1 using multivariate analysis of variance and
multiple regression analysis.
2 ry and meal attributes was examined by using
multiple regression analysis.
3 rameters, DXA BMD, and FL were correlated at
multiple regression analysis.
4 edict estimated VO(2max) was determined with
multiple regression analysis.
5 data obtained were analyzed using linear and
multiple regression analysis.
6 Our primary analytical method was
multiple regression analysis.
7 d order polynomial model was developed using
multiple regression analysis.
8 in sensitivity, and leptin concentrations by
multiple regression analysis.
9 ta were analyzed with the Student t test and
multiple regression analysis.
10 ude of IOP reduction were investigated using
multiple regression analysis.
11 s that influenced cost were identified using
multiple regression analysis.
12 sual function parameters were compared using
multiple regression analysis.
13 ect on bone-mineral density was estimated by
multiple regression analysis.
14 imum-minimum area, cm(2)) were identified by
multiple regression analysis.
15 e logrank test (univariate analyses) and Cox
multiple regression analysis.
16 and various dietary factors was assessed by
multiple regression analysis.
17 ein concentrations, and low weight-forage in
multiple regression analysis.
18 HLA mismatch (P = 0.06) impacted survival in
multiple regression analysis.
19 using Pearson's correlation coefficients and
multiple regression analysis.
20 for Mn and Fe, respectively) as indicated by
multiple regression analysis.
21 entilation and preserved sensory function by
multiple regression analysis.
22 tary intake variables were achieved by using
multiple regression analysis.
23 enrichment factor (EF), in the conventional
multiple regression analysis.
24 r = -0.282, P = .257), as confirmed by using
multiple regression analysis.
25 and lesion characteristics was explored with
multiple regression analysis.
26 Associations were estimated using
multiple regression analysis.
27 Data was analysed using
multiple regression analysis.
28 stigated by using a general linear model and
multiple regression analysis.
29 rowth or fat mass in either cohort following
multiple regression analysis.
30 volume (V(S)), were evaluated with stepwise
multiple regression analysis.
31 on of GLUT-1 and GLUT-4 was characterized by
multiple-regression analysis.
32 lipid comparisons were subjected to weighted
multiple-regression analysis.
33 In Cox
multiple regression analysis,
3 of 24 confounding variab
34 By logistic
multiple regression analysis,
a low left ventricular eje
35 By
multiple regression analysis,
AAI was the only predictor
36 On
multiple regression analysis,
adipose IR index and postp
37 tios was modeled using a single multivariate
multiple regression analysis adjusted for age and curren
38 In a
multiple regression analysis adjusted for age; smoking;
39 s of interest in the VLSM model, including a
multiple regression analysis adjusted for confounding va
40 In a
multiple regression analysis adjusting for confounders,
41 Multiple regression analysis adjusting for the combined
42 However,
multiple regression analysis after adjustment for covari
43 ly correlated with life span (P<0.0003) in a
multiple regression analysis after adjustment for sex.
44 associated with advanced hepatic fibrosis on
multiple regression analysis after adjustments for age,
45 In stepwise
multiple regression analysis,
after entering all the var
46 Stepwise
multiple regression analysis also showed no differences
47 Multiple regression analysis also showed that current to
48 to a second-order polynomial equation using
multiple regression analysis and analyzed by appropriate
49 on of age with change in QOL was measured by
multiple regression analysis and based on two meta-score
50 This article provides an introduction to
multiple regression analysis and its application in diag
51 and demographic variables were examined with
multiple regression analysis and multilevel modelling.
52 relationship among measures was assessed by
multiple regression analysis and structural equation mod
53 In both
multiple regression analysis and structural equation mod
54 With use of
multiple regression analysis and various models, NOx FSR
55 ty, predictors of higher titers of antibody (
multiple regression analysis),
and cutoff values of meas
56 atistics, the chi(2) test, rank correlation,
multiple regression analysis,
and analysis of variance w
57 curve, intraclass correlation coefficients,
multiple regression analysis,
and paired Student t tests
58 Outlier data were excluded from
multiple regression analysis,
and reference equations we
59 tcome limited the number of variables in the
multiple regression analysis,
and whether nonsignificant
60 Multiple regression analysis applying generalized estima
61 Multiple regression analysis assessed the associations b
62 Using
multiple regression analysis,
BAP1 mutations were associ
63 icantly correlated with VA in univariate and
multiple regression analysis (
both P < 0.001).
