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1 ked immunosorbent spot responses (P = 0.042, multiple regression).
2 ned predictive validity using univariate and multiple regression.
3 ffect of dispersal limitation when tested by multiple regression.
4 urinary Cd using variable-threshold censored multiple regression.
5 n of their genomic landscape, using standard multiple regression.
6 HR-HPV acquisition were estimated by Poisson multiple regression.
7 y parameters was established initially using multiple regression.
8 n modeling, Bayesian unified frameworks, and multiple regression.
9 cs by using Spearman techniques and standard multiple regressions.
10                                       In the multiple regression adjusted for age, gender, BMI, tobac
11                                              Multiple regression, adjusted for brachial BP, showed HR
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                      Principal component and multiple regression analyses of the parameter ensembles
25 neral pattern of results was observed in the multiple regression analyses of wave 2 prevalent psychia
26                                 Multivariate multiple regression analyses revealed decreased fraction
27                                              Multiple regression analyses revealed significant associ
28                                 Multivariate multiple regression analyses revealed that LPH variance
29                                              Multiple regression analyses revealed that mothers' defe
30                                              Multiple regression analyses showed that the strongest p
31 sialoadhesin (Siglec-1) to COMP and TSP-1 in multiple regression analyses significantly improved best
32      However, when these were entered into a multiple regression analyses that controlled for pre-tre
33 st-treatment and follow-up were entered into multiple regression analyses that controlled for pre-tre
34                           Next, we performed multiple regression analyses to estimate the predictive
35 l Health Systems (WHO-AIMS) were included in multiple regression analyses to investigate the role of
36  levels were correlated with the MRSS, using multiple regression analyses to obtain best-fit models.
37                        We used mixed effects multiple regression analyses to relate each preoperative
38                               Univariate and multiple regression analyses were applied to identify va
39         Moreover, Spearman's correlation and multiple regression analyses were carried out.
40                Chi-square tests, ANCOVA, and multiple regression analyses were conducted.
41                                              Multiple regression analyses were performed controlling
42                                              Multiple regression analyses were performed for the asso
43            Spearman rank test and simple and multiple regression analyses were performed to compare t
44                     Spearman correlation and multiple regression analyses were performed to determine
45                                              Multiple regression analyses were performed to evaluate
46                                              Multiple regression analyses were performed to examine t
47                               Univariate and multiple regression analyses were performed to investiga
48                                   Simple and multiple regression analyses were used to analyze the da
49                                           In multiple regression analyses, we found wider venular dia
50  were used for between-group comparisons and multiple regression analyses.
51 haracteristics using principal component and multiple regression analyses.
52  volume, lacune volume, and brain volume) in multiple regression analyses.
53 n by means of structural equation models and multiple regression analyses; (2) genetic/environmental
54                            Across all sites, multiple-regression analyses revealed that spongivore ab
55 n of interest-based analysis of variance and multiple-regression analyses.
56 icantly correlated with VA in univariate and multiple regression analysis (both P < 0.001).
57                                              Multiple regression analysis (F = 7.51; P < .001) showed
58 egression analysis (P </= 0.018) but not the multiple regression analysis (P >/= 0.210).
59 s efficacy in depression, and a prespecified multiple regression analysis (path analysis) to calculat
60 tios was modeled using a single multivariate multiple regression analysis adjusted for age and curren
61 s of interest in the VLSM model, including a multiple regression analysis adjusted for confounding va
62                                         In a multiple regression analysis adjusting for confounders,
63 associated with advanced hepatic fibrosis on multiple regression analysis after adjustments for age,
64                                     Stepwise multiple regression analysis also showed no differences
65  to a second-order polynomial equation using multiple regression analysis and analyzed by appropriate
66 and demographic variables were examined with multiple regression analysis and multilevel modelling.
67  relationship among measures was assessed by multiple regression analysis and structural equation mod
68                                      In both multiple regression analysis and structural equation mod
69                                  With use of multiple regression analysis and various models, NOx FSR
70                                              Multiple regression analysis confirmed that higher plasm
71                                              Multiple regression analysis confirmed this finding (B =
72        By using 4D four-dimensional CT data, multiple regression analysis demonstrated that TGD troch
73                                   The use of multiple regression analysis demonstrates that FAEE cont
74                                           At multiple regression analysis for group 1, lesion size an
75 of possible importance were evaluated with a multiple regression analysis for pretreatment PFTs and w
