1 Third,
bivariate amyloid and tau PET relationships differed acr
2 s were gathered, detecting associations with
bivariate analyses and constructing a multiple logistic
3 Bivariate analyses and multiple logistic regression mode
4 We then conducted
bivariate analyses and multivariable random forest and l
5 Bivariate analyses compared demographic and treatment va
6 Bivariate analyses examined differences by age group and
7 Bivariate analyses followed by logistic regression model
8 Bivariate analyses found associations among fatty pancre
9 We hypothesized that
bivariate analyses of findings from a meta-analysis of g
10 Bivariate analyses of participant demographics were cond
11 According to
bivariate analyses on 39 patients who did not receive a
12 Bivariate analyses showed RS4-specific associations of t
13 The
bivariate analyses specified CD4 + decline remained grea
14 Bivariate analyses suggested that implementation of all
15 Bivariate analyses using log-linked Poisson regression w
16 Factors associated with death in
bivariate analyses were age <5 years, bleeding at any ti
17 Matched and unmatched (controlling for age)
bivariate analyses were done and risk factors for illnes
18 Descriptive and
bivariate analyses were used to characterize disease and
19 Bivariate analyses were used to establish demographic an
20 Summary statistics,
bivariate analyses, and mixed-effects logistic regressio
21 were analyzed using descriptive statistics,
bivariate analyses, and multivariable regression.
22 Following
bivariate analyses, component factors representing immun
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 ly higher explanatory power than traditional
bivariate analyses.
27 long with NP domains that were identified in
bivariate analyses.
28 he best variable cutoffs was performed using
bivariate analyses.
29 Bivariate analysis also revealed a close association bet
30 Bivariate analysis and multiple logistic regressions wer
31 Initial
bivariate analysis assessed potential associations betwe
32 oss-match result were associated with AMR by
bivariate analysis but neither was an independent predic
33 Bivariate analysis estimated risk ratios for maternal an
34 containing 50% ALA (mothers or pups), while
bivariate analysis indicated a significant association o
35 The
bivariate analysis indicated that being younger than 30
36 Bivariate analysis methods and multivariate generalized
37 It performed a descriptive and
bivariate analysis of the recorded data.
38 Bivariate analysis revealed pooled sensitivity and speci
39 Bivariate analysis revealed positive (harmful) associati
40 Bivariate analysis showed a nonsignificant association b
41 Bivariate analysis showed a significant difference in mo
42 Bivariate analysis showed no significant association bet
43 Bivariate analysis showed that 14% of the total variance
44 Thirdly, we use
bivariate analysis to assess how similar the genetic arc
45 We used
bivariate analysis to compare outcomes between the inter
46 For specific mutations, we performed
bivariate analysis to determine relative risk of baselin
47 All significant variables at
bivariate analysis were entered into a logistic regressi
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 Consistently, by
bivariate analysis, CD49d reliably identified patient su
52 On
bivariate analysis, children with medical errors appeare
53 In
bivariate analysis, Gal-3 and ST2 were independent varia
54 In
bivariate analysis, high safe patient handling behaviors
55 Using
bivariate analysis, highly competitive programs were mor
56 In
bivariate analysis, patients in the control group were m
57 In
bivariate analysis, predictors of better QOL included co
58 In
bivariate analysis, RV LGE presence was independently as
59 In the
bivariate analysis, several demographic factors were sig
60 On
bivariate analysis, the use of oral and topical antibiot
61 d tested for correlations using Spearman Rho
bivariate analysis.
62 for meta-analysis for diagnostic test and a
bivariate analysis.
63 nt was performed using standard approach and
bivariate analysis.
64 r of nurses per bed and doctors per bed in a
bivariate analysis.
65 95% CI [1.08-19.01]) correlated with sPTB on
bivariate analysis.
66 mpared between case patients and controls in
bivariate and adjusted conditional logistic-regression m
67 with survival and neurologic function using
bivariate and generalized estimating equation analyses.
68 her hospitals were estimated with the use of
bivariate and graphical regression methods.