64 By stepwise
multiple regression analysis,
both mitral annular area a
65 On
multiple regression analysis,
choroidal thickness, age,
66 Multiple regression analysis confirmed independent assoc
67 Multiple regression analysis confirmed that higher plasm
68 Multiple regression analysis confirmed that hpIGFBP-1 wa
69 Multiple regression analysis confirmed this finding (B =
70 Multiple regression analysis controlling for all factors
71 olvent effect was fitted satisfactorily with
multiple regression analysis,
correlating the obtained s
72 Furthermore,
multiple regression analysis could only confirm an indep
73 Cox
multiple regression analysis demonstrated a significant
74 Multiple regression analysis demonstrated a significant
75 Furthermore,
multiple regression analysis demonstrated an independent
76 Multiple regression analysis demonstrated that baseline
77 By using 4D four-dimensional CT data,
multiple regression analysis demonstrated that TGD troch
78 Multiple regression analysis demonstrated that the three
79 Multiple regression analysis demonstrated that waist cir
80 The use of
multiple regression analysis demonstrates that FAEE cont
81 In
multiple regression analysis,
duration of corticosteroid
82 challenged by other perinatal variables in a
multiple regression analysis,
early weaning significantl
83 Multiple regression analysis (
F = 7.51; P < .001) showed
84 Multiple regression analysis failed to find a relationsh
85 Results At
multiple regression analysis,
fibrosis was the only vari
86 At
multiple regression analysis for group 1, lesion size an
87 of possible importance were evaluated with a
multiple regression analysis for pretreatment PFTs and w
88 Multiple regression analysis for survival and DIPS paten
89 nt with the existence of suppressor effects,
multiple-regression analysis found amygdala responses to
90 Multiple regression analysis further shows that the incr
91 On stepwise
multiple regression analysis,
glycemic load accounted fo
92 In the
multiple regression analysis,
having CD predicted 10% of
93 At
multiple regression analysis,
HEF was the only parameter
94 id cocaine use disorder were controlled in a
multiple regression analysis,
however, comorbid cocaine
95 An explorative stepwise
multiple regression analysis identified 1) post-treatmen
96 g pooled 7q- and 11p-linked blood relatives,
multiple regression analysis identified both genotype (p
97 Further
multiple regression analysis identified certain pre-extr
98 Stepwise
multiple regression analysis identified initial AVA, cur
99 Multiple regression analysis identified pT.Bili as the o
100 Multiple regression analysis identified that number of S
101 In
multiple regression analysis,
IGFBP-1 was independently
102 Multiple regression analysis in the D2 mice revealed an
103 A
multiple regression analysis in which adjustment was mad
104 C-PP was calculated for each sex by a
multiple regression analysis including B-PP, age, height
105 A
multiple regression analysis including data of TLR4 expr
106 Multiple regression analysis including limbic (hippocamp
107 In
multiple regression analysis,
including established card
108 In
multiple regression analysis,
increased IMT in children
109 On
multiple regression analysis,
increases in PImax correla
110 In a
multiple regression analysis,
increasing age, increasing
111 Multiple regression analysis indicated that age and CLS
112 Multiple regression analysis indicated that age, mean ar
113 Multiple regression analysis indicated that approximatel
114 Multiple regression analysis indicated that divergence i
115 Multiple regression analysis indicated that Douglas-fir
116 Multiple regression analysis indicated that for all subj
117 Multiple regression analysis indicated that infarct size
118 us and the 3 absorption periods were pooled,
multiple regression analysis indicated that iron absorpt
119 Multiple regression analysis indicated that the inverse
120 Furthermore,
multiple regression analysis indicated that the relation
121 DT concentrations in soils based on stepwise
multiple regression analysis is developed.
122 value of these predictors, identifying that
multiple regression analysis is necessary to understand
123 By analysis of covariance and
multiple regression analysis,
it was found that only the
124 A
multiple-regression analysis led to a final model explai
125 In
multiple regression analysis,
levels of tumor necrosis f
126 In
multiple regression analysis,
lower socioeconomic status
127 On
multiple regression analysis,
male gender and not having
128 In a
multiple regression analysis model, the increase of CD4(
129 Multiple regression analysis modeled with age and time f
130 On
multiple regression analysis,
obesity was the strongest
131 r analysis of graft and patient survival and
multiple regression analysis of 1-year graft function we
132 In the
multiple regression analysis of 34653 respondents (14564
133 Multiple regression analysis of dose versus root growth
134 Stepwise
multiple regression analysis of semiquantitative data sh
135 Multiple regression analysis of the data showed that, al
136 d old peptide fractions was determined using
multiple regression analysis of the observed spectrum as
137 In this study, we present a
multiple regression analysis of transcriptomic data in 1
138 By
multiple regression analysis,
only average fasting plasm
139 When subjected to
multiple regression analysis,
only fat mass was predicti
140 Using Cox
multiple regression analysis,
only histologic grade had
141 egression analysis (P </= 0.018) but not the
multiple regression analysis (
P >/= 0.210).
