ライフサイエンスコーパス: analysis of variance

1 and 78.0+/-6.9 mum in Indians (P < 0.001 by analysis of variance).

2 ldren (P<0.001 by permutational multivariate analysis of variance).

3 ,0; 5.8), saline: 4.4 (3.5; 5.8) (P = 0.016, analysis of variance).

4 ss heterogeneous in AC than in SCC (Friedman analysis of variance).

5 , saline 3 (3, 3) and MM 3 (3, 3) (P< 0.001, analysis of variance).

6 sely resembling controls (p < 0.01 for each, analysis of variance).

7 parable for all eyes (0.04 mm(2); P < 0.001, analysis of variance).

8 t Arm) X 3 (Time of Assessment) mixed-method analysis of variance.

9 etration of the bone surface and analyzed by analysis of variance.

10 tion of a composite model using multivariate analysis of variance.

11 each group and statistically compared using analysis of variance.

12 mpared with that in control regions by using analysis of variance.

13 quality of life were assessed using one-way analysis of variance.

14 ed across groups using chi-squared tests and analysis of variance.

15 mes were assessed with Poisson regression or analysis of variance.

16 ms were analyzed by using paired t tests and analysis of variance.

17 assessed by using two-way repeated-measures analysis of variance.

18 commonly applied variable selection method - analysis of variance.

19 were performed by using the chi(2) test and analysis of variance.

20 th exact binomial tests or repeated-measures analysis of variance.

21 ual term, based on a regression analysis and analysis of variance.

22 red before and after TIPS placement by using analysis of variance.

23 by using the independent Student t test and analysis of variance.

24 ight-for-length percentiles was tested using analysis of variance.

25 tics linked to expertise were explored using analysis of variance.

26 yzed by using the Student t test and two-way analysis of variance.

27 n changes were assessed by repeated-measures analysis of variance.

28 Variables were compared by using analysis of variance.

29 stology, and data were analyzed with one-way analysis of variance.

30 e trimesters were evaluated by using one-way analysis of variance.

31 ere compared between subject groups by using analysis of variance.

32 ed against other readers (nonoutliers) using analysis of variance.

33 he other genotype groups were evaluated with analysis of variance.

34 er exact test, t test, and repeated-measures analysis of variance.

35 groups were analyzed using paired t test and analysis of variance.

36 red using Akaike's Information Criterion and analysis of variance.

37 ated was statistical analysis was done using analysis of variance.

38 compared between adherence arms using 1-way analysis of variance.

39 alyses among groups were tested with one-way analysis of variance.

40 The experimented data was evaluated by the analysis of variance.

41 ectrophotometrically; rates were compared by analysis of variance.

42 for drug or ablation groups by using two-way analysis of variance.

43 on and other outcomes with repeated-measures analysis of variance.

44 okine and metabolite levels were analyzed by analysis of variance.

45 echanical cycling and analyzed with 3-factor analysis of variance.

46 Data were analyzed using repeated measures analysis of variance.

47 Data were analyzed with analogs of analysis of variance.

48 mance by using a Student t test or a one-way analysis of variance.

49 ric BMD and bone biomarkers were compared by analysis of variance, adjusted for strata.

50 ntake in controls, it increased in patients (analysis of variance: alcohol state x group, p = 0.004).

51 The analysis of variance (alpha=0.05) showed no significant

52 geted peptides as a function of abundance by analysis of variance analysis (p = 0.17).

53 A 2-way analysis of variance analyzing solution, time, and solut

54 m were assessed by using a repeated measures analysis of variance and a post hoc Bonferroni multiple

55 Regional analysis of variance and a support vector machine were u

56 d periodontal parameters were assessed using analysis of variance and Bonferroni post hoc tests.

57 Data were analyzed using the split-plot analysis of variance and chi(2) tests with a significanc

58 Data were analyzed via analysis of variance and Fisher's post hoc analyses.

59 Mixed-model analysis of variance and Kaplan-Meyer method was accesse

60 = 39) brain bank donors were analysed using analysis of variance and linear mixed effects regression

61 ast density estimation was investigated with analysis of variance and linear regression.

