1 ar mixed model) with time to event outcomes (
Cox regression).
2 and adjusted for baseline characteristics by
Cox regression.
3 follow-up were analyzed using multivariable
Cox regression.
4 tive risk (RR) of psoriasis was estimated by
Cox regression.
5 y outcome was overall survival, evaluated by
Cox regression.
6 th decreased overall survival on univariable
Cox regression.
7 sive simulations and compared with the lasso
Cox regression.
8 with patient survival by using multivariate
Cox regression.
9 ational Patient Register, were analyzed with
Cox regression.
10 nd graft loss were estimated by time-varying
Cox regression.
11 ied treatment were compared and estimated by
Cox regression.
12 ulated mortality rate ratios (MRRs) based on
Cox regression.
13 repeat revascularization, was assessed with
Cox regression.
14 gnostic effects for overall survival (OS) by
Cox regression.
15 alysis with log-rank tests and multivariable
Cox regression.
16 gene RS by using log-rank, Kaplan-Meier, and
Cox regression.
17 hs [range, 6-108 months]) was examined using
Cox regression.
18 ence of apoC-III with risk of diabetes using
Cox regression.
19 ents without such cancer (comparators) using
Cox regression.
20 score-matched analysis with a shared frailty
Cox regression.
21 ios and 95% confidence intervals computed in
Cox regressions.
22 al analysis included Kaplan-Meier curves and
Cox regressions.
23 ograft failure were assessed by multivariate
Cox regression adjusted for recipient, donor, and transp
24 zard ratio [HR], 1.21; 95% CI, 0.95-1.54) in
Cox regression adjusted for sociodemographic covariates,
25 We used multivariable
Cox regression adjusted for sociodemographic factors, tu
26 ogression of PDR and VH were calculated with
Cox regression after stratifying by baseline diabetic re
27 datasets using Kaplan-Meier and multivariate
Cox regression analyses and was further validated in 42
28 We used multivariable
Cox regression analyses for incident diabetes (892 new c
29 Multivariate
Cox regression analyses revealed that GPS, NLR, and occu
30 We used
Cox regression analyses to examine the association betwe
31 We performed multivariable
Cox regression analyses to identify factors associated w
32 Cox regression analyses was used to calculate univariate
33 Registry, Kaplan-Meier, competing risk, and
Cox regression analyses were performed on adult, first k
34 Multivariable
Cox regression analyses were performed to assess differe
35 Cox regression analyses were performed to correlate both
36 Multivariate
Cox regression analyses were performed, censoring at car
37 Kaplan-Meier and
Cox regression analyses were performed.
38 Cox regression analyses were performed.
39 hose with and without MLNR were compared and
Cox regression analyses were used to adjust for demograp
40 Extended
Cox regression analyses were used to estimate hazards of
41 Logistic and
Cox regression analyses were used to evaluate perioperat
42 The Kaplan-Meier method and
Cox regression analyses were used to identify predictors
43 Cox regression analyses were used to investigate prospec
44 tic makeup, and early environmental factors,
Cox regression analyses were used, conditioning on indiv
45 and an additional 7 were also significant in
Cox regression analyses when adjusted for age, sex, and
46 ll survival was analyzed using multivariable
Cox regression analyses, adjusting for diagnosis year, r
47 In multivariable-adjusted
Cox regression analyses, ID associated with increased mo
48 In
Cox regression analyses, larger CTG expansions were sign
49 In univariate and multivariate
Cox regression analyses, only female recipient was assoc
50 In
Cox regression analyses, patients with NNAs at screening
51 endocrine therapy, we used Kaplan-Meier and
Cox regression analyses, stratified according to trial a
52 In separate
Cox regression analyses, the MRI-derived left ventricula
53 Notably, in
Cox regression analyses, we found no association of effl
54 ssion analyses and for overall survival with
Cox regression analyses.
55 rtality score) to control for confounders in
Cox regression analyses.
56 as examined by single-marker and multimarker
Cox regression analyses.
57 aft loss (DCGL) were examined using adjusted
Cox regression analyses.
58 ession before breast cancer in multivariable
Cox regression analyses.
59 e analyzed by Kaplan-Meier log-rank test and
Cox regression analysis ( P < 0.05).
