ライフサイエンスコーパス: Cox model

1 terval [CI], 0.46 to 0.90 by pair-stratified Cox model).

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

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

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

5 and conducted multivariate analysis using a Cox model.

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

7 ing an inverse probability weighted marginal Cox model.

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

9 in the literature, kernel Cox regression and Cox model.

10 ing 95% confidence interval estimated from a Cox model.

11 g the exposure period using a time-dependent Cox model.

12 vival were assessed by using a multivariable Cox model.

13 were handled as time-dependent covariates in Cox models.

14 ndard ambulatory care and investigated using Cox models.

15 ar disease outcomes using crude and adjusted Cox models.

16 were estimated with the use of multivariable Cox models.

17 ause mortality with the use of multivariable Cox models.

18 examined all-cause mortality using adjusted Cox models.

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

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

21 Survival predictors were tested in Cox models.

22 were estimated using adjusted discrete-time Cox models.

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

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

25 and required the use of marginal structural Cox models.

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

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

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

29 1.50; 95% CI, 1.07-2.12) in fixed-covariate Cox models.

30 dictors of ASCVD events in the multivariable Cox models.

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

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

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

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

35 clinical events with logistic regression and Cox models.

36 al failure were assessed using multivariable Cox models.

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

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

39 nalyzed using Kaplan-Meier and multivariable Cox models.

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

41 sted for several potential confounders using Cox models.

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

43 and stratified univariable and multivariable Cox models.

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

45 phthalmitis were examined using multivariate Cox models.

46 F were evaluated with multivariable adjusted Cox models.

47 rvival based on cross-validated multivariate Cox models.

48 were explored as predictors of mortality in Cox models.

49 d with Kaplan-Meier curves and multivariable Cox models.

50 covery at week 48, and death by week 48 with Cox models.

51 ied and contrasted with CHD and stroke using Cox models.

52 art failure (n=216) were determined by using Cox models.

53 ney transplant on the basis of multivariable Cox modeling.

54 ected relative risk and an interval-censored Cox model accurately estimate VES and only require serol

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

56 In a Cox model adjusted for age, sex, and smoking history, dr

57 We used Cox models adjusted for age and melanoma risk factors.

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

59 Multivariable Cox models adjusted for age, body mass index, surgery, a

60 c graft loss and death were determined using Cox models adjusted for baseline donor, recipient, and t

61 studied through landmark and time-dependent Cox models adjusted for baseline tumor load, occurrence

62 In Cox models adjusted for clinical risk factors, 29 protei

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

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

65 Cox models adjusted for potential confounders were used

66 In Cox models adjusted for risk factors for coronary artery

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

68 From Cox models adjusted for sociodemographic, behavioral, an

69 g the Kaplan-Meier method and compared using Cox models adjusted for treatment and stratification fac

70 a were thereafter combined, and a stratified Cox model, adjusted for covariates, was fitted in order

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

72 and this finding persisted on multivariable Cox modeling adjusting for demographic, clinical, and tr

73 [Apo] A1 and B) with CVD were compared using Cox models adjusting for classical risk factors, and pre

74 Marginal structural Cox models adjusting for time-dependent confounding were

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

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

77 as implemented in a time-dependent covariate Cox model, adjusting for treatment with other glucose-lo

78 idence intervals (CIs) were calculated using Cox modeling, adjusting for risk factors associated with

79 In such scenarios, the relative merits of a Cox model, an accelerated failure time model, a mileston

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

81 Forest plots were created based on Cox model analysis.

82 Cox models analyzed time to first ARI (upper or lower) b

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

84 VD outcome and mortality were compared using Cox models and adjusting for atherosclerotic risk factor

85 Survival analysis was performed by Cox models and component-wise boosting.

86 Adjusted associations were estimated using Cox models and event rates and population attributable f

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

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

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

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

91 were evaluated using multivariable-adjusted Cox models and multiplicative interactions of CAC with s

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

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

94 revascularization rates were compared using Cox modeling, and patients were matched by propensity sc

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 ing demographics-adjusted, cohort-stratified Cox models, and we compared models using Akaike's inform

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 tive incidence differences and multivariable Cox models assessed the association between dialysis fac

101 es related to survival outcome; however, the Cox model assumes proportional hazards (PH), which is un

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

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

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

105 In an adjusted Cox model, brincidofovir exposure remained associated wi

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 The final Cox model consisted of 4 habitat evolution-based feature

110 Cox modeling demonstrated that PSC patients have a posit

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

112 Adjusted Cox models demonstrated that body mass index greater tha

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

114 In a multivariate Cox model, DSA with DGF was an independent predictor for

115 Cox models estimated hazard ratios (HRs) with 95% CIs ac

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

117 .09; 90% CI, 0.81 to 1.46; P = .58) did not (Cox model for interaction of study arm and RAS status: P

118 A multivariate Cox model for OS using allogeneic hematopoietic cell tra

119 A Cox model for predictors of time-to-incident visual fiel

120 h inverse probability of treatment weighting Cox modeling for the composite end point of cardiovascul

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

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

123 ipient characteristics, we fit multivariable Cox models for death-censored graft failure and examined

124 atment and outcomes was analyzed by separate Cox models for each outcome.

125 One-year and 3-year Cox models for posttransplant survival were fitted with

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

127 Cox models for RSClin were compared with RS alone and cl

128 Cox models for the primary composite cardiovascular outc

129 Cox models for these outcomes were adjusted for age, sex

130 Cox models for three metrics landmarked at 12 weeks and

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

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

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

134 range, 0-3) MeDi score in the fully adjusted Cox model (hazard ratio, 0.59; 95% confidence interval,

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

136 In multivariable Cox models, higher number of vessels with PAD was associ

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

138 endent variable by generating time-dependent Cox models; HRs at an ELF threshold of 10.51 were 1.94 (

139 A Cox model identified receipt of radiation therapy plus c

140 Multivariable Cox modeling identified age (HR, 1.06; 95% CI, 1.04-1.08

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

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

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

144 9, and 0.007, respectively) in multivariable Cox models in AA TNBCs but not White TNBCs.