62 Analysis of variance and Mann-Whitney U tests with post

63 Data were analyzed by using random effects analysis of variance and mean and standard error of the

64 s, associations were explored through use of analysis of variance and multivariable logistic regressi

65 phs were compared by using repeated-measures analysis of variance and one-way analysis of variance, w

66 bo groups were examined by repeated-measures analysis of variance and paired t tests.

67 on a per-tumor basis and were compared with analysis of variance and paired two-tailed t tests.

68 Data were tested statistically by analysis of variance and Pearson rank correlation test.

69 d the review times across review types using analysis of variance and post hoc Scheffe tests after ac

70 Data were analyzed using analysis of variance and post hoc tests (significance le

71 zed using mean and standard deviation; 1-way analysis of variance and post hoc Tukey's studentized ra

72 Two-way analysis of variance and posterior Fisher least signific

73 It draws upon strategies from analysis of variance and principal component analysis in

74 Data were subjected to an analysis of variance and principal components analysis.

75 t tests, analysis of variance and receiver operating characterist

76 good discriminant validity as judged by the analysis of variance and receiver operating curves.

77 Statistical analysis was performed by using analysis of variance and serial measurement testing.

78 ies of modified Fisher scale was found using analysis of variance and Spearman rank correlation (p =

79 Multivariate analysis of variance and structural equation modeling wa

80 Two-way analysis of variance and Student t test were used for st

81 Two-way analysis of variance and Student t tests were used to de

82 Analysis of variance and t test were used for statistica

83 at day 0 and at sacrifice were compared with analysis of variance and the two-tailed Student t test.

84 Analysis of variance and Tukey honest significant differ

85 One-way analysis of variance and Tukey honestly significant diff

86 ween age groups were determined with one-way analysis of variance and Tukey multiple comparisons test

87 Data were submitted to two-way analysis of variance and Tukey post hoc test with P < 0.

88 The values obtained underwent analysis of variance and Tukey testing (P <0.05).

89 os were compared between groups by using the analysis of variance and were analyzed relative to group

90 on (P < 0.0002 by permutational multivariate analysis of variance) and development between groups.

91 Univariable statistics (chi(2) test and analysis of variance) and logistic regression were used

92 Between-group differences were assessed by analysis of variance, and associations were assessed by

93 Pearson correlation, analysis of variance, and intraclass correlation analyse

94 sing the chi-square test, Fisher exact test, analysis of variance, and Kaplan-Meier analysis.

95 Univariable regression and analysis of variance, and multivariable analysis of cova

96 Descriptive statistics, t-tests, one-way analysis of variance, and Pearson or Spearman's correlat

97 partial least squares-discriminant analysis, analysis of variance, and random forest tests were used

98 ere compared between groups by using one-way analysis of variance, and the relationships with pulmona

99 ll the optimized parameters were analyzed by analysis of variance, and were found to be statistically

100 , and -3+/-4.3 mmHg, respectively; P = 0.02 [analysis of variance] and P = 0.002 [t test] for NSAID v

101 mpact on full-blown PML-IRIS latency; (2) an analysis of variance ANOVA to investigate their impact o

102 ith the most commonly used existing method - analysis of variance (ANOVA) - and show that ANOVA assum

103 Analysis of variance (ANOVA) and kinetic modeling were a

104 All results were evaluated by analysis of variance (ANOVA) and principal component ana

105 Analysis of variance (ANOVA) and receiver operating char

106 Data were statistically analyzed by one-way analysis of variance (ANOVA) and Student Newman Keuls's

107 hysics based numerical techniques along with Analysis of variance (ANOVA) and Taguchi optimization me

108 We used the analysis of variance (ANOVA) and the Student's t-test.

109 All results were subjected to a one-way analysis of variance (ANOVA) and, if significant differe

110 rrected minor allele frequencies, we applied ANalysis Of VAriance (ANOVA) based on a linear mixed mod

