1 ked immunosorbent spot responses (P = 0.042,
multiple regression).
2 y parameters was established initially using
multiple regression.
3 n modeling, Bayesian unified frameworks, and
multiple regression.
4 ned predictive validity using univariate and
multiple regression.
5 ffect of dispersal limitation when tested by
multiple regression.
6 urinary Cd using variable-threshold censored
multiple regression.
7 ns with disease severity were analyzed using
multiple regression.
8 cs by using Spearman techniques and standard
multiple regressions.
9 model for the outcome and covariates in the
multiple regressions.
10 In the
multiple regression adjusted for age, gender, BMI, tobac
11 C) from baseline to 96 weeks, analysed using
multiple regression,
adjusting for baseline normalised b
12 We used
multiple regression analyses (logistic or binominal) to
13 Multiple regression analyses also established a robust n
14 nd treatment characteristics on fatigue with
multiple regression analyses and identified fatigue traj
15 Multiple regression analyses and structural equation mod
16 o the initial infarct location in simple and
multiple regression analyses and using voxel-based lesio
17 Discriminant and
multiple regression analyses based on 2 previously condu
18 Principal component and
multiple regression analyses demonstrated that peak ejec
19 ng in the different cognitive groups whereas
multiple regression analyses explored the association be
20 its and body mass index), were considered in
multiple regression analyses for data analyses (alpha =
21 Multiple regression analyses highlighted associations be
22 Stepwise
multiple regression analyses in mosquito populations fro
23 Multiple regression analyses indicated that scores on th
24 Multiple regression analyses indicated that the extent t
25 Multiple regression analyses indicated that the HIV x pu
26 solved by performing dynamic correlation and
multiple regression analyses of IQGAP1 scaffold mutants.
27 Principal component and
multiple regression analyses of the parameter ensembles
28 neral pattern of results was observed in the
multiple regression analyses of wave 2 prevalent psychia
29 The simple and
multiple regression analyses revealed a significant but
30 Multivariate
multiple regression analyses revealed decreased fraction
31 Multiple regression analyses revealed significant associ
32 Multivariate
multiple regression analyses revealed that LPH variance
33 Multiple regression analyses revealed that mothers' defe
34 Multiple regression analyses showed that the strongest p
35 However, when these were entered into a
multiple regression analyses that controlled for pre-tre
36 st-treatment and follow-up were entered into
multiple regression analyses that controlled for pre-tre
37 1 to 5 years earlier, using univariable and
multiple regression analyses to assess the associations
38 Next, we performed
multiple regression analyses to estimate the predictive
39 We used mixed effects
multiple regression analyses to relate each preoperative
40 Univariate and
multiple regression analyses were applied to identify va
41 Moreover, Spearman's correlation and
multiple regression analyses were carried out.
42 Chi-square tests, ANCOVA, and
multiple regression analyses were conducted.
43 Multiple regression analyses were performed controlling
44 Multiple regression analyses were performed for the asso
45 Spearman rank test and simple and
multiple regression analyses were performed to compare t
46 Spearman correlation and
multiple regression analyses were performed to determine
47 Multiple regression analyses were performed to evaluate
48 Multiple regression analyses were performed to examine t
49 Univariate and
multiple regression analyses were performed to identify
50 Simple and
multiple regression analyses were used to analyze the da
51 Multiple regression analyses were used to evaluate assoc
52 In
multiple regression analyses, bulky lymphadenopathy (>=5
53 Using
multiple regression analyses, we found that brain respon
54 In
multiple regression analyses, we found wider venular dia
55 volume, lacune volume, and brain volume) in
multiple regression analyses.
56 haracteristics using principal component and
multiple regression analyses.
57 were used for between-group comparisons and
multiple regression analyses.
58 n by means of structural equation models and
multiple regression analyses; (2) genetic/environmental
59 Across all sites,
multiple-regression analyses revealed that spongivore ab
60 cal parametric mapping 12-based, voxel-wise,
multiple-regression analyses to detect white matter hype
61 n of interest-based analysis of variance and
multiple-regression analyses.
