1 s were gathered, detecting associations with
bivariate analyses and constructing a multiple logistic
2 Bivariate analyses and hierarchical generalized linear m
3 Bivariate analyses and multiple logistic regression mode
4 We then conducted
bivariate analyses and multivariable random forest and l
5 Using
bivariate analyses and multivariate proportional odds lo
6 Bivariate analyses compared demographic and treatment va
7 Bivariate analyses demonstrated an association between h
8 Bivariate analyses examined differences by age group and
9 Bivariate analyses found associations among fatty pancre
10 hs on treatment and performed univariate and
bivariate analyses of PSA, BSI, and survival.
11 According to
bivariate analyses on 39 patients who did not receive a
12 Bivariate analyses showed RS4-specific associations of t
13 Bivariate analyses showed that the incidence of violence
14 Factors associated with death in
bivariate analyses were age <5 years, bleeding at any ti
15 Bivariate analyses were conducted to determine which fac
16 Univariate and
bivariate analyses were conducted with standard methods
17 Matched and unmatched (controlling for age)
bivariate analyses were done and risk factors for illnes
18 Descriptive and
bivariate analyses were performed with survey, blood lea
19 Descriptive and
bivariate analyses were used to characterize disease and
20 Bivariate analyses were used to establish demographic an
21 Summary statistics,
bivariate analyses, and mixed-effects logistic regressio
22 In
bivariate analyses, CS was not associated with increased
23 From
bivariate analyses, the sensitivity and specificity of t
24 of the variables were performed, followed by
bivariate analyses, using the chi(2) test.
25 In
bivariate analyses, we observe significant genetic corre
26 long with NP domains that were identified in
bivariate analyses.
27 he best variable cutoffs was performed using
bivariate analyses.
28 ly higher explanatory power than traditional
bivariate analyses.
29 Bivariate analysis also revealed a close association bet
30 Bivariate analysis and multiple logistic regressions wer
31 oss-match result were associated with AMR by
bivariate analysis but neither was an independent predic
32 Bivariate analysis by using generalized linear modeling
33 In the
bivariate analysis complications were 2.7 times more fre
34 udy and pooled estimates were computed using
bivariate analysis if there was clinical and statistical
35 containing 50% ALA (mothers or pups), while
bivariate analysis indicated a significant association o
36 The
bivariate analysis indicated that being younger than 30
37 Bivariate analysis methods and multivariate generalized
38 Bivariate analysis of duration and severity showed a sig
39 Bivariate analysis of factors associated with receiving
40 Bivariate analysis revealed pooled sensitivity and speci
41 Bivariate analysis revealed positive (harmful) associati
42 Bivariate analysis revealed that both IOP (RhoG = 0.80;
43 Bivariate analysis showed a nonsignificant association b
44 Bivariate analysis showed a significant difference in mo
45 Bivariate analysis showed that 14% of the total variance
46 Thirdly, we use
bivariate analysis to assess how similar the genetic arc
47 We used
bivariate analysis to compare outcomes between the inter
48 In
bivariate analysis, age, diabetes duration, being under
49 n practices were associated with survival on
bivariate analysis, although only 3 were significant aft
50 In
bivariate analysis, among other factors, knowledge of pr
51 ntly predicted young adult depression in the
bivariate analysis, but this effect was entirely account
52 Consistently, by
bivariate analysis, CD49d reliably identified patient su
53 In the
bivariate analysis, change in BSI while adjusting for PS
54 On
bivariate analysis, children with medical errors appeare
55 In
bivariate analysis, Gal-3 and ST2 were independent varia
56 In
bivariate analysis, high safe patient handling behaviors
57 Using
bivariate analysis, highly competitive programs were mor
58 In
bivariate analysis, patients in the control group were m
59 In
bivariate analysis, predictors of better QOL included co
60 In
bivariate analysis, RV LGE presence was independently as
61 In the
bivariate analysis, several demographic factors were sig
62 On
bivariate analysis, the use of oral and topical antibiot
63 Using
bivariate analysis, we estimate a genetic correlation be
64 for meta-analysis for diagnostic test and a
bivariate analysis.
65 nt was performed using standard approach and
bivariate analysis.
66 r of nurses per bed and doctors per bed in a
bivariate analysis.
67 by conventional meta-analytical pooling and
bivariate analysis.
68 d tested for correlations using Spearman Rho
bivariate analysis.
