1 Associations were estimated using
multiple regression analysis.
2 Data was analysed using
multiple regression analysis.
3 stigated by using a general linear model and
multiple regression analysis.
4 rowth or fat mass in either cohort following
multiple regression analysis.
5 volume (V(S)), were evaluated with stepwise
multiple regression analysis.
6 using multivariate analysis of variance and
multiple regression analysis.
7 ry and meal attributes was examined by using
multiple regression analysis.
8 rameters, DXA BMD, and FL were correlated at
multiple regression analysis.
9 edict estimated VO(2max) was determined with
multiple regression analysis.
10 data obtained were analyzed using linear and
multiple regression analysis.
11 Our primary analytical method was
multiple regression analysis.
12 in sensitivity, and leptin concentrations by
multiple regression analysis.
13 ta were analyzed with the Student t test and
multiple regression analysis.
14 s that influenced cost were identified using
multiple regression analysis.
15 sual function parameters were compared using
multiple regression analysis.
16 ect on bone-mineral density was estimated by
multiple regression analysis.
17 imum-minimum area, cm(2)) were identified by
multiple regression analysis.
18 e logrank test (univariate analyses) and Cox
multiple regression analysis.
19 and various dietary factors was assessed by
multiple regression analysis.
20 ein concentrations, and low weight-forage in
multiple regression analysis.
21 HLA mismatch (P = 0.06) impacted survival in
multiple regression analysis.
22 using Pearson's correlation coefficients and
multiple regression analysis.
23 d order polynomial model was developed using
multiple regression analysis.
24 ude of IOP reduction were investigated using
multiple regression analysis.
25 for Mn and Fe, respectively) as indicated by
multiple regression analysis.
26 entilation and preserved sensory function by
multiple regression analysis.
27 tary intake variables were achieved by using
multiple regression analysis.
28 enrichment factor (EF), in the conventional
multiple regression analysis.
29 r = -0.282, P = .257), as confirmed by using
multiple regression analysis.
30 using correlation, bivariate regression, and
multiple regression analysis.
31 and lesion characteristics was explored with
multiple regression analysis.
32 on of GLUT-1 and GLUT-4 was characterized by
multiple-regression analysis.
33 lipid comparisons were subjected to weighted
multiple-regression analysis.
34 In Cox
multiple regression analysis,
3 of 24 confounding variab
35 With
multiple regression analysis,
a forward selection proced
36 By logistic
multiple regression analysis,
a low left ventricular eje
37 By
multiple regression analysis,
AAI was the only predictor
38 On
multiple regression analysis,
adipose IR index and postp
39 tios was modeled using a single multivariate
multiple regression analysis adjusted for age and curren
40 In a
multiple regression analysis adjusted for age; smoking;
41 s of interest in the VLSM model, including a
multiple regression analysis adjusted for confounding va
42 In a
multiple regression analysis adjusting for confounders,
43 Multiple regression analysis adjusting for the combined
44 However,
multiple regression analysis after adjustment for covari
45 ly correlated with life span (P<0.0003) in a
multiple regression analysis after adjustment for sex.
46 associated with advanced hepatic fibrosis on
multiple regression analysis after adjustments for age,
47 In stepwise
multiple regression analysis,
after entering all the var
48 Stepwise
multiple regression analysis also showed no differences
49 Multiple regression analysis also showed that current to
50 to a second-order polynomial equation using
multiple regression analysis and analyzed by appropriate
51 on of age with change in QOL was measured by
multiple regression analysis and based on two meta-score
52 First, using voxel-wise
multiple regression analysis and controlling for CSF bio
53 This article provides an introduction to
multiple regression analysis and its application in diag
54 bines two complementary tools, namely: (1) a
multiple regression analysis and its generalization, a c
55 and demographic variables were examined with
multiple regression analysis and multilevel modelling.