60 was confirmed as an independent predictor in
Cox regression analysis (hazard ratio, 1.97 [95% CI, 1.1
61 Expanded Disability Status Scale score in a
Cox regression analysis (per 1-SD increase in MSIS-29-PH
62 TBM than CC-genotype patients, according to
Cox regression analysis (univariate P = .040 and multiva
63 ctors of AT were identified by multivariable
Cox regression analysis accounting for left truncation.
64 We used
Cox regression analysis and the landmark approach to inv
65 se mortality and cardiovascular mortality by
Cox regression analysis and with severity of disease by
66 After
Cox regression analysis controlling for age, tumor size,
67 Cox regression analysis demonstrated that factors indepe
68 Cox regression analysis estimated the instantaneous haza
69 Cox regression analysis explored risk factors for interi
70 Multivariable
Cox regression analysis identified that Model A or Model
71 Moreover, multivariable
Cox regression analysis identified the combination of B3
72 Multivariate
Cox regression analysis of MIPI before postibrutinib tre
73 Multivariable
Cox regression analysis revealed GLS and LAVI to be inde
74 Cox regression analysis revealed that BK was a significa
75 Cox regression analysis revealed that elevated PDW was a
76 Cox regression analysis revealed that reduced MPV was an
77 Cox regression analysis revealed that the amount of resi
78 Multivariable
Cox regression analysis showed that intrahospital CVEs (
79 Multivariable
Cox regression analysis showed that intrahospital Pneumo
80 Cox regression analysis showed that macrovascular invasi
81 Cox regression analysis showed that MPV was an independe
82 Multivariable
Cox regression analysis showed that the third versus the
83 Multivariable
Cox regression analysis tested the relationship between
84 We used
Cox regression analysis to assess differences in risk fo
85 We used
Cox regression analysis to calculate the hazard ratio (H
86 t skin cancer were tested using multivariate
Cox regression analysis to yield adjusted hazard ratios
87 Cox regression analysis was performed to assess the adju
88 Multivariable linear, logistic, and
Cox regression analysis was performed.
89 S) and overall survival, and a multivariable
Cox regression analysis was performed.
90 Multivariate
Cox regression analysis was used to account for the infl
91 Cox regression analysis was used to assess the effects o
92 Cox regression analysis was used to compute 1- to 35-yea
93 Cox regression analysis was used to determine the associ
94 Multivariate
Cox regression analysis was used to identify covariates
95 The Kaplan-Meier method and
Cox regression analysis were performed to calculate cumu
96 Chi-squared tests and multivariate
Cox regression analysis were performed.
97 On multivariable
Cox regression analysis, 3 preoperatively available fact
98 In single-predictor
Cox regression analysis, age, disease stage, tumor weigh
99 Analysis was done by
Cox regression analysis, ANOVA, and chi(2).
100 In
Cox regression analysis, elderly recipients of elderly D
101 tly associated with outcome by multivariable
Cox regression analysis, in addition to age, NT-proBNP s
102 In multivariate
Cox regression analysis, interaction between use of suni
103 In
Cox regression analysis, neither increasing the number o
104 In multivariate
Cox regression analysis, only LV ejection fraction (EF)
105 In multivariable
Cox regression analysis, Share 35 was associated with im
106 In multivariable
Cox regression analysis, treatment with either regimen (
107 On
Cox regression analysis, younger age was independently a
108 incident cancer were examined using adjusted
Cox regression analysis.
109 5% CI, 1.11-3.08) persisted on multivariable
Cox regression analysis.
110 log-rank survival analysis and multivariate
Cox regression analysis.
111 development were evaluated via multivariate
Cox regression analysis.
112 % confidence interval [CI], .38-1.48) in the
Cox regression analysis.
113 ence interval, 0.84-0.99) in a multivariable
Cox regression analysis.
114 edictors were identified using multivariable
Cox regression analysis: connective tissue disease (haza
115 A
Cox-regression analysis revealed that mortality was much
116 After multivariate
Cox-regression analysis, higher PDRI (hazard ratio [HR],
117 We used
Cox regression and a case-only approach to test for mult
118 Statistical analyses were performed with
Cox regression and adjusted for main confounders.
119 Cox regression and ANCOVA were used for the analyses.
120 with outcome were analyzed by multivariable
Cox regression and correlations with echocardiographic m
121 We used multivariate
Cox regression and found two independent MRI predictors
122 d Network for Organ Sharing, competing risk,
Cox regression and Kaplan-Meier analyses were performed
123 Based on country-specific Prentice-weighted
Cox regression and random-effects meta-analysis, the FA-
124 nd cardiovascular events were estimated with
Cox regression and standardized incidence rates.