69 mpared between case patients and controls in
bivariate and adjusted conditional logistic-regression m
70 with survival and neurologic function using
bivariate and generalized estimating equation analyses.
71 her hospitals were estimated with the use of
bivariate and graphical regression methods.
72 Bivariate and logistic regressions were used to identify
73 Bivariate and mixed-effects regression analyses were per
74 body size parameters was investigated using
bivariate and multiple linear regression.
75 Bivariate and multiple logistic or Poisson regression an
76 l and sexual TDV) and "none." Sex-stratified
bivariate and multivariable analyses assessed associatio
77 Bivariate and multivariable analyses were conducted usin
78 ngly associated with survival (P < .0001) in
bivariate and multivariable analyses.
79 Bivariate and multivariable competing-risks models were
80 to mass spectrometry, with OS determined by
bivariate and multivariable Cox models.
81 Bivariate and multivariable logistic regression analyses
82 , defined as "FRC use" versus "non-FRC use."
Bivariate and multivariable regression models were perfo
83 spirometric abnormalities were computed, and
bivariate and multivariable regression were used to iden
84 Bivariate and multivariate analyses assessed differences
85 Bivariate and multivariate analyses controlling for pati
86 Bivariate and multivariate analyses were done to find as
87 Descriptive,
bivariate and multivariate analyses were done using odds
88 Bivariate and multivariate analyses were used to identif
89 ences between groups were investigated using
bivariate and multivariate analyses.
90 We used
bivariate and multivariate analysis to identify surgeon
91 on between risk factors and mortality in the
bivariate and multivariate analysis, respectively.
92 t-related characteristics was analyzed using
bivariate and multivariate analysis.
93 Bivariate and multivariate linear regression models esti
94 Bivariate and multivariate logistic regression models an
95 t was the outcome of interest, assessed with
bivariate and multivariate logistic regression models.
96 Bivariate and multivariate logistic regressions were use
97 Bivariate and multivariate models were used to determine
98 use discontinuations were analyzed using Cox
bivariate and multivariate models.
99 tive statistics were calculated, followed by
bivariate and multivariate Poisson regression models to
100 factors associated with year-of-reporting by
bivariate and multivariate regression modeling.
101 lity and impact factor were identified using
bivariate and multivariate regression.
102 Bivariate and multivariate statistical analyses were com
103 with the total phenolic content (TPC) using
bivariate and multivariate statistical approaches.
104 Bivariate and multivariate statistical tools were used t
105 We used
bivariate and multivariate techniques to assess the rela
106 Bivariate and regression analyses were performed to asse
107 al study applied descriptive (univariate and
bivariate)
and multivariable logistic regression analyse
108 Baseline descriptive,
bivariate,
and concordance analyses were performed.
109 We performed univariate,
bivariate,
and multivariate analyses to identify variabl
110 We used descriptive,
bivariate,
and multivariate statistical methods based on
111 Commonly applied univariate and
bivariate approaches to detecting genetic constraints ca
112 ate area was positively correlated with fPRL
bivariate area and the percent time the fPRL was on the
113 Fingertip retinal
bivariate area was positively correlated with fPRL bivar
114 We applied a novel
bivariate association method, which was a joint test of
115 Patients were compared to controls for
bivariate association with minor alleles.
116 ined the severity of anemia and measured the
bivariate associations between anemia and factors at the
117 We observed significant
bivariate associations between delayed OL and variables
118 We assessed
bivariate associations between testing behaviors and pro
119 Significant
bivariate associations emerged for: 1) MSDP/cotinine and
120 Adjusting for sex and age, we found that
bivariate associations of all pairs of diagnoses from wa
121 e of four bumetanide dose levels by use of a
bivariate Bayesian sequential dose-escalation design to
122 use were related to the outcome variables in
bivariate but not multivariate analyses.
123 The primary end point was the
bivariate change from baseline in the serum creatinine l
124 ility analyses were performed using uni- and
bivariate Cholesky decomposition models.
125 DLBCL microenvironment was the best gene in
bivariate combination with LMO2.
126 Bivariate comparisons assessed the associations between
127 ormed with the Student t test for continuous
bivariate comparisons, the Pearson correlation for conti
128 The major outcome measure was a
bivariate construct that represented hot flash frequency
129 We formulate a
bivariate continuous-time Markov process for the numbers
130 xation instability was quantified as the 95%
bivariate contour ellipse area (95% BCEA), the best-fit
131 rom each instrument were used to calculate a
bivariate contour ellipse area (BCEA) that encompassed 6
132 ated by both linear regression (R(2)WLS) and
bivariate copula (R(2)Copula) models.