76                                              Multiple regression analysis further shows that the incr
77                      An explorative stepwise multiple regression analysis identified 1) post-treatmen
78                                      Further multiple regression analysis identified certain pre-extr
79                                              Multiple regression analysis identified that number of S
80                                              Multiple regression analysis in the D2 mice revealed an
81        C-PP was calculated for each sex by a multiple regression analysis including B-PP, age, height
82                                            A multiple regression analysis including data of TLR4 expr
83                                              Multiple regression analysis including limbic (hippocamp
84                                              Multiple regression analysis indicated that Douglas-fir
85                                              Multiple regression analysis indicated that infarct size
86                                              Multiple regression analysis indicated that the inverse
87 DT concentrations in soils based on stepwise multiple regression analysis is developed.
88  value of these predictors, identifying that multiple regression analysis is necessary to understand
89 r analysis of graft and patient survival and multiple regression analysis of 1-year graft function we
90                                       In the multiple regression analysis of 34653 respondents (14564
91                                              Multiple regression analysis of dose versus root growth
92                                     Stepwise multiple regression analysis of semiquantitative data sh
93                                              Multiple regression analysis of the data showed that, al
94                  In this study, we present a multiple regression analysis of transcriptomic data in 1
95                                              Multiple regression analysis performed on the combined e
96                                   Univariate multiple regression analysis revealed a common, domain-i
97                                              Multiple regression analysis revealed CS was important f
98                                              Multiple regression analysis revealed disease duration,
99                                     Stepwise multiple regression analysis revealed that a poor visual
100                                              Multiple regression analysis revealed that being within
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                                              Multiple regression analysis revealed that the intergrou
104                                              Multiple regression analysis revealed that, controlling
105                                   Finally, a multiple regression analysis reveals bilateral preSMA-ST
106                                              Multiple regression analysis showed 4 Health Belief Mode
107                                              Multiple regression analysis showed a high correlation b
108                                              Multiple regression analysis showed corneal hysteresis t
109                                              Multiple regression analysis showed that 9 of 35 BMI-ass
110                                              Multiple regression analysis showed that African America
111                                              Multiple regression analysis showed that Cr and Ni were
112                                              Multiple regression analysis showed that lower age, high
113                                              Multiple regression analysis showed that PDT type was no
114                               Univariate and multiple regression analysis showed that the area of the
115                                              Multiple regression analysis showed that the timing of f
116                                              Multiple regression analysis shows that low asthma quali
117                                              Multiple regression analysis shows that the likelihood o
118                                              Multiple regression analysis suggested that lower suPAR
119                       In this study, we used multiple regression analysis to estimate the pathogenici
120                          Furthermore, linear multiple regression analysis using SI_INS mRNA and SI_16
121                                              Multiple regression analysis was performed to assess the
122                                              Multiple regression analysis was performed, and statisti
123                                         When multiple regression analysis was performed, the extent o
124                                              Multiple regression analysis was used to assess associat
125                                              Multiple regression analysis was used to compare changes
126                                              Multiple regression analysis was used to determine if AB
127                                              Multiple regression analysis was used to determine the a
128                                              Multiple regression analysis was used to determine wheth
129                                              Multiple regression analysis was used to evaluate correl
130                                              Multiple regression analysis was used to examine MR imag
131                                              Multiple regression analysis was used to identify brain
132                                              Multiple regression analysis was used to test prediction
133                               Univariate and multiple regression analysis were performed.
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 clerosis, and diabetes were then assessed by multiple regression analysis with backward elimination.
138                        The authors performed multiple regression analysis with MPOD as the dependent
139 The parameters were correlated at simple and multiple regression analysis with the expression of the
140 ty, predictors of higher titers of antibody (multiple regression analysis), and cutoff values of meas
141                                           On multiple regression analysis, adipose IR index and postp
142 tcome limited the number of variables in the multiple regression analysis, and whether nonsignificant
143                                        Using multiple regression analysis, BAP1 mutations were associ
144                                           On multiple regression analysis, choroidal thickness, age,
145 olvent effect was fitted satisfactorily with multiple regression analysis, correlating the obtained s
146                                           In multiple regression analysis, duration of corticosteroid
147                                   Results At multiple regression analysis, fibrosis was the only vari
148                                           At multiple regression analysis, HEF was the only parameter
149                                           On multiple regression analysis, male gender and not having
150                            When subjected to multiple regression analysis, only fat mass was predicti
151                                           In multiple regression analysis, patients with no response
152                                           In multiple regression analysis, predictors of mortality in
153                             Through stepwise multiple regression analysis, Q(peak), RBCV and Hb(mass)
154                                           On multiple regression analysis, SF >1.5 x ULN was independ
155                                           In multiple regression analysis, the association of a treat
156 nd positive lymph nodes and after conducting multiple regression analysis, the hazard ratio for chemo
157                              On the basis of multiple regression analysis, urinary alpha-CEHC excreti
158  enrichment factor (EF), in the conventional multiple regression analysis.