142 maging findings and clinical scores (P >.05,
multiple regression analysis;
P =.25-.75, Mann-Whitney U
143 s efficacy in depression, and a prespecified
multiple regression analysis (
path analysis) to calculat
144 In
multiple regression analysis,
patients with no response
145 Multiple regression analysis performed on the combined e
146 In
multiple regression analysis,
predictors of mortality in
147 species and time are themselves correlated,
multiple regression analysis provides a statistical fram
148 Through stepwise
multiple regression analysis,
Q(peak), RBCV and Hb(mass)
149 In a
multiple regression analysis,
race and season were the s
150 Univariate
multiple regression analysis revealed a common, domain-i
151 Multiple regression analysis revealed an association bet
152 Multiple regression analysis revealed CS was important f
153 Multiple regression analysis revealed direct correlation
154 Multiple regression analysis revealed disease duration,
155 Multiple regression analysis revealed O2Pmax to be the b
156 Stepwise
multiple regression analysis revealed that a poor visual
157 Multiple regression analysis revealed that being within
158 Multiple regression analysis revealed that coronary flow
159 Multiple regression analysis revealed that for fibrinoge
160 Multiple regression analysis revealed that plasma angiot
161 Also in the main clinical trial,
multiple regression analysis revealed that SF + D best p
162 A
multiple regression analysis revealed that the decrease
163 Multiple regression analysis revealed that the degree of
164 Multiple regression analysis revealed that the intergrou
165 In eyes with macular cysts,
multiple regression analysis revealed that visual acuity
166 Multiple regression analysis revealed that, controlling
167 Finally, a
multiple regression analysis reveals bilateral preSMA-ST
168 On
multiple regression analysis,
SAA levels were predicted
169 In
multiple regression analysis,
severity of disease indica
170 On
multiple regression analysis,
SF >1.5 x ULN was independ
171 Multiple regression analysis showed 4 Health Belief Mode
172 Multiple regression analysis showed a high correlation b
173 Multiple regression analysis showed a significant negati
174 Multiple regression analysis showed corneal hysteresis t
175 Multiple regression analysis showed patient age, contras
176 Multiple regression analysis showed that 9 of 35 BMI-ass
177 Multiple regression analysis showed that a vertical patt
178 Multiple regression analysis showed that African America
179 Multiple regression analysis showed that all subscales (
180 Multiple regression analysis showed that among all subje
181 Multiple regression analysis showed that at week 12, 48%
182 Multiple regression analysis showed that Cr and Ni were
183 A hierarchical
multiple regression analysis showed that in Vietnam thea
184 Multiple regression analysis showed that LDL cholesterol
185 Multiple regression analysis showed that lower age, high
186 Multiple regression analysis showed that male gender, ag
187 Stepwise
multiple regression analysis showed that MX1 expression
188 Multiple regression analysis showed that PDT type was no
189 Multiple regression analysis showed that renal failure w
190 Univariate and
multiple regression analysis showed that the area of the
191 Multiple regression analysis showed that the significant
192 Multiple regression analysis showed that the timing of f
193 Multiple regression analysis shows that low asthma quali
194 Multiple regression analysis shows that the likelihood o
195 In a
multiple regression analysis,
smaller hospital size and
196 In Cox
multiple regression analysis,
sodium intake was inversel
197 greement, McNemar test, Mann-Whitney U test,
multiple regression analysis,
Spearman correlation) were
198 In
multiple regression analysis,
SSPG concentration added m
199 Multiple regression analysis suggested that lower suPAR
200 Multiple regression analysis suggested that sulcular dep
201 Using
multiple regression analysis that included all subjects
202 We found, by use of
multiple regression analysis,
that sex, age, race, and s
203 In
multiple regression analysis,
the association of a treat
204 In
multiple regression analysis,
the changes in TGC, inspir
205 We analyzed, by
multiple regression analysis,
the determinants of PV ant
206 nd positive lymph nodes and after conducting
multiple regression analysis,
the hazard ratio for chemo
207 By
multiple regression analysis,
the predictors of O2Pmax w
208 By stepwise
multiple regression analysis,
the strongest predictor fo
209 By using
multiple regression analysis,
the strongest predictors o
210 In a Cox
multiple regression analysis,
the strongest prognostic i
211 We used