133 Linear regression and
bivariate correlation analysis were carried out and leve
134 Logistic regression,
bivariate correlation, and the chi(2) test were used to
135 using multivariate analysis of variance and
bivariate correlation.
136 tes) were associated with periodontitis, and
bivariate correlations between responses to these questi
137 Bivariate correlations demonstrated that baseline QA was
138 quality of cardiopulmonary resuscitation and
bivariate correlations elicited factors affecting team-l
139 individual scored in another dimension (with
bivariate correlations ranging from 0.05 to 0.96).
140 Bivariate correlations revealed that for men, higher rat
141 Bivariate correlations showed a positive correlation bet
142 Based on
bivariate correlations, pain (numeric rating scale), lev
143 BPM and ABPM were close according to Pearson
bivariate correlations.
144 BPM and ABPM were close according to Pearson
bivariate correlations.
145 e of a small number of events, 2 independent
bivariate Cox models were tested for PFS.
146 Using copulas and
bivariate dependence analysis, we also quantify the incr
147 bias can arise from temporal changes in the
bivariate distribution of education and income.
148 leiotropic model of mutations sampled from a
bivariate distribution of effects of mutations on a quan
149 Bivariate elevated CXCL13 plus IL-10 was 99.3% specific
150 produce retinal maps showing the scotoma and
bivariate ellipses of fPRL and fingertip retinal positio
151 to pharmacologic therapy with respect to the
bivariate end point of the change in the serum creatinin
152 te regression model based on the significant
bivariate findings, poorer physical function (increased
153 Bivariate fine mapping provided evidence that the indivi
154 Here, we propose a
bivariate flood hazard assessment approach that accounts
155 used chi(2) tests to examine differences in
bivariate frequencies and used logistic models to examin
156 The algorithm is based on a sequential
bivariate gating approach that generates a set of predef
157 otropic, distributed as vertically elongated
bivariate Gaussians.
158 t analysis (GCTA)-GREML; independent samples
bivariate GCTA-GREML using Generation Scotland for cogni
159 as well as variables that were identified in
bivariate generalized estimating equation models, and ma
160 Bivariate genetic analyses showed that, although the gen
161 Bivariate genetic analyses were used to estimate the sha
162 FOF, attention, and their correlations using
bivariate genetic analysis.
163 Results from a
bivariate genetic model indicated that genetic factors e
164 Through
bivariate genetic modeling, genetic and environmental in
165 In
bivariate genetic models based on monozygotic and dizygo
166 In this study, we conducted a two-stage
bivariate genome-wide association study (BGWAS) of the K
167 e estimated using the following: same-sample
bivariate genome-wide complex trait analysis (GCTA)-GREM
168 We conducted classical univariate and
bivariate genome-wide linkage analysis of TNF production
169 e-out procedure in the current sample), (ii)
bivariate genomic-relationship-matrix restricted maximum
170 ii) a weak negative genetic correlation with
bivariate GREML analyses, but this correlation was not c
171 2,528 autosomal gene expression probes using
bivariate GREML, and tested for differences in autosomal
172 Here, Medina-Gomez and colleagues perform
bivariate GWAS analyses of total body lean mass and bone
173 the shared SNP heritability and performed a
bivariate GWAS meta-analysis of total-body lean mass (TB
174 This is the first
bivariate GWAS meta-analysis to demonstrate genetic fact
175 ared to be due to shared genetic influences (
bivariate heritabilities, 0.54-0.71).
176 tested our hypotheses through univariate and
bivariate heritability analyses in a three-generation pe
177 Bivariate heritability analyses provided the first evide
178 A
bivariate heritability model was used to assess the gene
179 cs data that relies on an intensity-weighted
bivariate kernel density estimation on a pooling of all
180 was depicted in an animated display using a
bivariate kernel smoother.
181 s the model fits for different methods using
bivariate lag-distributions of the dihedral/planar angle
182 d four other samples (n=20 806) for BMI; and
bivariate LDSC analysis using the largest genome-wide as
183 REML approach and -0.22 (s.e. 0.03) from the
bivariate LDSC analysis.