125 We used stepwise
Cox regression and the Kaplan-Meier method to assess var
126 s, hazard ratios (HRs) were calculated using
Cox regression and were tested against a noninferiority
127 g characteristic curve, Kaplan-Meier method,
Cox regression,
and classification and regression tree (
128 analysis with log-rank tests, multivariable
Cox regression,
and propensity score matching.
129 Adjusted
Cox regression,
corrected for treatment adjustments, sho
130 d using multivariable linear regression, and
Cox regression defined the association between baseline
131 ed by univariate and subsequent multivariate
Cox regression for predicting patient survival.
132 and future acute and fatal CHD events using
Cox regression,
Gray's model, and competing risks analys
133 We used
Cox regression (
hazard ratios [HRs]) to compare survival
134 In multivariable
Cox regression,
HCC was not associated with post-LT surv
135 Cox regression identified compliance with optimal medica
136 Best subset selection analyses with
Cox regression identified subsets of frailty measures th
137 ithout bridging LRT utilizing competing risk
Cox regression in consecutive patients from 20 US center
138 Cox regression including a propensity score for receivin
139 Cox regression,
logistic regression, and classification
140 with overall survival by use of multivariate
Cox regression:
MELD-sodium (MELD-Na), tumour burden sco
141 In a
Cox regression model adjusting for clinical variables, h
142 follow-up period analyzed by a multivariable
Cox regression model and an analysis of covariance model
143 rtality were compared between groups using a
Cox regression model controlling for demographic charact
144 A
Cox regression model including RECPAM classes confirmed
145 A
Cox regression model showed fewer teeth, higher age, and
146 In a multivariable
Cox regression model that included age, left ventricular
147 We used a
Cox regression model to analyze associations between pre
148 A
Cox regression model was fitted for each indication to d
149 A
Cox regression model was used for multivariate analysis
150 A
Cox regression model was used to determine whether type
151 Mortality risk was evaluated using
Cox regression model with propensity score calibrated fo
152 A
Cox regression model, adjusted for age, sex, race/ethnic
153 In multivariate
Cox regression model, age, sex, TNM stage, and PDW were
154 In a
Cox regression model, exposure to voriconazole alone (ad
155 In a multivariate time-varying
Cox regression model, HCV-infected patients had a 27% in
156 In a
Cox regression model, transplantation at the weekend was
157 in waitlisted patients using a multivariate
Cox regression model, with a competing risk approach as
158 t of vWF on prognosis was calculated using a
Cox regression model.
159 HLA matching on survival was studied using a
Cox regression model.
160 he effect of betaPV by using a multivariable
Cox regression model.
161 model was similar to a previously published
Cox regression model.
162 carried out with univariate and multivariate
Cox regressions model.
163 Analysis with
Cox regression modeling showed that complicated ulcer he
164 Next, we performed multivariable
Cox regression modeling to determine factors associated
165 Multivariate
Cox regression models (adjusted for age, diabetes, sex,
166 In
Cox regression models (adjusted for demographics, measur
167 as associated with outcomes in multivariable
Cox regression models (eg, hazard ratio 1.75 per 5% incr
168 former was based on an ensemble of penalised
Cox regression models (ePCR), which uniquely identified
169 ncreasing height (Q1-Q5) using multivariable
Cox regression models adjusted for demographics, comorbi
170 justed hazard ratios (HRs) were estimated by
Cox regression models and presented with 95% CIs.
171 ity and non-relapse mortality using adjusted
Cox regression models at day 200 after transplantation.