multiple regression analysis to assess the associations
212 as met (population achievement), and we used
multiple regression analysis to determine the extent to
213 dietary records through the use of stepwise
multiple regression analysis to develop models that rela
214 In this study, we used
multiple regression analysis to estimate the pathogenici
215 We also compared a neural network model with
multiple regression analysis to identify independent var
216 with asthma of varying severity, and we used
multiple regression analysis to relate genotypic finding
217 plied principal component analysis (PCA) and
multiple regression analysis to study the covariance str
218 0.79) and the RMR (R2 = 0.81) were seen, by
multiple regression analysis,
to correlate with glucagon
219 At
multiple regression analysis,
tumor at the prostate base
220 On the basis of
multiple regression analysis,
urinary alpha-CEHC excreti
221 or grade II-IV acute GVHD were identified in
multiple regression analysis:
use of 2 UCB units, use of
222 trata in China; for instance, a cross-county
multiple regression analysis using data from the 2000 ce
223 Furthermore, linear
multiple regression analysis using SI_INS mRNA and SI_16
224 the FVC curve (FEF(25-75)) was evaluated by
multiple regression analysis using transformed values ad
225 On
multiple regression analysis,
variables associated with
226 Multiple regression analysis was conducted to test if th
227 s of other laboratory and clinical criteria,
multiple regression analysis was performed and showed ag
228 Multiple regression analysis was performed to assess the
229 First, a hierarchical
multiple regression analysis was performed to determine
230 Multiple regression analysis was performed, and statisti
231 When
multiple regression analysis was performed, the extent o
232 P < 0.001) and leptin (r = 0.55, P < 0.01),
multiple regression analysis was repeated, adding total
233 significant predictor of plasma 25(OH)D when
multiple regression analysis was used to adjust for othe
234 Multiple regression analysis was used to assess associat
235 Multiple regression analysis was used to compare changes
236 Multiple regression analysis was used to determine if AB
237 Multiple regression analysis was used to determine the a
238 Multiple regression analysis was used to determine wheth
239 Multiple regression analysis was used to determine wheth
240 Multiple regression analysis was used to evaluate correl
241 Multiple regression analysis was used to examine MR imag
242 Multiple regression analysis was used to examine the rel
243 Multiple regression analysis was used to examine the var
244 Multiple regression analysis was used to identify brain
245 onships to RFS and OS were investigated, and
multiple regression analysis was used to identify intera
246 Multiple regression analysis was used to identify the pr
247 Multiple regression analysis was used to investigate dif
248 Multiple regression analysis was used to measure the ass
249 Multiple regression analysis was used to test prediction
250 Using univariate and
multiple regression analysis,
we analyzed risk of early
251 On the basis of the
multiple regression analysis,
we developed the following
252 Using site-directed mutagenesis and
multiple regression analysis,
we have studied the molecu
253 Univariate and
multiple regression analysis were performed.
254 edictors of iron absorption as determined by
multiple regression analysis were the contents of animal
255 -tailed z tests of percentages and means and
multiple regression analysis were used to compare inform
256 Univariate and
multiple regression analysis were used to examine the as
257 statistics, simple correlation, and stepwise
multiple regression analysis were used to identify signi
258 Partial correlations and
multiple regression analysis were used to test the assoc
259 between the patient and control groups using
multiple regression analysis while adjusting for age and
260 After
multiple regression analysis with adjustment for age, bo
261 In
multiple regression analysis with age and sex controlled
262 clerosis, and diabetes were then assessed by
multiple regression analysis with backward elimination.
263 Multiple regression analysis with combined 1/T2 (with re
264 In a
multiple regression analysis with fat, FFM, sex, age, an
265 The authors performed
multiple regression analysis with MPOD as the dependent
266 The parameters were correlated at simple and
multiple regression analysis with the expression of the
267 Multiple regression analysis with the OHSI as the depend
268 Results were analyzed using
multiple regression analysis,
with adjustment for age, s
269 e, and necrosis-inflammation score; however,
multiple-regression analysis yielded P values of <0.1 on