184 At the
bivariate level, gun carrying was consistently associate
185 omputationally efficient implementation of a
bivariate linear mixed model for settings where hundreds
186 ce and eyelid markers was calculated through
bivariate linear regression analysis, and the associatio
187 In a
bivariate linear regression analysis, distance to primar
188 We used adjusted
bivariate linear regression to examine the relation betw
189 There was a
bivariate linear relationship between S. mutans levels a
190 ts were utlized along with disease status in
bivariate linkage analysis.
191 lysis conducted in the region underlying the
bivariate linkage peak revealed a variant meeting the co
192 authors used a combination of univariate and
bivariate linkage to investigate pleiotropy between amyg
193 e LOD 3.2, P = 0.0012, and 2.38, P = 0.0087;
bivariate LOD 2.66), and one additional region showed li
194 A
bivariate logistic regression was then performed, which
195 A
bivariate mapping model identified 11 pleiotropic hQTLs
196 demonstrate the implications of thresholded
bivariate measures for network inference.
197 Here, we demonstrate analytically how
bivariate measures relate to the respective multivariate
198 Bivariate meta-analysis demonstrated a significantly hig
199 In random-effects
bivariate meta-analysis of 22 studies, the summary sensi
200 We have used genome-wide association in a
bivariate meta-analysis of both traits to identify genes
201 We performed a
bivariate meta-analysis of diagnostic data for an Asperg
202 We performed a
bivariate meta-analysis of the published literature to c
203 We did a random-effects
bivariate meta-analysis using a non-linear mixed model a
204 receiver operating characteristics curve and
bivariate meta-regression.
205 works if observations thereof are treated by
bivariate methods.
206 er confidence intervals than do recommended (
bivariate)
methods.
207 falcon is based on a change-point model on a
bivariate mixed Binomial process, which explicitly model
208 A
bivariate mixed-effects binary regression model was used
209 Annualized event rates were pooled using a
bivariate mixed-effects binomial regression model to cal
210 A
bivariate mixed-effects model was applied for pooling th
211 A
bivariate model for diagnostic meta-analysis was used to
212 A
bivariate model of HIV RNA control (P < 0.05) and increa
213 rating characteristic curve) or recommended (
bivariate model or hierarchic summary receiver operating
214 Model 1 was a
bivariate model to determine differences in preventive c
215 ounterparts (cumulative hazard rate based on
bivariate model, 26% vs 16%; hazard ratio [HR], 1.8; 95%
216 ombining GM and PCR were estimated using the
bivariate model.
217 analyses were carried out in STATA using the
bivariate model.
218 ate pooling methods were recalculated with a
bivariate model.
219 fects meta-analyses were reanalyzed with the
bivariate model; the average change in the summary estim
220 st and pure-tone audiometry determined using
bivariate modelling.
221 ociation with depression diagnosis claims in
bivariate models and models adjusted for demographic (ag
222 ted odds of subsequent suicidal behaviors in
bivariate models.
223 rceived burden using Fisher's exact test and
bivariate modified Poisson regression.
224 onship between periodontal disease and PH on
bivariate multiple logistic regression analysis.
225 Bivariate multiple logistic regression and adjusted prev
226 Descriptive,
bivariate,
multivariate and Cochran-Armitage trend analy
227 em recommendations, and those derived from a
bivariate/
multivariate analysis of variables associated
228 zed multivariate (complex network measures),
bivariate (
network-based statistic), and univariate (reg
229 with different split criteria and found that
bivariate node-splitting random survival forests with lo
230 with survival outcomes and introduce a novel
bivariate node-splitting random survival forests.
231 aracteristic curves, technical cut-offs, 95%
bivariate normal density ellipse prediction, and statist
232 MGA decreased with increasing PRL
bivariate normal ellipse area, and visual reaction time
233 We propose a
bivariate null kernel (BNK) hypothesis testing method, w
234 imary or secondary immunological outcomes in
bivariate or multivariable models.
235 low-income and middle-income countries using
bivariate or multivariate analysis and published in Engl
236 between ocular symptoms was obtained through
bivariate ordered logistic regression.