69 Bivariate and logistic regressions were used to identify
70 Bivariate and mixed-effects regression analyses were per
71 body size parameters was investigated using
bivariate and multiple linear regression.
72 Bivariate and multiple logistic or Poisson regression an
73 l and sexual TDV) and "none." Sex-stratified
bivariate and multivariable analyses assessed associatio
74 Bivariate and multivariable analyses revealed several fa
75 Bivariate and multivariable analyses were conducted usin
76 r antibodies against vaccine components, and
bivariate and multivariable analyses were performed to i
77 Descriptive,
bivariate and multivariable analyses were performed.
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 ho reported ever using methamphetamine using
bivariate and multivariable logistic regression.
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 controlling for pati
85 The
bivariate and multivariate analyses were carried out, us
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 determi
89 Bivariate and multivariate analyses were used to identif
90 In both
bivariate and multivariate analyses, age, race/Hispanic
91 ences between groups were investigated using
bivariate and multivariate analyses.
92 ndicators, we examined relationships through
bivariate and multivariate analysis and calculated a com
93 Bivariate and multivariate analysis estimated risk ratio
94 We used
bivariate and multivariate analysis to identify surgeon
95 on between risk factors and mortality in the
bivariate and multivariate analysis, respectively.
96 t-related characteristics was analyzed using
bivariate and multivariate analysis.
97 Bivariate and multivariate comparisons were made, as wel
98 13-18 years) and comparisons were made using
bivariate and multivariate generalized estimating equati
99 Bivariate and multivariate linear regression models esti
100 Bivariate and multivariate logistic regression models an
101 t was the outcome of interest, assessed with
bivariate and multivariate logistic regression models.
102 Bivariate and multivariate logistic regressions were use
103 Bivariate and multivariate models were used to determine
104 use discontinuations were analyzed using Cox
bivariate and multivariate models.
105 tive statistics were calculated, followed by
bivariate and multivariate Poisson regression models to
106 HCV infection and HIV-HCV co-infection using
bivariate and multivariate regression, and estimated HCV
107 ed the data using descriptive statistics and
bivariate and multivariate regressions to examine predic
108 with the total phenolic content (TPC) using
bivariate and multivariate statistical approaches.
109 We used
bivariate and multivariate techniques to assess the rela
110 Bivariate and regression analyses were performed to asse
111 al study applied descriptive (univariate and
bivariate)
and multivariable logistic regression analyse
112 Baseline descriptive,
bivariate,
and concordance analyses were performed.
113 We performed univariate,
bivariate,
and multivariate analyses to identify variabl
114 We used descriptive,
bivariate,
and multivariate statistical methods based on
115 Commonly applied univariate and
bivariate approaches to detecting genetic constraints ca
116 raphic distribution of reporters, along with
bivariate associations among them, restricted analyses w
117 ined the severity of anemia and measured the
bivariate associations between anemia and factors at the
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 The
bivariate attribution analysis demonstrates that forcing
122 e of four bumetanide dose levels by use of a
bivariate Bayesian sequential dose-escalation design to
123 Bivariate (
chi-square tests or the Fisher's exact test)
124 ility analyses were performed using uni- and
bivariate Cholesky decomposition models.
125 e, duration of illness, and DAT dosage using
bivariate comparisons.
126 xation instability was quantified as the 95%
bivariate contour ellipse area (95% BCEA), the best-fit
127 amplitude, the spread of saccade endpoints (
bivariate contour ellipse area), location of saccade lan
128 The mean 1SD-BCEA (
bivariate contour ellipse area), which is the smallest e
129 ated by both linear regression (R(2)WLS) and
bivariate copula (R(2)Copula) models.
130 were analyzed using descriptive statistics,
bivariate correlation analysis and cognitive salience in
131 Logistic regression,
bivariate correlation, and the chi(2) test were used to
132 Bivariate correlation, coefficient of determination, and
133 using multivariate analysis of variance and
bivariate correlation.
