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1 ared to the de novo group (57% versus 69%, P univariate=0.008, P multivariate=0.021).
2  examine two types of coding schemes, namely univariate activity and multivariate pattern, in the pos
3 pendent of encoding-related connectivity and univariate activity measures.
4                                              Univariate analyses (voxel-lesion symptom mapping for tu
5             Using the MBSAQIP data registry, univariate analyses and hierarchical logistical regressi
6                                              Univariate analyses and multiple linear regression were
7                     Furthermore, traditional univariate analyses applied to the same data were insens
8                                           In univariate analyses at month 6, reduction of KIT D816V E
9                                              Univariate analyses found significantly lower levels of
10                             Multivariate and univariate analyses of the metabolome data revealed a lo
11                                              Univariate analyses revealed the recruitment of function
12                                              Univariate analyses were performed to assess the relatio
13 iables associated with hematoma expansion in univariate analyses with P </= .10.
14 A predicted type-specific HPV concordance in univariate analyses, but in multivariable models the ind
15                                           In univariate analyses, diabetic nephropathy class was not
16                                           In univariate analyses, hypodensities were associated with
17                                           In univariate analyses, increased postoperative complicatio
18                                           In univariate analyses, reported as odds ratios (95% CIs),
19                                           In univariate analyses, we found significant heritability f
20 cimens could be more powerful than the usual univariate analyses.
21 as not significantly associated with tSCC in univariate analysis (hazard ratio = 1.48; 95% CI, .95-2.
22 r risk of distant metastases at follow-up in univariate analysis (Log-rank P = 0.0084) but not in mul
23 associated with noninfectious uveitis in the univariate analysis (odds ratio, 2.53; 95% CI, 1.42-4.51
24 ively, and the difference was significant in univariate analysis (P = .004) and in multivariate analy
25  Outcomes were compared in these cohorts via univariate analysis and multivariate logistic regression
26               PCR approached significance at univariate analysis and was not significant at multivari
27 antly associated with both PFS and OS in the univariate analysis and were still statistically signifi
28                                      Initial univariate analysis assessed potential associations betw
29 OP requiring treatment after keratoplasty in univariate analysis but not in multivariate analysis.
30                                              Univariate analysis confirmed incomplete adaptive coding
31                                              Univariate analysis demonstrated differences in QRS axis
32 ighty-one HTx recipients were included, with univariate analysis demonstrating peak hazards of mortal
33                                           In univariate analysis donor type (mother) and GVHD prophyl
34                                              Univariate analysis identified 3 statistically significa
35                                              Univariate analysis identified age (odds ratio [OR], 1.0
36                                              Univariate analysis identified factors associated with I
37                                              Univariate analysis identified pre-extracorporeal membra
38                                              Univariate analysis identified preoperative factors asso
39 st and powerful alternatives to the standard univariate analysis in genome-wide association studies.
40                                              Univariate analysis including Mann-Whitney U test and Sp
41                                              Univariate analysis indicates that both abiotic and biot
42 (HR, 1.53; 95% CI, 1.04-2.24; P = 0.0297) on univariate analysis only.