159 r = -0.282, P = .257), as confirmed by using multiple regression analysis.
160 and lesion characteristics was explored with multiple regression analysis.
161            Associations were estimated using multiple regression analysis.
162                      Data was analysed using multiple regression analysis.
163 stigated by using a general linear model and multiple regression analysis.
164 rowth or fat mass in either cohort following multiple regression analysis.
165  volume (V(S)), were evaluated with stepwise multiple regression analysis.
166  using multivariate analysis of variance and multiple regression analysis.
167 ry and meal attributes was examined by using multiple regression analysis.
168 d order polynomial model was developed using multiple regression analysis.
169 ude of IOP reduction were investigated using multiple regression analysis.
170 for Mn and Fe, respectively) as indicated by multiple regression analysis.
171 entilation and preserved sensory function by multiple regression analysis.
172 tary intake variables were achieved by using multiple regression analysis.
173 nt with the existence of suppressor effects, multiple-regression analysis found amygdala responses to
174                                            A multiple-regression analysis led to a final model explai
175 is nonlinear relationship analytically using multiple regression and apply it to data on piglet birth
176 Data were analysed using simple descriptive, multiple regression and complex multi-level modelling te
177            For association analysis, we used multiple regression and found that the FEV1/FVC ratio de
178                                      We used multiple regression and propensity score matching to est
179                                 Results from multiple regressions and multivariate canonical correlat
180 aging to quantify SM (dependent variable for multiple regressions) and anthropometric variables (inde
181 predicted from this cortical decoupling with multiple regressions, and the reduction of synchronizati
182                                           In multiple regression, antibacterial prophylaxis reduced a
183       Our methodology is the first penalized multiple regression approach that explicitly controls Ty
184                                      Using a multiple regression approach that is physically motivate
185 first study to use a principal component and multiple regression approach to understand how lake chem
186 tory functional connectivity and whole brain multiple regression approaches were used to analyze how
187 nefits of these methods compared to standard multiple regression are described.
188 enotypes show that the results of HAPRAP and multiple regression are highly consistent.
189 ed at month 1 posttransplant using validated multiple regression-derived limited sampling strategies.
190                                              Multiple regression equations showed that only the numbe
191                                    ANOVA and multiple regression equations were used in the analysis.
192                                Here, using a multiple regression framework, we investigate primate Al
193 FS for both patients with BRCA-mutant HGSOC (multiple regression: hazard ratio [HR] = 26.7 P < .001)
194                                           In multiple regression, including anthropometric and metabo
195 e concentrations and behavior using adjusted multiple regression interaction models.
196                      Although many penalized multiple regression methodologies have been proposed to
197                                              Multiple regression methods were used to examine the ind
198 h increased risk for final VA <20/200 in the multiple regression model (OR, 4.35; P = 0.011).
199                                     The best multiple regression model achieved, using all the potent
200                                          Our multiple regression model described 40% of the variance
201                              Our regularized multiple regression model had a high level of predictive
202                                            A multiple regression model including age and precipitatio
203                                       Here a multiple regression model is developed for the first tim
204 ysis, a simple (from a clinical perspective) multiple regression model preanalyzing infarct size and
205                                   A stepwise multiple regression model showed that AHI was independen
206                                            A multiple regression model showed that the combination of
207 y, we combined flow-cytometry variables in a multiple regression model that identified individuals wi
208                                         In a multiple regression model that included age, sex, and as
209                                    We used a multiple regression model to analyze data from 227 inten
210 uction of PCV13 and used a negative binomial multiple regression model to estimate how much of the ch
211 ngland Region) is an empirical least-squares multiple regression model using mercury (Hg) deposition
212  predicting the occurrence of LV thrombus, a multiple regression model was applied.
213                                            A multiple regression model was used to examine difference
214 VA, NVA, and CS with glare testing (P < .05, multiple regression model).
215                                       In the multiple regression model, age, smoking, race, gender, a
216                                         In a multiple regression model, MTX area under the concentrat
217                                         In a multiple regression model, the G allele was associated d
218 ach were compared with those from individual multiple regression model.
219 lculate Pearson correlation coefficients and multiple regression model.
220  the dependent variable in each hierarchical multiple regression model.
221 )remained statistically significant in a Cox multiple regression model.
222  were analyzed with Pearson correlation in a multiple regression model.