56 relationship among measures was assessed by
multiple regression analysis and structural equation mod
57 In both
multiple regression analysis and structural equation mod
58 With use of
multiple regression analysis and various models, NOx FSR
59 ty, predictors of higher titers of antibody (
multiple regression analysis),
and cutoff values of meas
60 Multiple regression analysis,
and an artificial neutral
61 atistics, the chi(2) test, rank correlation,
multiple regression analysis,
and analysis of variance w
62 curve, intraclass correlation coefficients,
multiple regression analysis,
and paired Student t tests
63 Outlier data were excluded from
multiple regression analysis,
and reference equations we
64 tcome limited the number of variables in the
multiple regression analysis,
and whether nonsignificant
65 Multiple regression analysis applying generalized estima
66 Multiple regression analysis assessed the associations b
67 Using
multiple regression analysis,
BAP1 mutations were associ
68 icantly correlated with VA in univariate and
multiple regression analysis (
both P < 0.001).
69 By stepwise
multiple regression analysis,
both mitral annular area a
70 On
multiple regression analysis,
choroidal thickness, age,
71 Multiple regression analysis compared prescription data
72 Multiple regression analysis confirmed independent assoc
73 Furthermore,
multiple regression analysis confirmed that fCAL-turbo r
74 Multiple regression analysis confirmed that higher plasm
75 Multiple regression analysis confirmed that hpIGFBP-1 wa
76 Further stepwise
multiple regression analysis confirmed the positive asso
77 Multiple regression analysis confirmed this finding (B =
78 Multiple regression analysis controlling for all factors
79 olvent effect was fitted satisfactorily with
multiple regression analysis,
correlating the obtained s
80 Furthermore,
multiple regression analysis could only confirm an indep
81 Cox
multiple regression analysis demonstrated a significant
82 Multiple regression analysis demonstrated a significant
83 Furthermore,
multiple regression analysis demonstrated an independent
84 Multiple regression analysis demonstrated that baseline
85 By using 4D four-dimensional CT data,
multiple regression analysis demonstrated that TGD troch
86 Multiple regression analysis demonstrated that the three
87 Multiple regression analysis demonstrated that waist cir
88 The use of
multiple regression analysis demonstrates that FAEE cont
89 In
multiple regression analysis,
duration of corticosteroid
90 challenged by other perinatal variables in a
multiple regression analysis,
early weaning significantl
91 Multiple regression analysis (
F = 7.51; P < .001) showed
92 Multiple regression analysis failed to find a relationsh
93 Results At
multiple regression analysis,
fibrosis was the only vari
94 At
multiple regression analysis for group 1, lesion size an
95 of possible importance were evaluated with a
multiple regression analysis for pretreatment PFTs and w
96 Multiple regression analysis for survival and DIPS paten
97 nt with the existence of suppressor effects,
multiple-regression analysis found amygdala responses to
98 Multiple regression analysis further shows that the incr
99 In a
multiple regression analysis,
GDF-15 (growth and differe
100 On stepwise
multiple regression analysis,
glycemic load accounted fo
101 In the
multiple regression analysis,
having CD predicted 10% of
102 At
multiple regression analysis,
HEF was the only parameter
103 id cocaine use disorder were controlled in a
multiple regression analysis,
however, comorbid cocaine
104 An explorative stepwise
multiple regression analysis identified 1) post-treatmen
105 g pooled 7q- and 11p-linked blood relatives,
multiple regression analysis identified both genotype (p
106 Further
multiple regression analysis identified certain pre-extr
107 Stepwise
multiple regression analysis identified initial AVA, cur
108 Multiple regression analysis identified pT.Bili as the o
109 Multiple regression analysis identified that number of S
110 In
multiple regression analysis,
IGFBP-1 was independently
111 In
multiple regression analysis in stroke patients, plasma
112 Multiple regression analysis in the D2 mice revealed an
113 A
multiple regression analysis in which adjustment was mad
114 C-PP was calculated for each sex by a
multiple regression analysis including B-PP, age, height
115 A
multiple regression analysis including data of TLR4 expr
116 Multiple regression analysis including limbic (hippocamp
117 In
multiple regression analysis,
including established card
118 In
multiple regression analysis,
increased IMT in children
119 On
multiple regression analysis,
increases in PImax correla
120 In a
multiple regression analysis,
increasing age, increasing
121 Multiple regression analysis indicated that age and CLS
122 Multiple regression analysis indicated that age, mean ar
123 Multiple regression analysis indicated that approximatel
124 Multiple regression analysis indicated that divergence i
125 Multiple regression analysis indicated that Douglas-fir
126 Multiple regression analysis indicated that for all subj
127 Multiple regression analysis indicated that infarct size
128 us and the 3 absorption periods were pooled,
multiple regression analysis indicated that iron absorpt
129 Multiple regression analysis indicated that the inverse
130 Furthermore,
multiple regression analysis indicated that the relation
131 DT concentrations in soils based on stepwise
multiple regression analysis is developed.