43  CI, 0.28-0.71; p = 0.0007 and interleukin-6 univariate analysis only: odds ratio, 0.55; 95% CI, 0.36
44                                          The univariate analysis revealed significant correlations be
45                                              Univariate analysis revealed that TLR 2, 3, 4, 7, and 9
46                                              Univariate analysis showed that patients with </= mild p
47   PEEP did not have a large enough effect in univariate analysis to warrant inclusion in the multivar
48 mine, two methodologies have been developed; univariate analysis using CN emission band and multivari
49 tive incidence of grade III-IV acute GVHD on univariate analysis was 8%, 12%, and 17% in the haploide
50                                 In addition, univariate analysis was conducted to predict reduction o
51 significant prognostic factors identified by univariate analysis was performed using step-up and step
52                                              Univariate analysis was used to identify covariates for
53 nificant overall survival prognosticators on univariate analysis were albumin, bilirubin, ascites, tu
54                                           In univariate analysis, age >60 years, radiation dose, bila
55                                           On univariate analysis, ascites (P = .02), liver disease (P
56                                           In univariate analysis, being married (OR = 1.57, 95%CI = 1
57                                           At univariate analysis, BMI (odds ratio, 1.12; 95% confiden
58                              On the basis of univariate analysis, body mass index, liver iron deposit
59                                           On univariate analysis, bulky disease (>10 cm), extranodal
60                                           On univariate analysis, clearance <1.0 or <1.5 mm, pT stage
61                                           In univariate analysis, clustering was significantly associ
62                                   Results In univariate analysis, duodenal invasion and/or EPNI on pr
63                                           In univariate analysis, EZ "normalized" reflectivity was fo
64                                           In univariate analysis, FibroScan values were slightly corr
65                                           At univariate analysis, FTV2 and RCB class had the stronges
66                                           At univariate analysis, FTV2, FTV4, and DeltaFTV4 had signi
67                                           In univariate analysis, having stage 5 ROP (vs. stage 4 ROP
68 val in univariable and multivariable models (univariate analysis, hazard ratio [HR] for a one-fold in
69                                           On univariate analysis, high TRG and lymph node metastases
70                                         In a univariate analysis, higher age (p = 0.0018), male gende
71                                           In univariate analysis, late TBN (P = 0.017) and acid inges
72                                           In univariate analysis, maternal factors associated with NA
73                                           At univariate analysis, mean pixel intensity with spatial s
74                                           In univariate analysis, means of parameters like total leuc
75                                           On univariate analysis, men with adverse pathology at radic
76                                        After univariate analysis, multivariate population-averaged li
77                                         In a univariate analysis, pancreatic cysts were more prevalen
78                                           In univariate analysis, patients in the high PaO2 group had
79                                           In univariate analysis, patients with reactivation were mor
80                                           At univariate analysis, predictors of shorter OS were tumor
81                                 According to univariate analysis, predictors of survival in AL amyloi
82                                           On univariate analysis, presentation age, foveal retinoblas
83                                           In univariate analysis, prior suboptimal response or TKI re
84               On the basis of the results of univariate analysis, significant predictors of diverticu
85                                           On univariate analysis, the parameters associated with HCC
86                                           In univariate analysis, the starting dose of capecitabine (
87                                       In the univariate analysis, the sunitinib group had longer over
88                                           In univariate analysis, there was no difference in rim area
89 se (regression coefficient: 0.7; P = .04) on univariate analysis, whereas age (<70 years old) was the
90 icting melanoma specific survival (MSS) in a univariate analysis, which was also consistent with AJCC
91                                       In the univariate analysis, younger age, higher levels of gamma
92 herapy interventions that was not evident by univariate analysis.
93 different between both outcome groups in the univariate analysis.
94 their impact on biomarkers was identified by univariate analysis.
95 were predictors of successful downstaging on univariate analysis.
96  factors independently associated with CR by univariate analysis.
97 ith an increased significant LVEF drop risk (univariate analysis: hazard ratio, 4.52; P < .001 and ha
98 < .001) and those with BRCA wild-type HGSOC (univariate analysis: reader 1, HR = 2.42, P < .001; read
99                                              Univariate and AUROC analyses were performed to validate
100             We tested our hypotheses through univariate and bivariate heritability analyses in a thre
101                                              Univariate and bivariate statistics were used to describ
102 his observational study applied descriptive (univariate and bivariate) and multivariable logistic reg
103                               The study used univariate and generalized linear mixed models to examin
104 en the proposed method and the commonly used univariate and Lasso approaches for variable selection r
105                                              Univariate and mixed-effects logistic regression analysi
106 diverticulitis recurrence were assessed with univariate and multiple Cox proportional hazard regressi
107                                      We used univariate and multiple logistic regression to examine c
108 zone was significantly correlated with VA in univariate and multiple regression analysis (both P < 0.
109                                              Univariate and multiple regression analysis showed that
110                                              Univariate and multivariable analyses identified indepen
111 cs, operative, and postoperative factors and univariate and multivariable analyses were used to asses
112    Potential risk factors were evaluated via univariate and multivariable analysis to determine predi
113  outcomes were compared between groups using univariate and multivariable Cox proportional hazards mo
114                                              Univariate and multivariable Cox proportional hazards we
115 s with positive findings were analyzed using univariate and multivariable linear regression models.