62 icantly correlated with VA in univariate and
multiple regression analysis (both P < 0.001).
63 Multiple regression analysis (F = 7.51; P < .001) showed
64 egression analysis (P </= 0.018) but not the
multiple regression analysis (P >/= 0.210).
65 s efficacy in depression, and a prespecified
multiple regression analysis (path analysis) to calculat
66 s of interest in the VLSM model, including a
multiple regression analysis adjusted for confounding va
67 In a
multiple regression analysis adjusting for confounders,
68 Stepwise
multiple regression analysis also showed no differences
69 to a second-order polynomial equation using
multiple regression analysis and analyzed by appropriate
70 First, using voxel-wise
multiple regression analysis and controlling for CSF bio
71 bines two complementary tools, namely: (1) a
multiple regression analysis and its generalization, a c
72 With use of
multiple regression analysis and various models, NOx FSR
73 Multiple regression analysis compared prescription data
74 Multiple regression analysis confirmed that higher plasm
75 Further stepwise
multiple regression analysis confirmed the positive asso
76 Multiple regression analysis confirmed this finding (B =
77 By using 4D four-dimensional CT data,
multiple regression analysis demonstrated that TGD troch
78 The use of
multiple regression analysis demonstrates that FAEE cont
79 At
multiple regression analysis for group 1, lesion size an
80 of possible importance were evaluated with a
multiple regression analysis for pretreatment PFTs and w
81 Multiple regression analysis further shows that the incr
82 An explorative stepwise
multiple regression analysis identified 1) post-treatmen
83 Further
multiple regression analysis identified certain pre-extr
84 Multiple regression analysis identified that number of S
85 C-PP was calculated for each sex by a
multiple regression analysis including B-PP, age, height
86 A
multiple regression analysis including data of TLR4 expr
87 Multiple regression analysis indicated that Douglas-fir
88 Multiple regression analysis indicated that infarct size
89 DT concentrations in soils based on stepwise
multiple regression analysis is developed.
90 value of these predictors, identifying that
multiple regression analysis is necessary to understand
91 r analysis of graft and patient survival and
multiple regression analysis of 1-year graft function we
92 In the
multiple regression analysis of 34653 respondents (14564
93 Multiple regression analysis of dose versus root growth
94 Stepwise
multiple regression analysis of semiquantitative data sh
95 In this study, we present a
multiple regression analysis of transcriptomic data in 1
96 Multiple regression analysis performed on the combined e
97 Multiple regression analysis revealed disease duration,
98 Stepwise
multiple regression analysis revealed that a poor visual
99 Multiple regression analysis revealed that being within
100 Multiple regression analysis revealed that latrine cover
101 Also in the main clinical trial,
multiple regression analysis revealed that SF + D best p
102 Multiple regression analysis revealed that the degree of
103 Furthermore,
multiple regression analysis revealed that the interacti
104 Multiple regression analysis revealed that the intergrou
105 Multiple regression analysis revealed that, controlling
106 Multiple regression analysis revealed TIP3 to be associa
107 Multiple regression analysis showed 4 Health Belief Mode
108 Multiple regression analysis showed a high correlation b
109 Multiple regression analysis showed corneal hysteresis t
110 Multiple regression analysis showed no relationship with
111 Multiple regression analysis showed that African America
112 The
multiple regression analysis showed that better self-rat
113 Multiple regression analysis showed that Cr and Ni were
114 The
multiple regression analysis showed that glucose influen
115 Multiple regression analysis showed that PDT type was no
116 Univariate and
multiple regression analysis showed that the area of the
117 Multiple regression analysis showed that the timing of f
118 Multiple regression analysis shows that low asthma quali
119 Multiple regression analysis shows that the likelihood o
120 We used
multiple regression analysis to estimate predictors of p
121 Multiple regression analysis was performed to assess the
122 Multiple regression analysis was performed to determine
123 Multiple regression analysis was performed, and statisti
124 Multiple regression analysis was used to assess associat