223                                            A multiple-regression model was used to study the associat
224                                              Multiple regression modeling identified calprotectin and
225                                              Multiple regression modeling identified increasing age (
226                                              Multiple regression models (standardized regression coef
227                                           In multiple regression models adjusted for several potentia
228 h diameter and cone size were analyzed using multiple regression models and evolutionary models of tr
229                                              Multiple regression models as a function of tertile grou
230                                              Multiple regression models confirmed that the transfusio
231                                              Multiple regression models controlled for age, sex, and
232                                     Adjusted multiple regression models demonstrated that resting hea
233                  Statistical models included multiple regression models for dementia and cognition an
234                                              Multiple regression models including all confounders ind
235 ttention and a reading test were analyzed in multiple regression models including all SNPs, SNP-sex i
236 pendent variable in gender-specific adjusted multiple regression models stratified by year 7 BMI.
237                                              Multiple regression models to predict severity were gene
238                                              Multiple regression models used cognitive change to pred
239                        Variables selected in multiple regression models used to explore environmental
240             Finally, we demonstrate that the multiple regression models we employed provide high leve
241                                              Multiple regression models were also established to pred
242                                 Hierarchical multiple regression models were applied to assess the ef
243                                              Multiple regression models were conducted in 2011 to est
244                                              Multiple regression models were fitted to estimate genet
245                                              Multiple regression models were proposed and results sho
246                    Multivariate analyses and multiple regression models were used to assess the diffe
247                                 Multivariate multiple regression models were used.
248 ially independent and were used to construct multiple regression models which explain about half of t
249 iable analysis (meta-regression and weighted multiple regression models) demonstrated that the person
250                                           In multiple regression models, persons with BSA at or above
251                                           In multiple regression models, socioenvironmental determina
252                             When analyzed in multiple regression models, the decrease in both length
253                                           In multiple regression models, the interaction between race
254                                        Using multiple regression models, we examined the factors that
255       Confounding variables were included in multiple regression models.
256  r(2) values as compared with those from the multiple regression models.
257 as associated with psychomotor scores in the multiple regression models.
258 ssociated with the total ASQ-3 scores in the multiple regression models.
259 ss intervention effects using chi-square and multiple regression models.
260  processing (R(2)=0.03) and UPSA score using multiple regression models.
261 ive and emotional outcome were calculated in multiple regression models.
262 oncentrations computed using sample-weighted multiple regression models.
263  symptoms suggesting ACS, using hierarchical multiple regression of elapsed time.
264 t image array to evaluate each CT image with multiple regression of gene expression analysis.
265 ait prediction problem from a novel angle: a multiple regression on categorical data problem, which r
266  right and left ventricle (r=0.47; P<0.0001; multiple regression P=0.0025).
267 s positively associated with glucose levels (multiple regression, P = 0.019) and white blood cell cou
268 plete cytoreduction in BRCA wild-type HGSOC (multiple regression: P < .001 each CT feature).
269 T were associated with BRCA mutation status (multiple regression: P < .001 for each CT feature).
270                                    Penalized Multiple Regression (PMR) can be used to discover novel
271                                           By multiple regression, pre- and posttransfusion hepcidin c
272                       Standard methods (e.g. multiple regression) require individual level genotypes.
273                                              Multiple regression revealed that age (beta = -0.535; P
274                                              Multiple regression revealed that whilst no socio-demogr
275                                            A multiple regression showed that Hg correlated with delta
276  sources of variation with a novel method of multiple regression that is useful for understanding non
277 ysed using Principal Components Analysis and Multiple Regression to establish correlations with the d
278 s incurred above the facility level and used multiple regression to estimate variation in these costs
279                                      We used multiple regression to examine effects of sex and COMT g
280                                      We used multiple regression to explore the relation between a co
281  infants born between 1972 and 2011, we used multiple regression to test whether parental season of b
282                                Next, we used multiple regressions to identify the models that most ac
283 our study, we used variation partitioning in multiple regressions to quantify cross-taxon congruence
284  on these reactions was analyzed by means of multiple regression using the Fujita steric constant Es
285                                              Multiple regression was used in a whole-brain analysis w
286                                              Multiple regression was used to assess associations betw
287 alues between groups were compared, stepwise multiple regression was used to assess if any Zernike te
288                                              Multiple regression was used to assess the interactive e
289                                              Multiple regression was used to assess the relation betw
290                                     Stepwise multiple regression was used to determine what factors c
291                 A linear, backward-selection multiple regression was used to obtain a model for the t
292                                        Using multiple regression, we assessed the effect of secondary
293 onparametric Wilcoxon-Mann-Whitney tests and multiple regression were used for statistical analysis.
294               Chi-square tests, t tests, and multiple regression were used to examine the association
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 icant for methane concentrations (P = 0.007; multiple regression), whereas distances to valley bottom
299         To test for exposure effect, we used multiple regression with exposure group (diesel vs. air)
300                        Data were analyzed by multiple regression, with techniques to gauge relative i

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