ライフサイエンスコーパス: ANOVA

1 ANOVA analysis indicates differences between samples fro

2 ANOVA and Bland-Altman analyses were used to determine l

3 ANOVA and Fisher's exact test were conducted to examine

4 ANOVA and multiple regression equations were used in the

5 ANOVA and post hoc t-test showed significant increases i

6 ANOVA and post-hoc analyses revealed that OBP had signif

7 ANOVA assessed the difference among cBL and rBL at diffe

8 ANOVA comparison and adjusted multinomial logistic regre

9 ANOVA confirmed that subjectively rated hunger (P = 0.56

10 ANOVA followed by Newman-Keuls post-hoc analyses were us

11 ANOVA indicated significant differences (P < .05) betwee

12 ANOVA results demonstrate that a learning effect disting

13 ANOVA results showed that concentration of ethanol and t

14 ANOVA results showed that the experimental data were sat

15 ANOVA revealed a statistically significant difference be

16 ANOVA revealed the significant difference of these 24 el

17 ANOVA showed no systematic differences between groups in

18 ANOVA subgroup analysis indicated that variations in eac

19 ANOVA tests resulted in significant differences (P < 0.0

20 ANOVA was conducted for each frequency band with the fol

21 ANOVA was used to test for group mean differences, varia

22 ANOVA with Bonferroni correction showed that plasma CoQ

23 ANOVA-simultaneous component analysis (ASCA) of the ring

24 ANOVAs with repeated-measures showed significant effects

25 tion of significant differences (p < 0.0001; ANOVA) in CD34+ cells mechanical properties throughout t

26 ion (quinolinic acid: F = 21.027, p < 0.001 [ANOVA]; BTP: F = 6.792, p < 0.01 [ANOVA]).

27 also had enlarged colon volumes (P < 0.001, ANOVA) and delayed colonic transit times (P = 0.01, Krus

28 uced significant growth inhibition (p=0.001, ANOVA) and enhanced median survival to 27 days over cont

29 tumor growth at study conclusion, P < 0.001; ANOVA on ranks with Dunn test).

30 nt as compared with control mice (P = 0.002; ANOVA on ranks with Dunn test), while standard gemcitabi

31 recorded with the DTL electrode (p < 0.005, ANOVA).

32 donepezil standard uptake values (P = 0.008, ANOVA).

33 p < 0.001 [ANOVA]; BTP: F = 6.792, p < 0.01 [ANOVA]).

34 at a false discovery rate of less than 0.05 (ANOVA), and 15 type I and type III interferon genes were

35 ere prepared to specified lengths (P < 0.05, ANOVA) and proved consistent in size.

36 cles were significantly different (p < 0.05, ANOVA) when performing the same digit movement in five d

37 or (IL-1R)-knockout mouse corneas (P < 0.05, ANOVA).

38 here was no significant difference (P > 0.1; ANOVA) in the mean preintervention serum 25(OH)D in the

39 f iRBD patients had normal scans (P < 10-13, ANOVA).

40 ted using a voxel-wise, mixed-effects, 2 x 2 ANOVA.

41 T-tests, chi (2), ANOVA and Pearson Correlation tests were performed, with

42 11.6 mL/kg (5.2); placebo, 17.1 mL/kg (14.3; ANOVA P=0.02).

43 whose milk allergy resolved (eta(2) = 0.43; ANOVA P = .034).

44 ean MIBG heart:mediastinum ratios (P < 10-5, ANOVA) and colon 11C-donepezil standard uptake values (P

45 +/- 2.4%; feature-tracking: -28.7 +/- 4.8%) (ANOVA with Tukey post-hoc, F-value 279.93, p < 0.01).

46 ulated differences between MS and HC using a ANOVA and associations with disability using linear regr

47 their respective age-matched controls (ACC; ANOVA main effect of diagnosis: F(1,58) = 0.407, p = 0.5

48 es showed nonsignificant lipid accumulation (ANOVA, P = 0.62).

49 In addition, ANOVA analysis showed that, for all the electrodes with

50 nd the smallest amount of stromal admixture (ANOVA p < 2.2e-16).

51 pic for all age groups but reduced with age (ANOVA, p < 0.001).

52 were better than 0.0 and improved with age (ANOVA, p < 0.001).

53 UBE3A (Parkin) increased with E or E+P (all ANOVA, P<0.003).

54 actin increased with E or E+P treatment (all ANOVA, P<0.03).

55 antly increased with E or E+P treatment (all ANOVA, P<0.05).

56 An ANOVA test for all the probes showed that at a 95% level

57 A design of experiments strategy and an ANOVA analysis are used to establish the effect of the a

58 Finally, as revealed by an ANOVA test, some volatiles differed markedly in content

59 We used an ANOVA to test for the heterogeneity of the effect on bir

60 levels were compared between groups using an ANOVA and adjusted for multiple comparisons using false

61 ) and ANOVA-simultaneous component analysis (ANOVA-SCA), stages 2 and 3 of reproduction show similari

62 Statistical analysis (ANOVA with post hoc two-sided t tests, P < 0.05) reveale

63 n) and the results of a univariate analysis (ANOVA), allowed the identification of potential volatile

64 hrough the application of variance analysis (ANOVA) factors critical to removing of lead were identif

65 nalysis was done by Cox regression analysis, ANOVA, and chi(2).