172 Multivariable-adjusted
Cox regression models estimated hazard ratios and 95% co
173 rd ratios (HR) and 95% CIs with multivariate
Cox regression models fitting stromal TILs as a continuo
174 We also used
Cox regression models in a prospective cohort of 174 pri
175 The multilevel
Cox regression models investigated the influence of orga
176 rank survival analysis and then multivariate
Cox regression models looking for association with overa
177 Stratified
Cox regression models provided propensity-adjusted hazar
178 Survival analyses and
Cox regression models revealed that TACE and a combinati
179 Multivariable
Cox regression models showed that PENK level was an inde
180 We used
Cox regression models to compare the risk of HCC in pati
181 We used
Cox regression models to determine the adjusted hazard r
182 We used
Cox regression models to estimate incidence rate ratios
183 We used multivariable time-dependent
Cox regression models to evaluate vaccine effectiveness,
184 We used
Cox regression models to examine rates and predictors of
185 We used
Cox regression models to investigate the relation betwee
186 Hazard ratios were estimated with weighted
Cox regression models using Barlow weights to account fo
187 Cox regression models were built to examine differences
188 on between potato consumption and mortality,
Cox regression models were constructed to estimate HRs w
189 Age-adjusted and multivariable
Cox regression models were performed to determine the re
190 Log-rank tests and
Cox regression models were used for univariate and multi
191 Multivariable
Cox regression models were used to analyze data.
192 Cox regression models were used to assess the associatio
193 Cox regression models were used to calculate hazard rati
194 Time-dependent
Cox regression models were used to calculate hazard rati
195 Cox regression models were used to estimate adjusted sur
196 Cox regression models were used to estimate hazard ratio
197 Cox regression models were used to estimate hazard ratio
198 Multivariable
Cox regression models were used to estimate hazard ratio
199 Cox regression models were used to estimate HRs and 95%
200 Cox regression models were used to identify factors asso
201 Cox regression models were used to identify risk factors
202 Univariable and multivariable
Cox regression models were used to investigate the prima
203 Cox regression models were used to study predictors of s
204 Kaplan-Meier and
Cox regression models were used to test for associations
205 hazard ratios were estimated using extended
Cox regression models with recent CD4 count and VL analy
206 rious clinical outcomes using time-dependent
Cox regression models with repeated yearly measures and
207 first episode of self-harm were analyzed in
Cox regression models with time-varying treatment, adjus
208 Results: After adjustment for covariates,
Cox regression models with up to 45 years of follow-up d
209 uously (per 5% of energy) were obtained from
Cox regression models, adjusting for demographic factors
210 tween breast cancer and air pollutants using
Cox regression models, adjusting for major lifestyle ris
211 tcomes was examined using either logistic or
Cox regression models, adjusting for patient disease and
212 incident CVD by using linear regression and
Cox regression models, respectively.
213 - and sex-adjusted and multivariate-adjusted
Cox regression models, whatever the significance thresho
214 or stroke, which was assessed using adjusted
Cox regression models.
215 up to 3 years after device implantation with
Cox regression models.
216 variable analyses by Flexible Parametric and
Cox regression models.
217 ethods, and predictors were determined using
Cox regression models.
218 gan transplant recipients using multivariate
Cox regression models.
219 t failure; risk factors were studied using a
Cox regression models.
220 ere tested using univariate and multivariate
Cox regression models.
221 (BP) and risk of mitral regurgitation using
Cox regression models.
222 e-specific hazard ratios were obtained using
Cox regression models.
223 aft and patient survivals were assessed with
Cox regression models.
224 to 85% transplants according to multivariate
Cox regression models.
225 coronary heart disease were estimated using
Cox regression models.
226 -induced cardiac toxicity were identified by
Cox regression models.
227 sed risk factors for ESRD using multivariate
Cox regression models.
228 utcomes between 1994 and 2012 using adjusted
Cox regression models.
229 trial group were calculated with the use of
Cox regression models.
230 CH cases using propensity score matching and
Cox regression models.
231 vior in the TD/CTD cohort were studied using
Cox regression models.
232 Applying
Cox regression,
no crude association to graft loss was f
233 Cox regression of prostate cancer death in each trial gr
234 os were estimated by logistic regression and
Cox regression,
respectively.
235 , the rate was 87% versus 94% (P = 0.24) and
Cox regression showed no statistically significant diffe
236 A prognostic index was built by
Cox regression (
stepwise selection) using data from 401
237 ion to estimate hazard ratios and a modified
Cox regression,
taking into account competing risks to d
238 In multivariable
Cox regression,
the presence of nonobstructive LM plaque
239 In univariable
Cox regressions,
the studied cell subsets were not assoc
240 We used paired
Cox regression to analyze the primary outcomes of death
241 y trends in carcinoid syndrome incidence and
Cox regression to assess the relationship between carcin
242 We used
Cox regression to associate physical activities and NO2
243 We used propensity matching and
Cox regression to compare rates of the outcomes with riv
244 We used
Cox regression to compute hazard ratios and 95% confiden
245 hed comparison cohort (n = 774 017), we used
Cox regression to compute rates and confounder-adjusted
246 We used linear, logistic, and
Cox regression to control for potential confounders.