132 value of these predictors, identifying that
multiple regression analysis is necessary to understand
133 By analysis of covariance and
multiple regression analysis,
it was found that only the
134 A
multiple-regression analysis led to a final model explai
135 In
multiple regression analysis,
levels of tumor necrosis f
136 In
multiple regression analysis,
lower socioeconomic status
137 On
multiple regression analysis,
male gender and not having
138 In a
multiple regression analysis,
MI was independently assoc
139 In a
multiple regression analysis model, the increase of CD4(
140 Multiple regression analysis modeled with age and time f
141 On
multiple regression analysis,
obesity was the strongest
142 r analysis of graft and patient survival and
multiple regression analysis of 1-year graft function we
143 In the
multiple regression analysis of 34653 respondents (14564
144 Multiple regression analysis of dose versus root growth
145 Stepwise
multiple regression analysis of semiquantitative data sh
146 Multiple regression analysis of the data showed that, al
147 d old peptide fractions was determined using
multiple regression analysis of the observed spectrum as
148 In this study, we present a
multiple regression analysis of transcriptomic data in 1
149 By
multiple regression analysis,
only average fasting plasm
150 When subjected to
multiple regression analysis,
only fat mass was predicti
151 Using Cox
multiple regression analysis,
only histologic grade had
152 However, upon
multiple regression analysis,
only the association betwe
153 egression analysis (P </= 0.018) but not the
multiple regression analysis (
P >/= 0.210).
154 maging findings and clinical scores (P >.05,
multiple regression analysis;
P =.25-.75, Mann-Whitney U
155 In a
multiple regression analysis,
participants who had recov
156 s efficacy in depression, and a prespecified
multiple regression analysis (
path analysis) to calculat
157 By
multiple regression analysis,
patient BMI remained indep
158 In
multiple regression analysis,
patients with no response
159 Multiple regression analysis performed on the combined e
160 In
multiple regression analysis,
PKP (vs DALK) (odds ratio
161 In
multiple regression analysis,
predictors of mortality in
162 species and time are themselves correlated,
multiple regression analysis provides a statistical fram
163 Through stepwise
multiple regression analysis,
Q(peak), RBCV and Hb(mass)
164 In a
multiple regression analysis,
race and season were the s
165 Univariate
multiple regression analysis revealed a common, domain-i
166 Multiple regression analysis revealed an association bet
167 Multiple regression analysis revealed CS was important f
168 Multiple regression analysis revealed direct correlation
169 Multiple regression analysis revealed disease duration,
170 A
multiple regression analysis revealed H and frequency do
171 Multiple regression analysis revealed O2Pmax to be the b
172 Stepwise
multiple regression analysis revealed that a poor visual
173 Multiple regression analysis revealed that being within
174 Multiple regression analysis revealed that coronary flow
175 Multiple regression analysis revealed that for fibrinoge
176 Multiple regression analysis revealed that latrine cover
177 Multiple regression analysis revealed that plasma angiot
178 Also in the main clinical trial,
multiple regression analysis revealed that SF + D best p
179 A
multiple regression analysis revealed that the decrease
180 Multiple regression analysis revealed that the degree of
181 Furthermore,
multiple regression analysis revealed that the interacti
182 Multiple regression analysis revealed that the intergrou
183 In eyes with macular cysts,
multiple regression analysis revealed that visual acuity
184 Multiple regression analysis revealed that, controlling
185 Multiple regression analysis revealed TIP3 to be associa
186 Finally, a
multiple regression analysis reveals bilateral preSMA-ST
187 On
multiple regression analysis,
SAA levels were predicted
188 In
multiple regression analysis,
severity of disease indica
189 On
multiple regression analysis,
SF >1.5 x ULN was independ