134 Here, we examine the
bivariate correlations between leaf economic traits of 5
135 tes) were associated with periodontitis, and
bivariate correlations between responses to these questi
136 In addition,
bivariate correlations of change scores were explored.
137 individual scored in another dimension (with
bivariate correlations ranging from 0.05 to 0.96).
138 Bivariate correlations revealed that for men, higher rat
139 Bivariate correlations showed a positive correlation bet
140 Descriptive statistics and
bivariate correlations were used to examine distributive
141 Based on
bivariate correlations, pain (numeric rating scale), lev
142 BPM and ABPM were close according to Pearson
bivariate correlations.
143 correlation coefficients were measured using
bivariate correlations.
144 Using copulas and
bivariate dependence analysis, we also quantify the incr
145 tivity analysis assessed uncertainties and a
bivariate deterministic sensitivity analysis examined th
146 ned these clinical groups in relation to the
bivariate distribution of amyloid and tau PET values.
147 e was to determine relationships between the
bivariate distribution of amyloid-beta and tau on PET an
148 bias can arise from temporal changes in the
bivariate distribution of education and income.
149 leiotropic model of mutations sampled from a
bivariate distribution of effects of mutations on a quan
150 Bivariate elevated CXCL13 plus IL-10 was 99.3% specific
151 Bivariate fine mapping provided evidence that the indivi
152 Here, we propose a
bivariate flood hazard assessment approach that accounts
153 Bivariate frailty models using both eyes were conducted,
154 used chi(2) tests to examine differences in
bivariate frequencies and used logistic models to examin
155 The algorithm is based on a sequential
bivariate gating approach that generates a set of predef
156 otropic, distributed as vertically elongated
bivariate Gaussians.
157 t analysis (GCTA)-GREML; independent samples
bivariate GCTA-GREML using Generation Scotland for cogni
158 as well as variables that were identified in
bivariate generalized estimating equation models, and ma
159 FOF, attention, and their correlations using
bivariate genetic analysis.
160 Results from a
bivariate genetic model indicated that genetic factors e
161 Through
bivariate genetic modeling, genetic and environmental in
162 We performed a
bivariate genome-based restricted maximum likelihood ana
163 In this study, we conducted a two-stage
bivariate genome-wide association study (BGWAS) of the K
164 e estimated using the following: same-sample
bivariate genome-wide complex trait analysis (GCTA)-GREM
165 We conducted classical univariate and
bivariate genome-wide linkage analysis of TNF production
166 e-out procedure in the current sample), (ii)
bivariate genomic-relationship-matrix restricted maximum
167 ii) a weak negative genetic correlation with
bivariate GREML analyses, but this correlation was not c
168 2,528 autosomal gene expression probes using
bivariate GREML, and tested for differences in autosomal
169 Here, Medina-Gomez and colleagues perform
bivariate GWAS analyses of total body lean mass and bone
170 Then, we performed four
bivariate GWAS analyses.
171 the shared SNP heritability and performed a
bivariate GWAS meta-analysis of total-body lean mass (TB
172 This is the first
bivariate GWAS meta-analysis to demonstrate genetic fact
173 ), largely due to genetic factors in common (
bivariate h2 > 70%).
174 ared to be due to shared genetic influences (
bivariate heritabilities, 0.54-0.71).
175 tested our hypotheses through univariate and
bivariate heritability analyses in a three-generation pe
176 Bivariate heritability analyses provided the first evide
177 cs data that relies on an intensity-weighted
bivariate kernel density estimation on a pooling of all
178 was depicted in an animated display using a
bivariate kernel smoother.
179 s the model fits for different methods using
bivariate lag-distributions of the dihedral/planar angle
180 Using a
bivariate latent change score model, we provide evidence
181 d four other samples (n=20 806) for BMI; and
bivariate LDSC analysis using the largest genome-wide as
182 REML approach and -0.22 (s.e. 0.03) from the
bivariate LDSC analysis.