111 Univariate statistical analysis of variance (ANOVA) established that individual

112 Two-way analysis of variance (ANOVA) results reveal that most me

113 The results from analysis of variance (ANOVA) showed that similar numbers

114 drawn independently by 2 masked graders, and analysis of variance (ANOVA) tests were used to compare

115 An analysis of variance (ANOVA) was performed to compare me

116 ween M1 and M2 determined by t test, and the analysis of variance (ANOVA) was used to compare the dat

117 Principal component analysis (PCA) and analysis of variance (ANOVA) were performed to evaluate

118 Median normalized absolute deviation (MNAD), analysis of variance (ANOVA), and coefficient of variati

119 nd imaging measurements were determined with analysis of variance (ANOVA), Holm-Bonferroni corrected

120 and multivariate chemometric tools, such as analysis of variance (ANOVA), principal component analys

121 Analysis of variance (ANOVA), principal component analys

122 and clinical outcomes was evaluated by using analysis of variance (ANOVA), the Kruskal-Wallis H test,

123 d variable selection (SRVS) approach or with analysis of variance (ANOVA)-based gene selection approa

124 Kruskal-Wallis analysis of variance (ANOVA)-on-Ranks with post-hoc Mann

125 The remaining peaks were subjected to analysis of variance (ANOVA)-simultaneous component anal

126 -subject coefficients of variation (CVs) and analysis of variance (ANOVA).

127 tients who received from 4 to 12 injections (analysis of variance [ANOVA] P = .027) and compared with

128 s observed in LHON eyes were compared (1-way analysis of variance [ANOVA]) with those of controls.

129 Analysis of variance applied to the dataset indicated th

130 (chi(2) test; 2-tailed, unpaired t test; and analysis of variance) as well as multivariable logistic

131 t (n=20; P<1.05E-05, 1-way repeated measures analysis of variance, Bonferroni threshold).

132 Therefore, analysis of variance can be applied and patterns are obs

133 tatistical software (version 22) using 1-way analysis of variance, chi2 tests, and Pearson correlatio

134 Analysis of variance compared differences among conditio

135 e results of a mixed between-within subjects analysis of variance, controlling for physical activity

136 Analyzed by Kruskal-Wallis analysis of variance, Dunn's statistic, and separate Man

137 %-36% except anterior cingulate cortex, 24%; analysis of variance, effect of diagnosis: P < .001 to P

138 d in [(11)C]NPA DeltaBPND (repeated-measures analysis of variance, F1,26 = 1.9, p = .18) between HCs

139 baseline [(11)C]NPA BPND (repeated-measures analysis of variance, F1,26 = 3.34, p = .08) between the

140 sses were compared across quartiles using an analysis of variance factorial design testing for intera

141 Analysis of variance findings for key mRNA and immunohis

142 yzed by ordinal logistic regression, one-way analysis of variance, Fisher exact test, and Kruskal-Wal

143 rrelations among variables was assessed with analysis of variance followed by linear regression.

144 and plant-based samples, one-way univariate analysis of variance followed by pair-wise comparison wa

145 Data were analyzed using analysis of variance followed by post hoc Bonferroni cor

146 Analysis of variance followed by post-hoc Tukey test was

147 iple comparisons were performed with two-way analysis of variance, followed by the Student t test wit

148 r independent groups and a repeated-measures analysis of variance for dependent groups.

149 Comparisons were performed with analysis of variance for repeated measures or Friedman t

150 ared among PC and absorption images by using analysis of variance for repeated measures with post hoc

151 tment significantly (P = 0.001, multivariate analysis of variance for repeated measures) lowered post

152 ere compared by using two-tailed t tests and analysis of variance for selected group comparisons.