190 Multiple regression analysis showed 4 Health Belief Mode
191 Multiple regression analysis showed a high correlation b
192 Multiple regression analysis showed a significant negati
193 Multiple regression analysis showed corneal hysteresis t
194 Multiple regression analysis showed no relationship with
195 Multiple regression analysis showed patient age, contras
196 Multiple regression analysis showed that 9 of 35 BMI-ass
197 Multiple regression analysis showed that a vertical patt
198 Multiple regression analysis showed that African America
199 Multiple regression analysis showed that all subscales (
200 Multiple regression analysis showed that among all subje
201 Multiple regression analysis showed that at week 12, 48%
202 The
multiple regression analysis showed that better self-rat
203 Multiple regression analysis showed that Cr and Ni were
204 The
multiple regression analysis showed that glucose influen
205 A hierarchical
multiple regression analysis showed that in Vietnam thea
206 Multiple regression analysis showed that LDL cholesterol
207 Multiple regression analysis showed that lower age, high
208 Multiple regression analysis showed that male gender, ag
209 Stepwise
multiple regression analysis showed that MX1 expression
210 Multiple regression analysis showed that PDT type was no
211 Multiple regression analysis showed that renal failure w
212 Univariate and
multiple regression analysis showed that the area of the
213 Multiple regression analysis showed that the significant
214 Multiple regression analysis showed that the timing of f
215 Multiple regression analysis shows that low asthma quali
216 Multiple regression analysis shows that the likelihood o
217 In a
multiple regression analysis,
smaller hospital size and
218 In Cox
multiple regression analysis,
sodium intake was inversel
219 greement, McNemar test, Mann-Whitney U test,
multiple regression analysis,
Spearman correlation) were
220 In
multiple regression analysis,
SSPG concentration added m
221 Multiple regression analysis suggested that lower suPAR
222 Multiple regression analysis suggested that sulcular dep
223 Using
multiple regression analysis that included all subjects
224 We found, by use of
multiple regression analysis,
that sex, age, race, and s
225 In
multiple regression analysis,
the association of a treat
226 In
multiple regression analysis,
the changes in TGC, inspir
227 We analyzed, by
multiple regression analysis,
the determinants of PV ant
228 nd positive lymph nodes and after conducting
multiple regression analysis,
the hazard ratio for chemo
229 By
multiple regression analysis,
the predictors of O2Pmax w
230 By stepwise
multiple regression analysis,
the strongest predictor fo
231 By using
multiple regression analysis,
the strongest predictors o
232 In a Cox
multiple regression analysis,
the strongest prognostic i
233 We used
multiple regression analysis to assess the associations
234 as met (population achievement), and we used
multiple regression analysis to determine the extent to
235 dietary records through the use of stepwise
multiple regression analysis to develop models that rela
236 We used
multiple regression analysis to estimate predictors of p
237 In this study, we used
multiple regression analysis to estimate the pathogenici
238 We also compared a neural network model with
multiple regression analysis to identify independent var
239 with asthma of varying severity, and we used
multiple regression analysis to relate genotypic finding
240 plied principal component analysis (PCA) and
multiple regression analysis to study the covariance str
241 0.79) and the RMR (R2 = 0.81) were seen, by
multiple regression analysis,
to correlate with glucagon
242 At
multiple regression analysis,
tumor at the prostate base
243 On the basis of
multiple regression analysis,
urinary alpha-CEHC excreti
244 or grade II-IV acute GVHD were identified in
multiple regression analysis:
use of 2 UCB units, use of
245 trata in China; for instance, a cross-county
multiple regression analysis using data from the 2000 ce
246 Furthermore, linear
multiple regression analysis using SI_INS mRNA and SI_16