66 aluated using multivariate data analysis and ANOVA.

67 ere analysed using Pearson's coefficient and ANOVA.

68 Descriptive statistics, correlations, and ANOVA were performed to compare surgeon and patient fact

69 t-squares-discriminant analysis (PLS-DA) and ANOVA-simultaneous component analysis (ANOVA-SCA), stage

70 VA), principal component analysis (PCA), and ANOVA simultaneous component analysis (ASCA), were used

71 cohort-specific classifiers by both SRVS and ANOVA methods are capable of providing significantly hig

72 Pearson's chi(2) test and ANOVA were used to compare tuberculosis treatment outcom

73 Data were analyzed using t tests and ANOVA.

74 assessed by chi-square, Mann-Whitney U, and ANOVA testing.

75 erformed through Fisher, Kruskal-Wallis, and ANOVA tests and a generalized estimation equations metho

76 Different statistical approaches (ANOVA, Principal Components Analysis, Cluster Analysis)

77 upled with other statistical methods such as ANOVA, is demonstrated on altogether twelve case studies

78 A 2-way balanced ANOVA was used to determine whether differences in micro

79 Anyway, both ANOVA and PCA analyses have highlighted the low influenc

80 and ADAM10 (alpha-secretase) increased (both ANOVA, P<0.02) but PSEN1 (presenilin1) decreased (ANOVA,

81 le subtype showed highest mutational burden (ANOVA p < 0.01) and the smallest amount of stromal admix

82 an difference, -6.7 mm Hg; global P=0.038 by ANOVA, adjusted P=0.043), no significant difference was

83 +/- standard error of the mean, p = 0.040 by ANOVA).

84 26 weeks (non-inferiority limit of 0.4%) by ANOVA in an intent-to-treat analysis (full analysis set)

85 Results were analysed by ANOVA and the optimal condition was determined through r

86 Data were analysed by ANOVA and Tukey's post-hoc tests or a linear mixed-effec

87 Differences among lines were analyzed by ANOVA and shown to be significant (P <0.05), for BW, WC,

88 The experimental data were analyzed by ANOVA method and a well-predictive, second order polynom

89 the effects of rearing on H3K4me3 binding by ANOVA.

90 ormed probes were significantly different by ANOVA with 0.01 q-value threshold reduced to zero signif

91 d with respect to baseline were evaluated by ANOVA.

92 lesions was significantly related to PSA by ANOVA, but there was a large overlap in the PSA values f

93 s; and responsiveness to change over time by ANOVA.

94 analysed by the chemometric technique called ANOVA-simultaneous component analysis (ASCA).

95 o variation in plasma exosome concentration (ANOVA, P < 0.05).

96 , P<0.02) but PSEN1 (presenilin1) decreased (ANOVA, P<0.02) with treatment.

97 A 3-way mixed-design ANOVA was performed to determine the main and interactio

98 es with early, moderate, and severe disease (ANOVA and linear regressions of thickness on VFMD).

99 assessed using a whole-brain, mixed-effects ANOVA with correction for multiple comparisons at curren

100 For overall injection efficiency, ANOVA tests indicated that stiffness was highly signific

101 tribution fits TnSeq data better than either ANOVA or a Negative Binomial (in a generalized linear mo

102 DP, ADC, and PRM gas trapping and emphysema (ANOVA, P < .001) measurements were significantly differe

103 es, such as those provided by the extendable ANOVA framework.

104 ween surveys were compared by using 1-factor ANOVA.

105 asting triglyceride concentrations (2-factor ANOVA) in plasma (P = 0.023) and large very-low-density

106 Two-factor ANOVA was used to examine the difference between protein

107 until week 16 (p < 0.0001, two-way factorial ANOVA).

108 until week 16 (p < 0.0001, two-way factorial ANOVA).

109 altered in copepods exposed to nylon fibers (ANOVA, P < 0.01) resulting in a nonsignificant 40% decre

110 r supported the specificity of this finding (ANOVA P = 0.02; pairwise t-tests P = 0.03 and P = 0.003,

111 icles ingested and retained more frequently (ANOVA, P < 0.01).

112 Statistic analysis included ANOVA, Wilcoxon test and multivariate regression analysi

113 tients who received more than 12 injections (ANOVA P = .001).

114 In contrast, non-rescaled measures - like ANOVA - find fewer interactions when single-stressor eff

115 Multivariate repeated-measure ANOVA for mixed models was employed.

116 Data were analyzed by repeated measures ANOVA and post hoc Bonferroni test.A total of 19 patient

117 eta by 7.0-fold (p < 0.05, repeated measures ANOVA on ranks).