116                                              Univariate and multivariable regression analyses adjusti
117                                              Univariate and multivariable statistical models after co
118                 Outcomes were analyzed using univariate and multivariable statistical techniques.
119 rior OS compared with low TP53 expression in univariate and multivariate analyses adjusting for MIPI
120 and minor components, were used as inputs in univariate and multivariate analyses aiming to character
121 ion-free survival and overall survival using univariate and multivariate analyses and assessed for hi
122                                              Univariate and multivariate analyses evaluated associati
123                         We conducted primary univariate and multivariate analyses for the intention-t
124 ophages and evaluated their performance with univariate and multivariate analyses involving data belo
125                                              Univariate and multivariate analyses of prognostic facto
126                                              Univariate and multivariate analyses of prognostic facto
127                                              Univariate and multivariate analyses of the a priori hyp
128                                 We performed univariate and multivariate analyses to assess for NOM u
129                                 We performed univariate and multivariate analyses to identify predict
130                                              Univariate and multivariate analyses were conducted to d
131                                              Univariate and multivariate analyses were performed by u
132                                              Univariate and multivariate analyses were performed to i
133                                              Univariate and multivariate analyses were performed.
134                                              Univariate and multivariate analyses were used to assess
135                                           On univariate and multivariate analyses, female sex (men to
136 using the Cox proportional hazards model for univariate and multivariate analyses, the association of
137 orrelation with liver fibrosis (P < .001) at univariate and multivariate analyses.
138  Associations with survival were analyzed by univariate and multivariate analyses.
139 apy were associated with overall survival on univariate and multivariate analyses.
140 hy of prematurity (ROP) were investigated by univariate and multivariate analyses.
141 characteristic (AUROC) curve was assessed in univariate and multivariate analyses.
142 s) were assessed at 3 months follow-up using univariate and multivariate analyses.
143 ong questionnaire data were identified using univariate and multivariate analyses.
144 ential predictors of CR were evaluated using univariate and multivariate analyses.
145 ests and Cox regression models were used for univariate and multivariate analysis to identify predict
146  examined prognostic factors, HRs derived by univariate and multivariate analysis were pooled in sepa
147                                              Univariate and multivariate analysis were used to determ
148 omarker measures in these compartments using univariate and multivariate approaches to correlate dise
149                                          The univariate and multivariate associations between BPE and
150 x proportional hazard model was used to test univariate and multivariate associations with band survi
151 eement for CT features was assessed, as were univariate and multivariate associations with TTP and CL
152                                              Univariate and multivariate chemometric tools, such as a
153 ars) were analyzed as the control group, and univariate and multivariate comparisons were performed t
154                                              Univariate and multivariate Cox models were used to anal
155 method and analyzed using the log rank test, univariate and multivariate Cox models, and propensity s
156 ematogenous metastases were studied by using univariate and multivariate Cox proportional analysis.
157                                           In univariate and multivariate Cox regression analyses, onl
158 for recurrent de novo SCAD were tested using univariate and multivariate Cox regression models.
159 The prognostic analysis was carried out with univariate and multivariate Cox regressions model.
160                                        Using univariate and multivariate fMRI analyses, we provide ev
161                                              Univariate and multivariate functional diversity measure
162  Statistical analysis was performed by using univariate and multivariate generalized linear models to
163 ox regression analyses was used to calculate univariate and multivariate hazard ratios for MS diagnos
164                                       We did univariate and multivariate linear analyses with MoCA ch
165                                              Univariate and multivariate linear mixed models comparin
166                                              Univariate and multivariate linear regression analyses w
167                     Factors were analyzed by univariate and multivariate logistic regression (with en
168                                 We performed univariate and multivariate logistic regression analyses
169                                           In univariate and multivariate logistic regression analyses
170     The DENWIS-indicators were included in a univariate and multivariate logistic regression analysis
171                                              Univariate and multivariate logistic regression was used
172                                              Univariate and multivariate logistic regressions were co
173                                              Univariate and multivariate logistic regressions were pe
174  effect trends could be rationalized through univariate and multivariate parameter analysis involving
175 y is the best predictor of nest sex ratio in univariate and multivariate predictive models.
176                                              Univariate and multivariate regression analyses were use
177 llitus and its mortality were assessed using univariate and multivariate regression.
