ライフサイエンスコーパス: analysis of variance

1 Data were analyzed statistically (P <0.05, analysis of variance).

2 ly greater decrease in GCF volume (P = 0.03, analysis of variance).

3 tly reduced compared with group C, P <0.001 (analysis of variance).

4 e matter , and GM gray matter were compared (analysis of variance).

5 ,0; 5.8), saline: 4.4 (3.5; 5.8) (P = 0.016, analysis of variance).

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

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

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

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

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

11 each group and statistically compared using analysis of variance.

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

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

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

15 Variables were compared by using analysis of variance.

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

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

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

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

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

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

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

23 bstruction and degree of hydronephrosis with analysis of variance.

24 significance was assessed with nonparametric analysis of variance.

25 r 2012 and October 2013 by repeated-measures analysis of variance.

26 lysed using principal component analysis and analysis of variance.

27 rns of the nonexposed group by performing an analysis of variance.

28 e analyzed by using statistical graphics and analysis of variance.

29 Data were analyzed by using one-way analysis of variance.

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

31 Data were examined by non-parametric analysis of variance.

32 ata were analyzed by using two-way factorial analysis of variance.

33 ic shock corticosteroid group (n = 70) using analysis of variance.

34 linical data and histopathologic analysis by analysis of variance.

35 Occlusion break surge was analyzed by 2-way analysis of variance.

36 T changes were analyzed by repeated-measures analysis of variance.

37 bone crest and statistically analyzed using analysis of variance.

38 ent t test, chi(2) analysis, and mixed-model analysis of variance.

39 the third month were analyzed using multiway analysis of variance.

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

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

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

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

44 commonly applied variable selection method - analysis of variance.

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

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

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

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

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

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

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

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

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

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 pg/mL) and among HT use (never, ever) using analysis of variance and analysis of covariance.

57 al analysis was performed using multivariate analysis of variance and bivariate correlation.

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

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

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

61 used to compare categorical values; one-way analysis of variance and Kruskal-Wallis tests were used

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

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

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

65 data were analyzed through repeated-measures analysis of variance and multiple comparisons Tukey test

66 ing whole-brain and region of interest-based analysis of variance and multiple-regression analyses.

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

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

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

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

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

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

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

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

75 Data were analyzed using repeated-measures analysis of variance and presented as means with 95% con

76 Analysis of variance and regression analyses were perfor

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

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

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

80 Analysis of variance and t test were used for statistica

81 ective review and statistical analysis using analysis of variance and the Fisher exact test.

82 tistical analysis was conducted with one-way analysis of variance and the Student t test.

83 Statistical analysis was performed with analysis of variance and the Tukey honestly significant

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

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

86 al differences were assessed using a one-way analysis of variance and Tukey multiple-comparison inter

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 Results were analyzed with analysis of variance and Tukey's test (alpha = 0.05).

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

91 Friedman analysis of variance and Wilcoxon matched-pairs tests we

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

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

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

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

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

97 le were compared by using t test and one-way analysis of variance, and receiver operating characteris

98 A combination of descriptive statistics, analysis of variance, and t tests was used for statistic

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

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

101 ses were performed by using t tests, one-way analysis of variance, and Wilcoxon and Mann-Whitney test

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

103 The results of analysis of variance (ANOVA) and correlation showed that

104 ilot test locations were analysed using both analysis of variance (ANOVA) and heritability adjusted-g

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

106 Analysis of variance (ANOVA) and receiver operating char

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 tatistical analysis was by repeated-measures analysis of variance (ANOVA) for intra-observer repeatab

113 An analysis of variance (ANOVA) reveals significant differe

114 type of compound) were evaluated through an analysis of variance (ANOVA) tool revealing the next ste

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

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

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

118 antly increased with E or E+P treatment (all analysis of variance (ANOVA), P<0.01).

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

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

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

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

123 tribution was determined using a statistical analysis of variance (ANOVA).

124 n fDMs were analyzed using repeated measures analysis of variance (ANOVA).

125 )) matrix and quantitatively assessed by the analysis of variance (ANOVA).

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

127 whereas increasing meal size did not (2-way analysis of variance [ANOVA] size versus frequency P = 0

128 Analysis of variance applied to the dataset indicated th

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

130 Analysis of variance at the 95% confidence interval was

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

132 s were analyzed using Kruskal-Wallis one-way analysis of variance by ranks.

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

134 re shown (Kruskal-Wallis nonparametric 1-way analysis of variance, chi = 21.5, P < 0.001).

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

136 strength and vendor, with repeated-measures analysis of variance; coefficients of variation (CVs) an

137 Analysis of variance compared differences among conditio

138 Analysis of variance compared performance across differe

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

140 Analysis of variance demonstrated a significant differen

141 %-36% except anterior cingulate cortex, 24%; analysis of variance, effect of diagnosis: P < .001 to P

142 ain regions analyzed (mean 32%, multivariate analysis of variance, F(7),(2)(4) = 3.67, p = .008).

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

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

145 in all brain regions examined (multivariate analysis of variance, F15,23 = 4.5 [P = .001]).