125 Multiple regression analysis was used to compare changes
126 Linear
multiple regression analysis was used to create models f
127 Multiple regression analysis was used to create predicti
128 Multiple regression analysis was used to determine if AB
129 Multiple regression analysis was used to determine the a
130 Multiple regression analysis was used to evaluate correl
131 Multiple regression analysis was used to evaluate the re
132 Multiple regression analysis was used to examine MR imag
133 Multiple regression analysis was used to test prediction
134 Univariate and
multiple regression analysis were used to examine the as
135 between the patient and control groups using
multiple regression analysis while adjusting for age and
136 After
multiple regression analysis with adjustment for age, bo
137 The parameters were correlated at simple and
multiple regression analysis with the expression of the
138 ty, predictors of higher titers of antibody (
multiple regression analysis), and cutoff values of meas
139 With
multiple regression analysis, a forward selection proced
140 Multiple regression analysis, and an artificial neutral
141 tcome limited the number of variables in the
multiple regression analysis, and whether nonsignificant
142 Using
multiple regression analysis, BAP1 mutations were associ
143 On
multiple regression analysis, choroidal thickness, age,
144 olvent effect was fitted satisfactorily with
multiple regression analysis, correlating the obtained s
145 Results At
multiple regression analysis, fibrosis was the only vari
146 In a
multiple regression analysis, GDF-15 (growth and differe
147 At
multiple regression analysis, HEF was the only parameter
148 By
multiple regression analysis, patient BMI remained indep
149 In
multiple regression analysis, PKP (vs DALK) (odds ratio
150 In
multiple regression analysis, predictors of mortality in
151 Through stepwise
multiple regression analysis, Q(peak), RBCV and Hb(mass)
152 In
multiple regression analysis, the association of a treat
153 First, using voxel-wise
multiple regression analysis, we identified the metaboli
154 ratio, 0.20; 95% CI, 0.06-0.73; P=0.015) by
multiple regression analysis, while age and valve type d
155 d order polynomial model was developed using
multiple regression analysis.
156 ude of IOP reduction were investigated using
multiple regression analysis.
157 for Mn and Fe, respectively) as indicated by
multiple regression analysis.
158 entilation and preserved sensory function by
multiple regression analysis.
159 tary intake variables were achieved by using
multiple regression analysis.
160 enrichment factor (EF), in the conventional
multiple regression analysis.
161 r = -0.282, P = .257), as confirmed by using
multiple regression analysis.
162 and lesion characteristics was explored with
multiple regression analysis.
163 Associations were estimated using
multiple regression analysis.
164 Data was analysed using
multiple regression analysis.
165 stigated by using a general linear model and
multiple regression analysis.
166 using correlation, bivariate regression, and
multiple regression analysis.
167 nt with the existence of suppressor effects,
multiple-regression analysis found amygdala responses to
168 A
multiple-regression analysis led to a final model explai
169 is nonlinear relationship analytically using
multiple regression and apply it to data on piglet birth
170 For association analysis, we used
multiple regression and found that the FEV1/FVC ratio de
171 We used
multiple regression and propensity score matching to est
172 patient, and treatment characteristics using
multiple regression and repeated measures analysis and c
173 Results from
multiple regressions and multivariate canonical correlat
174 aging to quantify SM (dependent variable for
multiple regressions)
and anthropometric variables (inde
175 predicted from this cortical decoupling with
multiple regressions,
and the reduction of synchronizati
176 In
multiple regression,
antibacterial prophylaxis reduced a
177 Using a
multiple regression approach that is physically motivate
178 first study to use a principal component and
multiple regression approach to understand how lake chem
179 tory functional connectivity and whole brain
multiple regression approaches were used to analyze how
180 nefits of these methods compared to standard
multiple regression are described.
181 enotypes show that the results of HAPRAP and
multiple regression are highly consistent.
182 4.8% of physical memory as compared to other
multiple regression-
based PGS methods.