183 At the
bivariate level, gun carrying was consistently associate
184 omputationally efficient implementation of a
bivariate linear mixed model for settings where hundreds
185 ce and eyelid markers was calculated through
bivariate linear regression analysis, and the associatio
186 In a
bivariate linear regression analysis, distance to primar
187 We used adjusted
bivariate linear regression to examine the relation betw
188 and other case and demographic factors using
bivariate linear regression with random effects modeling
189 lysis conducted in the region underlying the
bivariate linkage peak revealed a variant meeting the co
190 authors used a combination of univariate and
bivariate linkage to investigate pleiotropy between amyg
191 a soil chamber is partitioned according to a
bivariate log-normal probability distribution function (
192 Bivariate logistic regression showed that tattoo/scarifi
193 A
bivariate logistic regression was then performed, which
194 eiver operating characteristic (ROC) and the
bivariate logit-normal (Reitsma) models.
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 We have used genome-wide association in a
bivariate meta-analysis of both traits to identify genes
200 We performed a
bivariate meta-analysis of diagnostic data for an Asperg
201 We did a random-effects
bivariate meta-analysis using a non-linear mixed model a
202 A
bivariate meta-analysis was used to estimate summary sen
203 Twenty-seven studies were assessed by
bivariate meta-analysis.
204 positive rate (FPR) for each signature using
bivariate meta-analysis.
205 receiver operating characteristics curve and
bivariate meta-regression.
206 Questionnaire responses were analyzed using
bivariate methods and multiple logistic regression.
207 works if observations thereof are treated by
bivariate methods.
208 er confidence intervals than do recommended (
bivariate)
methods.
209 falcon is based on a change-point model on a
bivariate mixed Binomial process, which explicitly model
210 A
bivariate mixed-effects binary regression model was used
211 A
bivariate mixed-effects model was applied for pooling th
212 s was performed by using a random-effects or
bivariate mixed-effects regression model depending on th
213 A
bivariate model for diagnostic meta-analysis was used to
214 A
bivariate model of HIV RNA control (P < 0.05) and increa
215 rating characteristic curve) or recommended (
bivariate model or hierarchic summary receiver operating
216 Model 1 was a
bivariate model to determine differences in preventive c
217 analyses were carried out in STATA using the
bivariate model.
218 ate pooling methods were recalculated with a
bivariate model.
219 ombining GM and PCR were estimated using the
bivariate model.
220 were obtained for each parameter by using a
bivariate model.
221 to spectrophotometry using a random-effects
bivariate model.
222 fects meta-analyses were reanalyzed with the
bivariate model; the average change in the summary estim
223 ies or univariate random-effects models when
bivariate models failed to converge.
224 ted odds of subsequent suicidal behaviors in
bivariate models.
225 rceived burden using Fisher's exact test and
bivariate modified Poisson regression.
226 onship between periodontal disease and PH on
bivariate multiple logistic regression analysis.
227 Bivariate multiple logistic regression and adjusted prev
228 Descriptive,
bivariate,
multivariate and Cochran-Armitage trend analy
229 that predictive information, measured using
bivariate mutual information, cannot distinguish between
230 of differential expression with the use of a
bivariate negative binomial distribution for paired desi
231 aracteristic curves, technical cut-offs, 95%
bivariate normal density ellipse prediction, and statist
232 We propose a
bivariate null kernel (BNK) hypothesis testing method, w
233 analyses were performed using non-parametric
bivariate or multivariable logistic regression.
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 Bayesian
bivariate-
outcome hierarchical models were utilized to e
238 n simultaneously present the effect sizes of
bivariate outcomes and their standard errors in a 2-dime
239 d by examining the joint distribution of the
bivariate outcomes.
240 A dynamic random effects
bivariate panel probit model with initial conditions (Wo
241 to estimate the tight bounds on the two-site
bivariate probabilities in each viral sample, and the mu
242 fferences between the 2 groups, we created a
bivariate probit model to estimate the probability of re
243 uting to HSV-2 status in women and men using
bivariate probit.