178                                              Univariate and multivariate regressions were compared fo
179 ccessfully distinguished from non smokers by univariate and multivariate statistical analysis of the
180                                              Univariate and multivariate subgroup analyses were condu
181                                              Univariate and probabilistic sensitivity analyses determ
182                                              Univariate and probabilistic sensitivity analysis and an
183 ith adnexal torsion were identified by using univariate and recursive partitioning multivariate analy
184                                              Univariate and regression model analysis demonstrate tha
185 sts, and PET/CT parameters, were assessed by univariate and subsequent multivariate Cox regression fo
186 ose level, and hemoglobin level) and used in univariate, and multivariable, regression models to pred
187                    By combining behavioural, univariate, and multivariate fMRI measures of how sensor
188                        Spearman correlation, univariate, and multivariate regression models were perf
189 The application of a dual criterion based on univariate (ANOVA) and multivariate analyses (OPLS-DA) a
190  and coastal water level, and we show that a univariate approach may not appropriately characterize t
191 covery of genetic variants over the standard univariate approach.
192                                Compared with univariate associations, the relative weights of NMF sol
193 the excellent results obtained not only with univariate but also with multivariate analysis, shed mor
194 l after initiation of second-line therapy on univariate, but not on multivariate, analysis, where car
195 ed by following Ti emissions at 390.11nm for univariate calibration, and partial least square (PLS) c
196 centage error of 18.7% and 32.8% for PLS and univariate calibration, respectively.
197                                              Univariate comparisons between groups were made with a c
198                                              Univariate composite end points are used for the analysi
199 is lost when they are intermingled to form a univariate composite.
200              Additionally, MVPD outperformed univariate connectivity in its ability to explain indepe
201 omized BSL or TLG42% variable was used for a univariate Cox model, the Akaike information criterion d
202                                            A univariate Cox proportional hazards model was used to co
203 arison of Kaplan-Meier event rate curves and univariate Cox proportional hazards modeling.
204 estimated by using Kaplan-Meier survival and univariate Cox proportional hazards models to examine th
205                                              Univariate Cox proportional-hazards regression analysis
206 s of follow-up were predictive of relapse in univariate Cox regression analysis.
207  be significant predictors of progression on univariate Cox regression analysis.
208 r MDTMs, clinicians, and radiologists, using univariate Cox regression analysis.
209 tatistical significance was reached only for univariate CR analysis in this small group of 9 patients
210                         The phenotype can be univariate disease status, multivariate responses and ev
211 at community organisation is not captured by univariate diversity.
212 ndent variable with the outcome variables, a univariate estimate was performed.
213                                            A univariate filtering algorithm, which selects up to the
214  risk were analysed using Chi-square test or Univariate Fisher's exact test.
215 ed methods for meta-analysis as traditional (univariate fixed- or random-effects pooling or summary r
216                                              Univariate followed by multivariable logistic regression
217 n have you felt tired or had little energy?' Univariate GCTA-GREML found that the proportion of varia
218                                          The univariate hazard ratio for 90-day mortality for dysphag
219                                          The univariate hazard ratio for subsequent detection of HGD
220 on with other clinicopathological factors in univariate [hazard ratio (HR): 3.0, 95% Confidence Inter
221 -by-trial measures of prediction strength to univariate hippocampal responses to the outcomes.
222          In PSP, PSP-Richardson's phenotype (univariate HR 2.53; 95% CI 1.69 to 3.78), early dysphagi
223                                It served for univariate hypothesis testing and multivariate modeling
224                                              Univariate imaging parameter associations were noted for
225 sive to variations in pitch or timbre at the univariate level of analysis were largely overlapping.
226                 The data were analyzed using univariate liability threshold modeling, stratified by s
227 ith RV function and mass was quantified with univariate linear regression.
228  for progression-free survival by stratified univariate log-rank test with Kaplan-Meier curves and by
229                                 We performed univariate logistic regression analyses to assess the as
230 0.07; 95% CI, -0.29 to 0.14; P = .49) nor by univariate logistic regression analysis (odds ratio, 0.6
231 us ocular surgery: risk factors at baseline (univariate logistic regression analysis) included longer
232                                              Univariate logistic regression indicated that the need f
233 encies in geographically matched controls by univariate logistic regression.
234 se heterochronic parameters as phenotypes, a univariate mapping model detected 19 heterochronic quant
235 iated with significantly shorter survival at univariate (median 63 months, IQR 43-139 vs 145 months,
236                                              Univariate meta-regression analyses showed that the majo
237                            The gold-standard univariate method is based on collection of a 24-hr time
238  The optimization step was performed using a univariate methodology for 200mg samples and a multivari
239 ially regulated in multiple biospecimens but univariate methods can be more powerful if compounds are
240                                 Traditional (univariate) methods allow overestimation of diagnostic a
241 s a flexible and transparent means to impute univariate missing data under general missing-not-at-ran
242                                        IP-10 univariate model demonstrated high classification perfor
243 ion size can be accurately approximated by a univariate model governed by three key demographic param
244                                       In the univariate model, mutations of TP53 (11%) and NOTCH1 (4%
245  and HBCDD compared to adherence to TB117 in univariate models (p < 0.05).