183 Urate levels were compared by
multiple regression between subjects with and without a
184 Multiple regression equations showed that only the numbe
185 ANOVA and
multiple regression equations were used in the analysis.
186 based method incorporating cross-validation,
multiple regression,
grid search, and bisection algorith
187 FS for both patients with BRCA-mutant HGSOC (
multiple regression:
hazard ratio [HR] = 26.7 P < .001)
188 In
multiple regression,
including anthropometric and metabo
189 e concentrations and behavior using adjusted
multiple regression interaction models.
190 ller than the number of markers, a penalized
multiple regression method can be adopted by fitting all
191 Multiple regression methods were used to examine the ind
192 h increased risk for final VA <20/200 in the
multiple regression model (OR, 4.35; P = 0.011).
193 The best
multiple regression model achieved, using all the potent
194 Our
multiple regression model described 40% of the variance
195 Our regularized
multiple regression model had a high level of predictive
196 A
multiple regression model including age and precipitatio
197 In a
multiple regression model including regional measures of
198 Here a
multiple regression model is developed for the first tim
199 ysis, a simple (from a clinical perspective)
multiple regression model preanalyzing infarct size and
200 A
multiple regression model showed that the combination of
201 y, we combined flow-cytometry variables in a
multiple regression model that identified individuals wi
202 In a
multiple regression model that included age, sex, and as
203 uction of PCV13 and used a negative binomial
multiple regression model to estimate how much of the ch
204 predicting the occurrence of LV thrombus, a
multiple regression model was applied.
205 ntly with visual gain after treatment in the
multiple regression model with adjustment for CRT and el
206 VA, NVA, and CS with glare testing (P < .05,
multiple regression model).
207 d in the lowest Akaike information criterion
multiple regression model, explaining 59% of S(spp) .
208 In a
multiple regression model, MTX area under the concentrat
209 In a
multiple regression model, the G allele was associated d
210 Using a
multiple regression model, we show that the combination
211 were analyzed with Pearson correlation in a
multiple regression model.
212 ach were compared with those from individual
multiple regression model.
213 lculate Pearson correlation coefficients and
multiple regression model.
214 the dependent variable in each hierarchical
multiple regression model.
215 Only rintSO2 remained significant in the
multiple regression model.
216 nalyzed by means of an age- and sex-adjusted
multiple regression model.
217 )remained statistically significant in a Cox
multiple regression model.
218 Multiple regression modeling identified calprotectin and
219 Multiple regression modeling identified increasing age (
220 Multiple regression models (standardized regression coef
221 In
multiple regression models adjusted for several potentia
222 Among 971 Singaporean women,
multiple regression models and area under receiver-opera
223 h diameter and cone size were analyzed using
multiple regression models and evolutionary models of tr
224 dicting lower limb sagittal kinematics using
multiple regression models based on walking speed, gende
225 Multiple regression models confirmed that the transfusio
226 Adjusted
multiple regression models demonstrated that resting hea
227 Multiple regression models including all confounders ind
228 ttention and a reading test were analyzed in
multiple regression models including all SNPs, SNP-sex i
229 We evaluated the utility of the DHIs using
multiple regression models predicting moose abundance by
230 teristics between high and low learners, and
multiple regression models to assess the respective impa
231 index of insulin resistance were included in
multiple regression models to examine the independent as
232 Multiple regression models to predict severity were gene
233 Multiple regression models used cognitive change to pred
234 Variables selected in
multiple regression models used to explore environmental
235 Multiple regression models were also established to pred
236 Multiple regression models were conducted in 2011 to est
237 Multiple regression models were fitted to estimate genet
238 Multiple regression models were proposed and results sho
239 Multivariate analyses and
multiple regression models were used to assess the diffe
240 Multiple regression models were used to examine the vari
241 Multivariate
multiple regression models were used.
