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1 assessed using random-effects Cox regression survival analyses.
2 er operating characteristic (ROC) curves and survival analyses.
3 diagnosis were examined with descriptive and survival analyses.
4 remained significant in univariate-adjusted survival analyses.
5 re identified by univariate and multivariate survival analyses.
6 in 925 RTR using univariate and multivariate survival analyses.
7 orectomy were evaluated using time-dependent survival analyses.
8 variables in Cox regression and Kaplan-Meier survival analyses.
9 luated using Kaplan-Meier and Cox regression survival analyses.
10 years for renal outcomes and 11.2 years for survival analyses.
11 ssion and Kaplan-Meier methods were used for survival analyses.
12 ortional hazards regression and Kaplan-Meier survival analyses.
13 performed functional gene set enrichment and survival analyses.
14 dates of hospitalization were excluded from survival analyses.
15 ns with generalised estimating equations and survival analyses.
16 xamined through time-dependent covariates in survival analyses.
17 es was evaluated by parametric models and by survival analyses.
18 h age-period-cohort models and breast cancer survival analyses.
19 log-rank p=0.12) or 3-6 (log-rank p=0.98) in survival analyses.
20 dard descriptive statistics and Kaplan-Meier survival analyses.
21 w-up data available and were included in the survival analyses.
22 tional hazards models were used for adjusted survival analyses.
23 r AIDS Cohort Study by means of Kaplan-Meier survival analyses.
24 biased exposure effect estimates in standard survival analyses.
25 e of positive prognostic value in univariate survival analyses.
26 Polarity conversions were evaluated by survival analyses.
27 ombined to allow multivariate and stratified survival analyses.
28 t investigate the effect of interventions by survival analyses.
29 nth prospective follow-up were assessed with survival analyses.
30 er evaluated during long-term follow-up with survival analyses.
31 istries in 11 countries were included in the survival analyses.
32 019, was conducted using cross-sectional and survival analyses.
33 evaluated using t testing of proportions and survival analyses.
34 tistics and Kaplan-Meier curves when used in survival analyses.
35 ng Cox proportional hazards and Kaplan-Meier survival analyses.
36 thin and betanidin were also included in the survival analyses.
37 rs were correlated in uni- and multivariable survival analyses.
38 arisons, clinical prevalence assessments and survival analyses.
39 ding log-rank statistics were calculated for survival analyses.
40 ay followed by univariable and multivariable survival analyses.
41 re were constructed for transplantation-free survival analyses.
42 N, EGFR, and CDKN2A, were assessed alongside survival analyses.
43 x proportional hazards modeling was used for survival analyses.
44 ), version 1.1 and overall survival (OS) for survival analyses.
45 y aborted; these patients were excluded from survival analyses.
46 luded from overall survival and disease-free survival analyses.
47 -naive/acute) was treated as time-varying in survival analyses.
48 ups were analyzed using Kaplan-Meier and Cox survival analyses.
49 o R1/D1 and R2/D2 groups for comparative and survival analyses.
50 through Cox proportional hazards regression survival analyses.
51 dies accounted for socioeconomic position in survival analyses.
52 le inverse probability of treatment weighted survival analyses.
53 y univariate and multivariate shared frailty survival analyses.
54 al were performed, together with conditional survival analyses.
55 -cause mortality was the primary endpoint in survival analyses.
56 Statistical methods included discrete-time survival analyses.
57 re estimated with the use of competing-risks survival analyses.
58 completely the effect of FA on multivariate survival analyses.
59 The Kaplan-Meier method was used for survival analyses.
60 ariate logistic regression, and Kaplan-Meier survival analyses.
61 05 for all but 1 comparison) in Kaplan-Meier survival analyses.
62 rulopathy (TG) development were estimated in survival analyses.
63 -Tree were validated using tumor pathway and survival analyses.
64 of reaching the end points were estimated by survival analyses.
65 inic population through probability-weighted survival analyses.
66 in RFS analyses and 5606 patients in overall survival analyses); 3638 patients were analyzed by cytog
72 Established clinical co-variates dominated survival analyses, although CCP and hypoxia may inform c
73 incidence of hyperglycemia was confirmed by survival analyses among C/C, C/T, and T/T carriers durin
77 was tested using univariate and multivariate survival analyses and by receiver-operating-characterist
79 m mortality were assessed using Kaplan-Meier survival analyses and Cox proportional hazards modeling,
80 pe of event (ischemic or hemorrhagic), using survival analyses and Cox proportional hazards models.
