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.