242 Multiple regression models were utilized to identify pat
243 ially independent and were used to construct
multiple regression models which explain about half of t
244 iable analysis (meta-regression and weighted
multiple regression models) demonstrated that the person
245 rontal, temporal, and parietal regions using
multiple regression models, adjusting for sociodemograph
246 In
multiple regression models, persons with BSA at or above
247 In
multiple regression models, socioenvironmental determina
248 When analyzed in
multiple regression models, the decrease in both length
249 Using
multiple regression models, we examined the factors that
250 oncentrations computed using sample-weighted
multiple regression models.
251 Confounding variables were included in
multiple regression models.
252 r(2) values as compared with those from the
multiple regression models.
253 s, and GrimAge acceleration were tested with
multiple regression models.
254 nd brain MRI measures were investigated with
multiple regression models.
255 as associated with psychomotor scores in the
multiple regression models.
256 ss intervention effects using chi-square and
multiple regression models.
257 e and function and individual weight gain in
multiple regression models.
258 ive and emotional outcome were calculated in
multiple regression models.
259 ssociated with the total ASQ-3 scores in the
multiple regression models.
260 symptoms suggesting ACS, using hierarchical
multiple regression of elapsed time.
261 t image array to evaluate each CT image with
multiple regression of gene expression analysis.
262 Propensity score-based methods or
multiple regressions of the outcome are often used for c
263 ait prediction problem from a novel angle: a
multiple regression on categorical data problem, which r
264 right and left ventricle (r=0.47; P<0.0001;
multiple regression P=0.0025).
265 s positively associated with glucose levels (
multiple regression,
P = 0.019) and white blood cell cou
266 plete cytoreduction in BRCA wild-type HGSOC (
multiple regression:
P < .001 each CT feature).
267 T were associated with BRCA mutation status (
multiple regression:
P < .001 for each CT feature).
268 Penalized
Multiple Regression (
PMR) can be used to discover novel
269 By
multiple regression,
pre- and posttransfusion hepcidin c
270 Using
multiple regression representational similarity analysis
271 Standard methods (e.g.
multiple regression)
require individual level genotypes.
272 Multiple regression revealed that age (beta = -0.535; P
273 Multiple regression revealed that whilst no socio-demogr
274 A
multiple regression showed that Hg correlated with delta
275 ther enhancer- or promoter-driven logic, and
multiple regression that connects genes to the enhancers
276 sources of variation with a novel method of
multiple regression that is useful for understanding non
277 IEQ per kilogram body weight (IEQ/kg), using
multiple regression to adjust for key disease and patien
278 ysed using Principal Components Analysis and
Multiple Regression to establish correlations with the d
279 We used
multiple regression to estimate least squares geometric
280 s incurred above the facility level and used
multiple regression to estimate variation in these costs
281 We used
multiple regression to examine effects of sex and COMT g
282 We used
multiple regression to explore the relation between a co
283 infants born between 1972 and 2011, we used
multiple regression to test whether parental season of b
284 Next, we used
multiple regressions to identify the models that most ac
285 our study, we used variation partitioning in
multiple regressions to quantify cross-taxon congruence
286 on these reactions was analyzed by means of
multiple regression using the Fujita steric constant Es
287 Standard
multiple regression was performed to identify predictors
288 Multiple regression was used in a whole-brain analysis w
289 Multiple regression was used to assess associations betw
290 alues between groups were compared, stepwise
multiple regression was used to assess if any Zernike te
291 Multiple regression was used to assess the interactive e
292 Stepwise
multiple regression was used to determine what factors c
293 Using
multiple regression,
we assessed the effect of secondary
294 onparametric Wilcoxon-Mann-Whitney tests and
multiple regression were used for statistical analysis.
295 Multiple regressions were applied to examine the associa
296 Multiple regressions were simpler for [M]tiss than [M]to
297 Correlations and
multiple regressions were used to determine whether pare
298 Multiple regressions were used to investigate the relati
299 icant for methane concentrations (P = 0.007;
multiple regression),
whereas distances to valley bottom
300 sponses to environment were quantified using
multiple regression;
within- and between-species respons