242 nts who completed neoadjuvant AI, stratified Cox modeling was used to assess whether time to recurren

243 Multivariable Cox modeling was used to compare the likelihood of outpa

244 Cox modeling was used to test whether category of hospit

245 t and regression formula of the multivariate Cox model, we identified a "5-gene score" associated wit

246 In a Cox model, we noted that districts with higher populatio

247 Using Cox models, we identified factors associated with death

248 increased risk of death in the multivariable Cox model were older age, male sex, comorbidities (immun

249 stratum, hazard ratios based on conditional Cox models were 0.98 (95% CI: 0.94, 1.02) and 1.17 (95%

250 The Kaplan-Meier method and multivariate Cox models were applied, with the different types of inf

251 Propensity score matching and Cox models were applied.

252 ompeting mortality, whereas risk scores from Cox models were associated with both increased cancer-sp

253 Graft and patient survival were compared and Cox models were built to determine independent predictio

254 Cox models were calculated to investigate the effect of

255 Cox models were constructed to evaluate differences in p

256 Adjusted, sex-stratified Cox models were constructed.

257 METHODS AND Cox models were derived from SPRINT trial data and valid

258 Hazard ratios from Cox models were expressed as positive or negative, strat

259 Multivariable mixed-effects Cox models were fitted to evaluate the influence of depr

260 The Cox models were then used to estimate the absolute reduc

261 Univariate and multivariate Cox models were used to analyze the relationship between

262 Kaplan-Meier curves and Cox models were used to assess genotype-specific cumulat

263 Cox models were used to assess the influence of ectopic

264 Correlations and Cox models were used to assess the relationship among ST

265 Logistic regression and adjusted Cox models were used to compare LapDP and OpenDP with re

266 Cox models were used to compare risks of mortality and h

267 Multivariable Cox models were used to determine the association betwee

268 Uni- and multivariate Cox models were used to determine the best cutoffs, as w

269 Cox models were used to estimate associations of time-va

270 Kaplan-Meier curves and adjusted Cox models were used to estimate cumulative probabilitie

271 Multivariable Cox models were used to estimate hazard ratios (HRs) and

272 Multivariable Cox models were used to estimate hazard ratios (HRs) and

273 Multivariable Cox models were used to estimate the hazard ratios and 9

274 Cox models were used to evaluate whether the reduction i

275 Cox models were used to examine (1) the hazard ratios fo

276 Multivariable Cox models were used to examine how the event risk chang

277 Adjusted Cox models were used to identify OTUs that were signific

278 Multivariable Cox models were used to test for interactions between co

279 zation AF/AT was prospectively assessed, and Cox models were used to test the independent association

280 Multivariable Cox models were used to test the prognostic significance

281 just for patient and lifestyle factors), and Cox models were used.

282 Mixed effect regression (and Cox) models were used to assess the association between

283 ial logistic regression) and survival (using Cox modeling) were examined across terciles.

284 ined independent statistical significance in Cox models when adjusted for the covariates of age and M

285 A Cox model, which adjusted incidence for participant demo

286 ncreased risk of subsequent dementia using a Cox model with pneumonia as a time-varying covariate.

287 A Cox model with restricted cubic splines identified the l

288 l analgesia was included simultaneously in a Cox model with the confounding factors age, American Soc

289 se mortality were examined in time-dependent Cox models with adjustment for relevant confounders.

290 pes were studied as censored variables using Cox models with age as time scale.

291 Cox models with exposure to sulfonylureas and DPP-4 inhi

292 repeated yearly measures and fixed-covariate Cox models with only baseline values after controlling f

293 hemotherapy and survival was evaluated using Cox models with restricted cubic splines.

294 alterations on CLAD risk was assessed using Cox models with serial BAL measurements as time-dependen

295 Using Cox models with time-varying covariates, we examined the

296 Cox models with time-varying number of RFs in control we

297 tors of revascularization, and multivariable Cox models with treatment strategy as a 3-level time-var

298 In a proportional hazards (Cox) model with adjustment for relevant covariates and m

299 treatment and amiodarone was tested using a Cox model, with main effects for randomized treatment an

300 tically with these 2 genotype groups under a Cox model, with P values of 0.000999 and 0.00366, respec