246                                           In univariate models, all risk factors except for borderlin
247  information criterion was used to compare 2 univariate models.
248 dels analyzed all variables with P < 0.05 on univariate models.
249 ted with increased rates of TB recurrence in univariate models: interleukin 6 (IL6) (odds ratio [OR]
250  the above outcomes were assessed by using a univariate, multivariable model.
251                                      Initial univariate/multivariate explorations indicated that cirr
252 e expectations have additive behavioural and univariate neural effects because they both improve the
253                                      Neither univariate nor multivariate analysis showed that echinoc
254 pes C and subtypes non-B and non-C in either univariate or multivariate analysis.
255 from various genomic characterizations using univariate or multivariate random forests that includes
256 ients, according to Cox regression analysis (univariate P = .040 and multivariable P = .037).
257 found to be associated with RHF (exploratory univariate P<0.10) were entered into a multivariable log
258 RT to ADT was associated with improved OS on univariate (P < .001) and multivariate analysis (hazard
259  and T3 stage were predictive of recurrence (univariate, P = 0.006), whereas tubular carcinoma type w
260 ighlighted stronger association signals than univariate phenotype analysis at established lipid and o
261                                              Univariate post-stroke lesion-behavior mapping is a part
262 linical and imaging risk factors, which were univariate predictors (age, body mass index, diabetes, L
263                                    Important univariate predictors of outcome (AUC range, 0.66-0.70)
264 02-1.23; P = 0.02) were the only significant univariate predictors of waitlist mortality and remained
265 ogesterone receptor, tumor size, and RS were univariate prognostic factors for disease-free survival;
266                                              Univariate prognostic significance of all evaluated pote
267                                              Univariate quantitative genetic models revealed that all
268 valuated in 3 studies were summarized with a univariate random-effects summary, and findings evaluate
269 arct size and MVO were dichotomized by using univariate receiver operating characteristic techniques,
270                                              Univariate regression analyses showed a significant posi
271                                           In univariate regression analyses, patient age, advanced ca
272 were significantly correlated with VA in the univariate regression analysis (P </= 0.018) but not the
273                                           On univariate regression analysis, the hazard ratios (HRs)
274 ic variants and a trait, which typically use univariate regression to test association between each s
275 ed with 6-month outcome at the 0.05 level in univariate regressions were included in multivariate reg
276 F tests and also to the traditional method - univariate repeated-measurements analysis of variance of
277            Specifically, we observe a strong univariate response in the posterior superior temporal s
278 ons between brain regions has focused on the univariate responses in the regions.
279                                         This univariate retrospective analysis of a single center/org
280 e customized interactive graphics, including univariate scatterplots, box plots, and violin plots, fo
281                                              Univariate sensitivity analyses demonstrated cost-saving
282 ing 22 key parameters) along with a range of univariate sensitivity analyses to evaluate the introduc
283            A combination of multivariate and univariate statistical analyses was employed.
284 tabolomic analysis by LC-MS and multivariate/univariate statistical analysis were used to identify di
285 s reliable results, and it is preferred to a univariate statistical approach.
286                             Multivariate and univariate statistics and pathway analyses were employed
287 were modelled by using both multivariate and univariate statistics by using the clinical metadata to
288 ence intervals, metabolites' VIP values, and univariate statistics.
289                                              Univariate stratified analyses detected a significant pr
290                                              Univariate survival regression on the cumulative inciden
291  mcr-1-negative E coli colonisation (p=0.03, univariate test).
292 titative trait locus (eQTL) effects via mass univariate testing of SNPs against gene expression in si
293                                              Univariate tests and logistic regression analyses were p
294 to genetic association studies is to perform univariate tests between genotype-phenotype pairs.
295                                  Relative to univariate tests, the multivariate procedures detected m
296                                              Univariate then multivariable logistic regression models
297 ion of leaf defenses can be predicted by the univariate trade-off model, which predicts that defenses
298 r gene- and pathway-level association with a univariate trait to the case with GWAS summary statistic
299 d approaches that typically use a mixture of univariate transition kernels.
300 e and complexation time) were optimized in a univariate way.

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