83 urther data maturation is needed for overall survival analyses and evaluation of the full predictive
88 evaluated using adjusted propensity-matched survival analyses and weighted cumulative exposure model
89 multilevel spatiotemporal exposure model and survival analyses) and short-to-medium-term exposure-mor
90 clinical and sociodemographic factors using survival analyses, and conducted genome-wide association
95 ks of mortality and survivorship (hazard and survival analyses) associated with 10 biomarkers and use
106 sults were compared with those from standard survival analyses (e.g., Weibull regression) with time-u
107 on and disease progression were monitored by survival analyses, echocardiography, and electrocardiogr
113 methylation, and contrast-enhancing volume) survival analyses for progression-free and overall survi
116 itive time-lagged associations were found in survival analyses for virtually all temporally primary l
121 l-cause mortality by Cox Proportional Hazard survival analyses in 1840 stable RTR derived from the As
122 as the potential to improve the precision of survival analyses in a number of different sectors, incl
124 een January 1980 and June 2005 that provided survival analyses in patients with breast cancer after a
128 ant marker of poor prognosis in multivariate survival analyses, including classic prognostic markers,
134 ting characteristic analysis, and subsequent survival analyses (Kaplan-Meier, hazard ratios [HRs]) of
136 e response rate and interim progression-free survival analyses, median follow-up was 6.2 months (IQR
138 comprehensive clinicobiologic, genetic, and survival analyses of a large cohort of 213 adult patient
146 By excluding matched patients, KM plots and survival analyses of the unreported subgroups were retri
150 ll-cohort and propensity-matched comparative survival analyses on our 3-center database of Sprint Fid
152 on models have often relied upon traditional survival analyses or outcomes data failing to extend bey
153 the consideration of a continuous series of survival analyses over 7 decades at Massachusetts Genera
166 protein interaction network and Kaplan-Meier survival analyses revealed the importance of METTL14 and
178 x and/or National Death Index, with adjusted survival analyses starting at 24 months after ART initia
186 ically significant according to Kaplan-Meier survival analyses that included 131 (95%) of 138 POWs an
188 .012), and, when tropism was included in the survival analyses, the effect of the SDF1-3'A allele on
191 Medical Center (CUMC) were used in separate survival analyses to compare those who did or did not re
192 ional hazards models along with Kaplan-Meier survival analyses to estimate outcomes at the end of 1,
194 sure records are available in TCGA, existing survival analyses typically did not consider drug exposu
198 sequent hypomania or mania was determined in survival analyses using Cox proportional hazards regress
199 NTS: This prospective cohort study performed survival analyses using data from the Baltimore Longitud
201 simple interface for conducting genome-wide survival analyses using VCF (outputted from Michigan or
202 al hazard regression models were applied for survival analyses, using register-based all-cause mortal
205 dence intervals when performing Kaplan-Meier survival analyses, we recommend adjusting for dependence
206 resonance imaging and histopathological and survival analyses, we show that tumor progression was si
219 ghbor caliper matching of propensity scores, survival analyses were conducted using Cox proportional
220 d using generalized linear mixed models, and survival analyses were conducted using Cox proportional
225 haracteristics of patients were compared and survival analyses were done to evaluate the effect of in
228 ed in the first year after randomization, so survival analyses were landmarked as starting at 1 year
242 t viral pathogens in nasal wash samples, and survival analyses were performed to determine whether in
252 LTL-stratified and medication-stratified survival analyses were performed using multivariable Cox
254 ssed using log-rank tests while multivariate survival analyses were performed using the Cox proportio
262 re investigated at 3 mo after treatment, and survival analyses were performed with the Kaplan-Meier m
279 Inverse probability of treatment weighted survival analyses were used to analyze posttransplant mo
288 proportional hazards regression modeling and survival analyses were used to identify factors related
289 ng multivariable regression and Kaplan-Meier survival analyses were used to predict risk of relapse,
291 y longer PFS and OS (both P 0.03; univariate survival analyses) whereas RANO criteria were not signif
292 onger PFS and OS (both P <= 0.03; univariate survival analyses) whereas RANO criteria were not signif
293 l age at miscarriage (GAM) to assign time in survival analyses, which overestimates duration of expos
296 the limited software options for performing survival analyses with millions of SNPs, we developed gw
299 ression and intervention were estimated from survival analyses, with risk factors determined by using
300 internalizing and externalizing disorders in survival analyses, with time-lagged associations consist