247 the FVC curve (FEF(25-75)) was evaluated by
multiple regression analysis using transformed values ad
248 On
multiple regression analysis,
variables associated with
249 Multiple regression analysis was conducted to test if th
250 s of other laboratory and clinical criteria,
multiple regression analysis was performed and showed ag
251 Multiple regression analysis was performed to assess the
252 Multiple regression analysis was performed to determine
253 First, a hierarchical
multiple regression analysis was performed to determine
254 conducted using analysis of covariance, and
multiple regression analysis was performed to identify f
255 Multiple regression analysis was performed, and statisti
256 When
multiple regression analysis was performed, the extent o
257 P < 0.001) and leptin (r = 0.55, P < 0.01),
multiple regression analysis was repeated, adding total
258 significant predictor of plasma 25(OH)D when
multiple regression analysis was used to adjust for othe
259 Multiple regression analysis was used to assess associat
260 Multiple regression analysis was used to compare changes
261 Linear
multiple regression analysis was used to create models f
262 Multiple regression analysis was used to create predicti
263 Multiple regression analysis was used to determine if AB
264 Multiple regression analysis was used to determine the a
265 Multiple regression analysis was used to determine wheth
266 Multiple regression analysis was used to determine wheth
267 Multiple regression analysis was used to evaluate correl
268 Multiple regression analysis was used to evaluate the re
269 Multiple regression analysis was used to examine MR imag
270 Multiple regression analysis was used to examine the rel
271 Multiple regression analysis was used to examine the var
272 Multiple regression analysis was used to identify brain
273 onships to RFS and OS were investigated, and
multiple regression analysis was used to identify intera
274 Multiple regression analysis was used to identify the pr
275 Multiple regression analysis was used to investigate dif
276 Multiple regression analysis was used to measure the ass
277 Multiple regression analysis was used to test prediction
278 Using univariate and
multiple regression analysis,
we analyzed risk of early
279 On the basis of the
multiple regression analysis,
we developed the following
280 Using site-directed mutagenesis and
multiple regression analysis,
we have studied the molecu
281 First, using voxel-wise
multiple regression analysis,
we identified the metaboli
282 In a
multiple regression analysis,
we used the balance in inh
283 Univariate and
multiple regression analysis were performed.
284 edictors of iron absorption as determined by
multiple regression analysis were the contents of animal
285 -tailed z tests of percentages and means and
multiple regression analysis were used to compare inform
286 Univariate and
multiple regression analysis were used to examine the as
287 statistics, simple correlation, and stepwise
multiple regression analysis were used to identify signi
288 Partial correlations and
multiple regression analysis were used to test the assoc
289 between the patient and control groups using
multiple regression analysis while adjusting for age and
290 ratio, 0.20; 95% CI, 0.06-0.73; P=0.015) by
multiple regression analysis,
while age and valve type d
291 After
multiple regression analysis with adjustment for age, bo
292 In
multiple regression analysis with age and sex controlled
293 clerosis, and diabetes were then assessed by
multiple regression analysis with backward elimination.
294 Multiple regression analysis with combined 1/T2 (with re
295 In a
multiple regression analysis with fat, FFM, sex, age, an
296 The authors performed
multiple regression analysis with MPOD as the dependent
297 The parameters were correlated at simple and
multiple regression analysis with the expression of the
298 Multiple regression analysis with the OHSI as the depend
299 Results were analyzed using
multiple regression analysis,
with adjustment for age, s
300 e, and necrosis-inflammation score; however,
multiple-regression analysis yielded P values of <0.1 on