146 Analysis of variance findings for key mRNA and immunohis

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

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

149 tically compared by using chi(2) and one-way analysis of variance followed by Tukey honestly signific

150 s test, followed by a post hoc Dunn test and analysis of variance, followed by a Tukey test using a 5

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

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

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

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

155 variables were analyzed using multivariable analysis of variance for repeated measures.

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

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

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

159 by 16% compared with menopause (multivariate analysis of variance, group effect, F16,94 = 3.03; P < .

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

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

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

163 Evaluation of responses by analysis of variance indicated high coefficient determin

164 Multivariate analysis of variance indicated significantly higher TSPO

165 Analysis of variance, Kaplan-Meier method, and Cox multi

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

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

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

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

170 yzed by a statistical tool, the multivariate analysis of variance (MANOVA) protocol.

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

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

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

174 FC and ACC was analyzed using a multivariate analysis of variance (MANOVA).

175 and non-AIDS chronic patients (multivariate analysis of variance [MANOVA], P = 0.12), indicating tha

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

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

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

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

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

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

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

183 The repeated measures analysis of variance of all parameters analyzed indicate

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

185 Analysis of variance of structural connectome metrics an

186 Analysis of variance of the descriptive data confirmed t

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

188 Data were statistically analyzed (analysis of variance or Kruskal-Wallis, P <0.05).

189 protocols and algorithms were analyzed with analysis of variance or Welch test depending on data dis

190 essed 9% to 91% (mean = 55% +/- 17%) of SCI (analysis of variance P = 0.025).

191 (almost 8-fold increase from 10 to 40 weeks, analysis of variance P<0.001).

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

207 is distance-based permutational multivariate analysis of variance; PERMANOVA), changes in Asthma Cont

208 Data were compared by factorial analysis of variance, post hoc Tukey, and Pearson correl

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

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

211 Analysis of variance revealed a full site by variety nes

212 Analysis of variance revealed no group differences in th

213 Analysis of variance revealed that of the 87 metabolites

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

215 valuated with repeated-measures multivariate analysis of variance separately for R2* mapping and phas

216 Repeated-measures analysis of variance showed an association between druse

217 Analysis of variance showed no difference between the su

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

219 The analysis of variance showed significant differences amon

220 Repeated-measures analysis of variance showed that demineralization period

221 Analysis of variance showed that each RSA parameter was

222 Analysis of variance showed that seed coat colour varied

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

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

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

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

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

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

229 An analysis of variance test followed by a post hoc test wa

230 luding t-tests, ANOVA and the Kruskal-Wallis analysis of variance test.

231 re compared by using both paired t tests and analysis of variance tests at the 95% significance level

232 Two-sample t tests or analysis of variance tests were performed to assess for

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

234 istical analysis was performed using a 1-way analysis of variance, the Student t test, chi test, and

235 sis was conducted by using repeated measures analysis of variance, the Student t test, chi(2) test, a

236 Data were analyzed by two-way analysis of variance, the Tukey honestly significant dif

237 istical analysis (the Box-Behnken design and analysis of variance); this is based on the deactivation

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

239 permutation-based extension of multivariate analysis of variance to a matrix of pairwise distances,

240 ta were analyzed as intention to treat using analysis of variance to assess intergroup differences.

241 38 were routinely isolated and subjected to analysis of variance to assess these NFC juices.

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

243 analysis (GSEA), resampling statistics, and analysis of variance to gain further insight into the ra

244 Data were statistically analyzed (analysis of variance; Tukey test, P <0.05).

245 Data were statistically analyzed (analysis of variance; Tukey, P <0.05).

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

247 Repeated measures analysis of variance, unpaired t tests, and correlation

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

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

250 An analysis of variance was conducted of overall OSCE profe

251 Multivariate analysis of variance was performed to assess the effect

252 Analysis of variance was performed to evaluate linear, q

253 Analysis of variance was performed to evaluate significa

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

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

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

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

258 Analysis of variance was used for comparison of lipid pr

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

260 Analysis of variance was used to assess differences betw

261 Analysis of variance was used to assess factors impactin

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

263 Analysis of variance was used to assess the number of st

264 A nonparametric version of multivariate analysis of variance was used to assess the safety outco

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

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

267 Analysis of variance was used to compare readmission pen

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

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

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

271 Analysis of variance was used to determine differences f

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

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

274 Analysis of variance was used to test the overall betwee

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

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

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

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

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

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

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

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

283 Repeated-measures analysis of variance with a Tukey test was applied to de

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

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

286 Analysis of variance with Bonferroni-corrected pairwise

287 Analysis of variance with false discovery rate correctio

288 Data were analyzed by using analysis of variance with multiple comparisons, t tests,

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

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

291 ers were compared (repeated-measures one-way analysis of variance with post hoc pairwise comparisons)

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

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

294 ment were analyzed for significance by using analysis of variance with repeated measures.

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

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

297 -week periods, and we used repeated measures analysis of variance with Tukey pairwise multiple compar

298 were compared by using unpaired t tests and analysis of variance with Tukey post hoc analysis.

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