153 rmed by using the Student t test and one-way analysis of variance for the effects of sex and indicati

154 Analysis of variance found a significant effect of age b

155 , and healthy control subjects (multivariate analysis of variance group effect: F6,102 = 5.6, p < .00

156 t (n=15; P<1.33E-04, 1-way repeated measures analysis of variance); >90% were directionally consisten

157 tween-group changes were assessed by one-way analysis of variance in our modified intention-to-treat

158 ly expressed genes (DEGs) were identified by analysis of variance, including covariates for RNA quali

159 Multivariate analysis of variance indicated significantly higher TSPO

160 Finally, the analysis of variance indicates that the effect of light

161 cal significance was determined with one-way analysis of variance (Kruskal-Wallis and Friedman tests)

162 tistically at baseline and 3 and 6 months by analysis of variance, Kruskal-Wallis, Mann-Whitney U, an

163 The multivariate analysis of variance (MANOVA) did not provide any within

164 responses were analyzed by the multivariate analysis of variance (MANOVA) protocol, a statistical to

165 We used a multivariate analysis of variance (MANOVA) to compare the cognitive p

166 ts were studied in detail using Multivariate Analysis of Variance (MANOVA) with the hypothesis that t

167 tended Simes procedure (TATES), multivariate analysis of variance (MANOVA), and joint model of multip

168 ltiple phenotypes including the multivariate analysis of variance (MANOVA), the principal component a

169 (2)) were further analysed with multivariate analysis of variance (MANOVA).

170 re estimated, and the multireader, multicase analysis of variance method was used to compare reconstr

171 ses involved a mixed model repeated-measures analysis of variance (MMRM ANOVA).

172 A 1-way analysis of variance model and the Tukey-Kramer multiple

173 Smoothing spline analysis of variance models and the contrast cycle diffe

174 tient groups showed significant impairments (analysis of variance models, all P < 0.05) of facial emo

175 n tau concentrations were determined through analysis of variance models, and area under the receiver

176 ors were assessed using linear regression or analysis of variance models.

177 Data were analyzed using analysis of variance, multilevel modeling, and survival

178 The repeated-measures analysis of variance of absolute error by lens power for

179 ar regression analysis and repeated-measures analysis of variance of all NIH grants awarded to depart

180 al method - univariate repeated-measurements analysis of variance of joint angle minima and maxima.

181 ferences between categories were compared by analysis of variance of logit-transformed percentage of

182 ates were evaluated using a repeated-measure analysis of variance or a nonparametric Friedman test.

183 One-way analysis of variance or Kruskal-Wallis test with post ho

184 dual genera were calculated by permutational analysis of variance or linear regression, respectively.

185 For group comparisons, analysis of variance or the Kruskal-Wallis test was perf

186 orrelated by using an independent t test and analysis of variance (P < .05).

187 of zones of inhibition were calculated, and analysis of variance (P <0.05) was used to determine whe

188 asses or a decrease in needle gauge (two-way analysis of variance, P < .0001 for both).

189 s increased significantly from 2002 to 2012 (analysis of variance, P < .001).

190 ss quartiles were statistically significant (analysis of variance, P < 0.001, P < 0.001, and P = 0.01

191 s, 6.5 kPa +/- 1.2; adults, 7.8 kPa +/- 1.2; analysis of variance, P = .0003) but not at 28 Hz (child

192 in children and adolescents than in adults (analysis of variance, P = .0009).

193 s, 2.2 kPa +/- 0.2; adults, 2.6 kPa +/- 0.3; analysis of variance, P = .009) and 84 Hz (children, 5.6

194 n measurements of both T2 (repeated measures analysis of variance, P = .025) and T2* (P < .001).

195 ly affect quantitative density measurements (analysis of variance, P = .37 to P = .75), with percent

196 s, 1.3 kPa +/- 0.3; adults, 1.2 kPa +/- 0.2; analysis of variance, P = .40).