237 A dynamic random effects
bivariate panel probit model with initial conditions (Wo
238 A correlational analysis using
bivariate plots and fixed effects linear regression mode
239 Among HIV-infected patients, HSROC/
bivariate pooled sensitivity estimates (highest quality
240 HSROC/
bivariate pooled specificity estimates were low for both
241 to estimate the tight bounds on the two-site
bivariate probabilities in each viral sample, and the mu
242 The
bivariate probit demonstrated significant correlation be
243 A
bivariate probit model estimated the effects of risk whi
244 fferences between the 2 groups, we created a
bivariate probit model to estimate the probability of re
245 he limiting probability distribution for the
bivariate process, conditioned on non-extinction of both
246 Bivariate quantitative genetic analysis between these ey
247 bined with a logistic regression model, with
bivariate random effects capturing heterogeneity in rate
248 We used the
bivariate random effects model for quantitative meta-ana
249 For the meta-analysis, a
bivariate random effects model was used to jointly model
250 tive likelihood ratios were calculated using
bivariate random effects models.
251 operating characteristic (HSROC) curves, and
bivariate random effects models.
252 Findings were pooled by using
bivariate random-effects and hierarchic summary receiver
253 We calculated predictive values with
bivariate random-effects generalised linear mixed modell
254 We performed a
bivariate random-effects meta-analysis of 45 studies, id
255 For detection of fever (
bivariate random-effects meta-analysis), sensitivity was
256 in 4 or more studies were summarized with a
bivariate random-effects meta-analysis.
257 Bivariate random-effects meta-analytic methods were used
258 A
bivariate random-effects meta-analytic model was impleme
259 Bivariate random-effects meta-analytical methods were us
260 culated with the unified model (comprising a
bivariate random-effects model and a hierarchical summar
261 Pooling results from a
bivariate random-effects model gave sensitivity and spec
262 meta-analysis was then performed by using a
bivariate random-effects model to derive estimates of se
263 titatively pooled for all studies by using a
bivariate random-effects model with exploration involvin
264 We did meta-analyses using a
bivariate random-effects model.
265 diagnostic accuracy of various NITs using a
bivariate random-effects model.
266 hood ratios (LRs) were determined by using a
bivariate random-effects model.
267 dies and pooled the accuracy numbers using a
bivariate random-effects model.
268 Bivariate random-effects modeling was used to obtain sum
269 ties for detecting influenza A from Bayesian
bivariate random-effects models were 54.4% (95% credible
270 individual studies were meta-analyzed using
bivariate random-effects models.
271 95% confidence intervals calculated using a
bivariate random-effects regression model.
272 A series of
bivariate regression analyses were conducted to examine
273 quotients, partial correlation analyses, and
bivariate regressions relating brain size to maternal in
274 Bivariate relations were assessed by Spearman's correlat
275 Both
bivariate relationships and multivariate relationships b
276 pearman correlation (rho) was used to assess
bivariate relationships.
277 were found related to alveolar bone loss in
bivariate relationships: age (P < or = 0.0001); smoking
278 Here we introduce the
Bivariate Response to Additive Interacting Doses (BRAID)
279 A
bivariate restricted maximum likelihood estimation metho
280 First, we use
bivariate shrinkage estimator in stationary wavelet doma
281 We also introduce a new
bivariate shrinkage model which shows the relationship o
282 The significance of the observed
bivariate spatial associations between the basal area of
283 ably detected directionality (anisotropy) in
bivariate species-environment relationships and identifi
284 g distributed lag non-linear models, using a
bivariate spline to model the exposure-lag-response over
285 Bivariate statistics and multiple correspondence analysi
286 Univariate and
bivariate statistics were used to describe the subtypes.
287 d predictors of parent-reported errors using
bivariate statistics.
288 Bivariate,
stratified, and multivariable analyses were u
289 We analyzed data using mixed-effect
bivariate summary receiver operating characteristic meta
290 arterial stenosis were calculated by using a
bivariate summary receiver operating characteristic or r
291 , building on previous results obtained with
bivariate systems and extending them to multivariate sys
292 ial to be extended to broader fields where a
bivariate test is needed.
293 5,657 children from Bwamanda to construct a
bivariate time-series model that tracks each child's hei
294 Bivariate trait analyses were used to estimate the genet
295 ce component-based heritability analyses and
bivariate trait analyses, we detected significant geneti
296 A standard
bivariate twin additive genetics and unique environment
297 tion in EUE and EOE were established using a
bivariate Twin Model.
298 Bivariate twin modeling confirmed both traits were herit
299 tion model-fitting, including univariate and
bivariate twin models, liability threshold models, DeFri
300 Bivariate variance components analysis was used to estim