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

146 events at both univariable and multivariable Cox modeling, independent of FRS (P < .001).

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

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

149 ection bias, we additionally used a weighted Cox model (marginal structural model) that accounts for

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

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

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

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

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

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

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

157 Recursive partitioning with univariate Cox models of event-free survival ("survival tree regres

158 Discrimination was tested using Cox models of MAGGIC score stratified by race, and combi

159 6 with group 4 tumours) in our multivariable Cox models of progression-free and overall survival.

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

161 Three factors were selected into the Cox model offering significant protection from GVHD deve

162 In the multivariable Cox model, older age (adjusted hazard ratio [aHR] 1.31 [

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

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

165 In the adjusted Cox Model, patients who traveled >360 miles had a slight

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

167 In a multivariable Cox model, prevalent CVD was significantly associated wi

168 and at the National Hospital and assessed in Cox models providing 6-week mortality rate ratios (MRRs)

169 ion on hospitalization risk were assessed in Cox models providing overall and major disease-group inc

170 and at the National Hospital and assessed in Cox-models providing 6-week Mortality Rate Ratios (MRRs)

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

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

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

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

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

176 Cox models revealed that men with circulating antibodies

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

178 Risk-adjusted Cox modeling showed esophagectomy was associated with im

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

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

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

182 CT-P13 versus RP in a multivariable marginal Cox model situated within prespecified margins (0.80 to

183 Multivariable Cox models stratified by center, age, and sex and adjust

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

185 95% CIs were estimated through multivariable Cox models stratified by trial.

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

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

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

189 Multivariable-adjusted Cox models tested the association between CHIP and incid

190 Multivariable Cox models tested the association between time-dependent

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

192 the composite risk value was obtained from a Cox model that incorporated age; nodal status; tumor siz

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

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

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

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

197 Using the weighted Cox model, the death hazard rate (HR) of EVS was 0.71 (9

198 In a multivariable Cox model, the interaction term between LNR and receipt

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

200 In the most adjusted Cox models, the risk of HF was 39% and 62% lower among m

201 In Cox models, there was no difference with regard to risk

202 covariates, we controlled for confounding in Cox models through inverse probability of treatment weig

203 We used the Andersen-Gill extension of the Cox model to estimate the effects of previous infections

204 We applied a multivariable Cox model to examine whether patient and hospital factor

205 ivariable analyses were performed by using a Cox model to identify variables associated with time to

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

207 Using hierarchical Cox modeling to adjust for imbalanced baseline character

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

209 We used Cox models to adjust for age and melanoma risk factors.

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

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

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

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

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

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

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 We used multivariable Cox models to determine the associations between hyperte

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

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

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

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

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

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

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

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

228 ormed competing risk-adjusted cause-specific Cox models to evaluate the effects of metabolic traits (

229 sed recursive partitioning and multivariable Cox models to identify associations between colorectal c

230 We used Cox models to investigate associations between baseline

231 ogical testing of participants once, while a Cox model using only symptomatic infections returns bias

232 the parameter estimates of the multivariable Cox model was computed for all patients.

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

234 A Cox model was developed, adjusting for propensity scores

235 The Cox model was employed to assess variables independently

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

237 A Cox model was used to calculate hazard ratios for ischem

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

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

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

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

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

243 entional Cox regression model and a weighted Cox model, we did not find a survival benefit for patien

244 Using a Cox model, we first estimated hazard ratios of amyotroph

245 en optimizing the parameter estimates in the Cox model, we modified the R package survival; covariate

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

247 ing demographics-adjusted, cohort-stratified Cox models, we assessed associations between anal cancer

248 Kaplan-Meier method, log-rank test, and Cox model were used for analysis.

249 Propensity score matching and Cox modeling were used for analysis.

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

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

252 Adjusted, sex-stratified Cox models were constructed.

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

254 Cox models were fitted to associate HDP with incident ca

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

256 tratified by study) hazard ratios (HRs) from Cox models were obtained for deferred/intermittent ART v

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

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

259 Cox models were used to assess cardiovascular risk assoc

260 Sex-stratified Cox models were used to assess cardiovascular risk.

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

262 Multivariable Cox models were used to assess graft survival.

263 Multivariate Cox models were used to assess the hazard ratios (HRs) o

264 Cox models were used to assess the independent prognosti

265 Pair-stratified Cox models were used to calculate 95% confidence interva

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

267 Cox models were used to compare outcomes by CMV risk and

268 Cox models were used to determine associations between m

269 Multivariable Cox models were used to determine the association betwee

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

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

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

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

274 Cox models were used to estimate multivariable-adjusted

275 Adjusted Cox models were used to evaluate the associations of bas

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

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

278 Cox models were used to examine whether sex modified the

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

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

281 Multivariable 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 patients who did not, using a multivariable Cox model with inverse probability weighting according t

287 Time to CAV diagnosis was assessed using a Cox model with occurrence of clinical rejection analyzed

288 A Cox model with restricted cubic splines identified the l

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

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

291 Multivariable adjusted Cox models with non-HDL cholesterol lower than 2.6 mmol/

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

293 Cox models with QRISK3 predictors and a frailty (random

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

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

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

297 ascular disease, infection, and cancer using Cox models with year of kidney transplant as the primary

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

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

300 ) were explored using separate multivariable Cox models within a competing risks framework.