244 software was used to perform univariate and
bivariate quantitative genetic analyses adjusting for ag
245 bined with a logistic regression model, with
bivariate random effects capturing heterogeneity in rate
246 We used the
bivariate random effects model for quantitative meta-ana
247 For the meta-analysis, a
bivariate random effects model was used to jointly model
248 tive likelihood ratios were calculated using
bivariate random effects models.
249 ic review and a meta-analysis using Bayesian
bivariate random-effects and fixed-effect models to crea
250 Findings were pooled by using
bivariate random-effects and hierarchic summary receiver
251 We calculated predictive values with
bivariate random-effects generalised linear mixed modell
252 Bivariate random-effects meta-analyses were used to calc
253 sessed the accuracy of diagnostic tests with
bivariate random-effects meta-analyses.
254 We performed a
bivariate random-effects meta-analysis of 45 studies, id
255 idual participant data were synthesized with
bivariate random-effects meta-analysis to estimate poole
256 For detection of fever (
bivariate random-effects meta-analysis), sensitivity was
257 in 4 or more studies were summarized with a
bivariate random-effects meta-analysis.
258 A
bivariate random-effects meta-analytic model was impleme
259 culated with the unified model (comprising a
bivariate random-effects model and a hierarchical summar
260 Pooling results from a
bivariate random-effects model gave sensitivity and spec
261 Metaanalysis was performed using a
bivariate random-effects model when at least 5 studies w
262 titatively pooled for all studies by using a
bivariate random-effects model with exploration involvin
263 dies and pooled the accuracy numbers using a
bivariate random-effects model.
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 ties for detecting influenza A from Bayesian
bivariate random-effects models were 54.4% (95% credible
268 individual studies were meta-analyzed using
bivariate random-effects models.
269 95% confidence intervals calculated using a
bivariate random-effects regression model.
270 A series of
bivariate regression analyses were conducted to examine
271 We identify a
bivariate regression model of LCP1 and ADPGK that can ac
272 ar results were obtained with univariate and
bivariate regression models for prediction of water in t
273 Factors with P values of less than 0.20 on
bivariate regression were included in multivariate linea
274 The data were analysed using correlation,
bivariate regression, and multiple regression analysis.
275 Bivariate relations were assessed by Spearman's correlat
276 Across 32 plant species, we found strong
bivariate relationships of both leaf dry matter content
277 pearman correlation (rho) was used to assess
bivariate relationships.
278 Here we introduce the
Bivariate Response to Additive Interacting Doses (BRAID)
279 A
bivariate restricted maximum likelihood estimation metho
280 gy when data are not normally distributed in
bivariate space.
281 The significance of the observed
bivariate spatial associations between the basal area of
282 ably detected directionality (anisotropy) in
bivariate species-environment relationships and identifi
283 g distributed lag non-linear models, using a
bivariate spline to model the exposure-lag-response over
284 Bivariate statistics and multiple correspondence analysi
285 Univariate and
bivariate statistics were used to describe the subtypes.
286 d predictors of parent-reported errors using
bivariate statistics.
287 Bivariate,
stratified, and multivariable analyses were u
288 We analyzed data using mixed-effect
bivariate summary receiver operating characteristic meta
289 arterial stenosis were calculated by using a
bivariate summary receiver operating characteristic or r
290 , building on previous results obtained with
bivariate systems and extending them to multivariate sys
291 ial to be extended to broader fields where a
bivariate test is needed.
292 5,657 children from Bwamanda to construct a
bivariate time-series model that tracks each child's hei
293 Bivariate trait analyses were used to estimate the genet
294 ce component-based heritability analyses and
bivariate trait analyses, we detected significant geneti
295 A standard
bivariate twin additive genetics and unique environment
296 tion in EUE and EOE were established using a
bivariate Twin Model.
297 Bivariate twin modeling confirmed both traits were herit
298 tion model-fitting, including univariate and
bivariate twin models, liability threshold models, DeFri
299 are analyzed using a modified version of the
bivariate von Mises distribution, which is well-known in
300 of analysis and applied the Williamson-York
bivariate weighted least squares estimation to preserve