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