197 here was significantly decreased wound area (Analysis of variance, p = 0.000) in the photobiomodulati

198 tensiometeric property (stress maximal load, Analysis of variance, p = 0.000) in the photobiomodulati

199 y 16, there was significantly decreased CFU (Analysis of variance, p = 0.001) in the photobiomodulati

200 ficantly modulated inflammatory response in (Analysis of variance, p = 0.049) in the photobiomodulati

201 One-way analysis of variance, paired t tests, concordance and Bl

202 Student t tests, one-way analysis of variance, Pearson correlation, and multivari

203 ed t tests, Wilcoxon rank sum tests, one-way analysis of variance, Pearson correlation, and Spearman

204 Results were analyzed using a 1-way analysis of variance, Pearson's chi-square test, and sim

205 Using permutational multivariate analysis of variance (PERMANOVA) and nonmetric multidime

206 analysis (NMDS), permutational multivariate analysis of variance (PERMANOVA) and random forest model

207 ar regression and permutational multivariate analysis of variance (PERMANOVA) considered variations i

208 s the commonly-used Permutation Multivariate Analysis of Variance (PERMANOVA) test by allowing flexib

209 among the groups (permutational multivariate analysis of variance [PERMANOVA] P < 0.001).

210 Permutational non-Euclidean analysis of variance, PERMANOVA, is routinely used in ex

211 sted association, permutational multivariate analysis of variance, PERMANOVA-S).

212 GS >/= 4 + 3 tumors by using paired t tests, analysis of variance, receiver operating characteristic

213 between groups were determined using a 2-way analysis of variance repeated measure (n>/=4; P<0.05).

214 Analysis of variance revealed a full site by variety nes

215 of the cingulum bundle, a repeated measures analysis of variance revealed a main effect of group (OC

216 A multivariate analysis of variance revealed a significant rearing effe

217 One-way analysis of variance revealed a statistically significan

218 The analysis of variance revealed significant variation amon

219 Analysis of variance revealed that of the 87 metabolites

220 Repeated measures analysis of variance (RM-ANOVA) showed that chicken litt

221 MT was then analyzed using Repeated Measures Analysis of Variance (RM-ANOVA).

222 2 +/- 1 (SBIR), and 4.6 +/- 1 (FBP); two-way analysis of variance showed a difference on the basis of

223 Analysis of variance showed no difference between the su

224 A multivariate, repeated-measures analysis of variance showed no effect of stimulation sta

225 Analysis of variance showed statistically significant di

226 Expression was analyzed by 1-way analysis of variance (significance at P < .01), unsuperv

227 Expression changes were evaluated by analysis of variance (significant P value < .05), hierar

228 Mann-Whitney U, chi(2), and analysis of variance statistics were used.

229 groups (n = 8 to 9 per group) were compared (analysis of variance, t test) at days 0, 7, 14, and 28 f

230 Data were analyzed by using analysis of variance, t test, or chi(2) test.

231 Statistical testing included analysis of variance, t tests, and permutation tests.

232 e hydroclimate response, analyzed through an analysis of variance technique, suggests that the choice

233 Analysis of variance techniques were used to compare gro

234 The repeated-measures analysis of variance test was used to determine the chan

235 as followed by nonparametric (Kruskal-Wallis analysis of variance) testing of associations between AD

236 Spearman correlation and analysis of variance tests were applied.

237 ys and analyzing the results through a 2-way analysis of variance, the cookies incorporated with spra

238 Groups were compared with analysis of variance, the Mann-Whitney U test, or the t

239 identified trajectory classes were tested by analysis of variance.Three body mass index (BMI; in kg/m

240 38 were routinely isolated and subjected to analysis of variance to assess these NFC juices.

241 formed t tests to compare DeltaA and DeltaV, analysis of variance to compare DeltaA and DeltaV across

242 isher exact tests and with repeated-measures analysis of variance to compare groups on the rate of ch

243 oss n visual fields (n = 3 to 6) and used an analysis of variance to determine if incorporating the f

244 We employed meta-analysis of variance to investigate interindividual vari

245 escriptive statistics, Spearman correlation, analysis of variance, two-sample t test, and intraclass

246 isita indices and Permutational Multivariate Analysis of Variance Using Distance Matrices (PERMANOVA)