247 clustering of patients within facilities and
Cox regression to determine the volume-outcome relations
248 nd clinical variables and used multivariable
Cox regression to develop a clinical prediction model ba
249 val curves and mortality rate estimation and
Cox regression to establish independent predictors of al
250 We used
Cox regression to estimate hazard ratios (HRs), stratify
251 We used Bayesian
Cox regression to estimate reinfection rates according t
252 We used Poisson and
Cox regression to evaluate pre- and posttreatment risk f
253 Methods We performed
Cox regression to evaluate the association of tumor-base
254 We used
Cox regression to examine the adjusted associations of p
255 k factors were analysed using time-dependent
Cox regression to examine their potential influence on t
256 by weighting and matching and multivariable
Cox regression to minimize treatment selection bias.
257 ing "harmonic analysis." We applied harmonic
Cox regression to model confounder-adjusted effects of t
258 We used multivariable
Cox regression to model the association of preexisting c
259 t absolute shrinkage and selection operator)
Cox regression to predict progression-free survival (PFS
260 We used
Cox regressions to estimate VE against all tuberculosis
261 According to a
Cox regression unadjusted analysis, the rate of overall
262 On multivariable
Cox regression,
upfront surgery was not associated with
263 ncer survival by the Kaplan-Meier method and
Cox regression using a matched comparison cohort of canc
264 rvival analysis was performed using adjusted
Cox regression,
using relevant adjusted variables.
265 A weighted, multivariable, extended
Cox regression was conducted, which suggested that in nu
266 were derived by the Kaplan-Meier method, and
Cox regression was performed to investigate the relation
267 Cox regression was stratified by matched groups and also
268 Multivariable
Cox regression was used to assess risk factors for reflu
269 ates were ascertained and propensity-matched
Cox regression was used to compare event rates according
270 Cox regression was used to compute the hazard ratios (HR
271 Cox regression was used to determine propensity score-ad
272 Time-dependent
Cox regression was used to establish a counterfactual fr
273 Multivariable
Cox regression was used to estimate adjusted hazard rati
274 Cox regression was used to estimate adjusted hazard rati
275 Prentice-weighted
Cox regression was used to estimate country-specific HRs
276 Survival analysis using
Cox regression was used to estimate hazard ratios for de
277 termine whether troponin levels were stable,
Cox regression was used to estimate risks for all-cause,
278 Stratified
Cox regression was used to estimate the hazard ratios of
279 was conducted using Kaplan-Meier curves, and
Cox regression was used to identify factors influencing
280 Univariable and multivariable
Cox regression was used to investigate the association o
281 Multiple
Cox regression was used to select and weight prognostic
282 Cox regression was used to study the association of mort
283 Multivariable competing-risks
Cox regression was used, including adjustment for birth
284 Chi-square tests and
cox-regression was used to determine association between
285 With
Cox regression,
we compared the hazard rates of HE grade
286 Multivariable logistic and
Cox regression were performed to identify predictors of
287 The Kaplan-Meier method and
Cox regression were used for the statistical analysis.
288 Generalized estimating equations and
Cox regression were used to assess associations of socio
289 Propensity score matching and stratified
Cox regression were used to compare the 2 strategies.
290 Kaplan-Meier method and
Cox regression were used to evaluate the prognostic impa
291 Kaplan-Meier method and
Cox regression were used to evaluate the prognostic impa
292 Multivariable logistic and
Cox regression were utilized to assess the outcomes of a
293 Cox regressions were used for survival (time-to-event) a
294 95% confidence intervals (CIs) for TBI in a
Cox regression,
while adjusting for age, sex, race/ethni
295 illance on colorectal cancer incidence using
Cox regression with adjustment for patient, procedural,
296 edicare and Medicaid Services linkage, using
Cox regression with late entries.
297 Survival analysis was performed with
Cox regression with survival censored past 90 days.
298 Data were analyzed by
Cox regression,
with adjustment for sex, age, HbA1c, DN,
299 nd self-reported measles was estimated using
Cox regression,
with VE calculated as 1 minus the hazard
300 Cox regression yielded adjusted hazard ratios (HRs) asso