247 A 2x2 analysis of variance using voxel-wise subsampling permut

248 An analysis of variance was conducted of overall OSCE profe

249 Analysis of variance was performed to evaluate linear, q

250 Analysis of variance was performed to evaluate the signa

251 hallenge, in which a 2 x 2 repeated-measures analysis of variance was performed with a drug (methylen

252 Repeated measures analysis of variance was performed, followed by post hoc

253 ed uptake values (SUVs) were determined, and analysis of variance was performed, with group (smoker v

254 One-way analysis of variance was used by assessing the magnitude

255 Analysis of variance was used for comparison of lipid pr

256 Two-way analysis of variance was used for differentiating accura

257 Analysis of variance was used to assess differences betw

258 A nonparametric version of multivariate analysis of variance was used to assess safety outcome m

259 Repeated-measures analysis of variance was used to assess time and group i

260 ation coefficient (r), two-sample t test, or analysis of variance was used to assess univariable asso

261 depression, obtained at the end of 2 weeks; analysis of variance was used to compare active with sha

262 Two-way analysis of variance was used to compare groups.

263 Two-way analysis of variance was used to compare labeling condit

264 Analysis of variance was used to compare readmission pen

265 A one-way analysis of variance was used to compare the simulated e

266 Two-way analysis of variance was used to compare the time course

267 Analysis of variance was used to compare vital signs.

268 Upon log transformation, repeated measures analysis of variance was used to detect groupwise region

269 Analysis of variance was used to determine differences f

270 The repeated measures analysis of variance was used to examine the effects of

271 Repeated measures analysis of variance was used to test efficacy of the in

272 Analysis of variance was used to test the overall betwee

273 Through one-way analysis of variance we present mean response rate per s

274 igned rank test, paired t test, and Friedman analysis of variance were conducted to evaluate differen

275 tailed Student t tests and repeated-measures analysis of variance were used for statistical analysis.

276 ncordance correlation coefficient (CCC), and analysis of variance were used for the first, second, an

277 test, the Mann-Whitney test, and the one-way analysis of variance were used to compare ADCs between p

278 lynomial model where regression analysis and analysis of variance were used to determine model fitnes

279 Multivariable linear regression and analysis of variance were used to evaluate individual co

280 exact, chi(2), and Kruskal-Wallis tests and analysis of variance were used to test correlation betwe

281 Pearson's chi-square and analysis of variance were used to test treatment effect

282 ic means of adjusted log-transformed values (analysis of variance) were 18.99 ng.h.L (TacHexal) and 2

283 ed-measures analysis of variance and one-way analysis of variance, whereas correlations were quantifi

284 ere compared by using a two-way mixed-design analysis of variance with a Bonferroni posthoc test.

285 Next, a two-way mixed analysis of variance with between-subject factor 'outcom

286 meters were statistically evaluated by using analysis of variance with Bonferroni correction.

287 eriodontal disease status were determined by analysis of variance with Bonferroni correction.

288 n performance were assessed by using two-way analysis of variance with Bonferroni correction.

289 compared by using two-way repeated measures analysis of variance with Bonferroni post hoc correction

290 t, border, and remote regions by using Welch analysis of variance with Games-Howell post hoc test for

291 crosis area were compared by using a one-way analysis of variance with post hoc analysis for statisti

292 analyzed by using repeated measures one-way analysis of variance with post hoc pair-wise comparisons

293 Repeated measures analysis of variance with post hoc paired t test and Ski

294 rve (AUC) were calculated and compared using analysis of variance with post hoc Tukey test at P </= 0

295 res were evaluated using a repeated-measures analysis of variance with post hoc two-tailed paired t-t

296 T test, chi(2), and one-way analysis of variance with posthoc Bonferroni correction

297 subpopulations were tested by using one-way analysis of variance with the Dunnett test, and correlat

298 ta were analyzed by using one-way or two-way analysis of variance with the Sidak or Tukey multiple co

299 Pearson correlation and analysis of variance with Tukey-Kramer post hoc correcti

300 quantitatively by using the Friedman two-way analysis of variance, with P < .05 considered to indicat