118 Repeated measures ANOVA revealed that the percentage of voxels with increa

119 Repeated measures ANOVA revealed time-by-victimization interactions on lef

120 Repeated measures ANOVA was used to asses within and between group differe

121 Repeated measures ANOVA was used to compare any possible differences betwe

122 n coefficients and one-way repeated measures ANOVA were used to observe the systolic, diastolic, and

123 conditions, assessed with repeated measures ANOVA, in all patients who completed assessments during

124 Repeated measures ANOVA, network analysis, and enrichment analysis methods

125 t 4, 8, and 12 weeks using repeated measures ANOVA.

126 and compared to sham using repeated measures ANOVA.

127 ficance using a t-test and repeated measures ANOVA.

128 d data were compared using repeated measures ANOVA.

129 Two-way mixed repeated-measures ANOVA and longitudinal regression models were applied.

130 e tested with the use of a repeated-measures ANOVA and paired sample t tests.

131 Groups were compared with repeated-measures ANOVA for fractional anisotropy (FA), and magnetization

132 A repeated-measures ANOVA showed there was no significant (P = 0.2) time x v

133 change values; and used a repeated-measures ANOVA to assess pre- to postintervention changes.

134 Repeated-measures ANOVA was performed to determine whether significant dif

135 Repeated-measures ANOVA was used to test for a racextime effect on measure

136 t metabolite identified by repeated-measures ANOVA, followed by eicosapentaenoate (P-interaction = 4.

137 l between reporting years (repeated-measures ANOVA, P = 0.985).

138 Repeated measures ANOVAs were performed on the: (a) coefficient of variati

139 rences were analysed using repeated measures ANOVAs.

140 Repeated-measures ANOVAs indicated a main effect of intensity for both ano

141 Two-way mixed ANOVA evaluated pain and satisfaction measures between g

142 criptive statistics, correlations, and mixed ANOVAs were performed to assess relationships between am

143 repeated-measures analysis of variance (MMRM ANOVA).

144 xamined with the use of a linear mixed-model ANOVA.

145 d calcium were compared by using mixed-model ANOVA.

146 Multifactor ANOVA revealed that levels of those volatiles changed si

147 Multifactor ANOVA, considering the content of total anthocyanins as

148 re subjected to evaluation using multifactor ANOVA and principal component analysis (PCA), both showi

149 Data were evaluated using multifactorial-ANOVA, response surface analysis and Principal Component

150 distribution-Wachter's MANOVA (multivariate ANOVA) spectral distribution, a phenomenon that was prev

151 were assessed by permutational multivariate ANOVA and hurdle regression models using the negative bi

152 groups (adjusted permutational multivariate ANOVA, P = 0.010).

153 paring them using permutational multivariate ANOVA.

154 vealed outliers in performance, and a nested ANOVA model revealed the extent to which all metrics or

155 We performed an adjusted 2-factor nested ANOVA mixed-effects model procedure on the postintervent

156 influence on LLGR were analyzed by means of ANOVA and the Levene test of homogeneity.

157 The results of ANOVA showed that variety, location, production year, an

158 data were compared by the Student t test or ANOVA, and categoric variables were compared by the chi(

159 tween conditions using an unpaired t-test or ANOVA, as appropriate.

160 cal analysis was performed with t testing or ANOVA.

161 lis test to eQTLs detected by the parametric ANOVA and linear model methods.

162 larval age and size of polystyrene particle (ANOVA, P < 0.01), and surface properties of the plastic,

163 compared to non-AMD and early AMD patients (ANOVA, P < .01).

164 pairwise resampling (and vastly out-performs ANOVA), but it also identifies additional genes where va

165 d using Spearman correlations, permutational ANOVAs, and multivariate analyses with linear models, re

166 test, address reduction, stable persistence, ANOVA, and F24.

167 icant 40% decrease in algal ingestion rates (ANOVA, P = 0.07), and copepods exposed to nylon granules

168 ficantly more reproducible than an OECD RBT (ANOVA, P < 0.05), with more consistent rates and extent

169 Results: ANOVA revealed significant group differences in lateral

170 Repeated measures analysis of variance (RM-ANOVA) showed that chicken litter leachate stimulated ph

171 g Repeated Measures Analysis of Variance (RM-ANOVA).

172 significant variables evaluated by a second ANOVA.

173 v increased significantly after spaceflight (ANOVA, P = 0.007).

174 an lifetime is modeled with smoothing-spline ANOVA given the covariates information including sex, li

175 s were analysed using parametric statistics (ANOVA).

176 Data were analyzed by parametric test (ANOVA) with Tukey post-hoc test (P < 0.05).

177 uding Fisher's Exact Test, Student's t-test, ANOVA, non-parametric tests, linear regression, logistic

178 T testing, ANOVA, and regression analyses are reviewed.

179 Paired t-tests, ANOVA and generalized-estimating-equations models were u

180 using statistical methods including t-tests, ANOVA and the Kruskal-Wallis analysis of variance test.

181 We find that ANOVA, F24, and JTK_CYCLE consistently outperform the ot

182 analysis of variance (ANOVA) - and show that ANOVA assumptions are often violated and have inherent l

183 The ANOVA and response surfaces analysis showed that the mos

184 The ANOVA results confirmed that all prices had risen over t

185 The ANOVA test was used to compare the refractive results am

186 )) after 1 week and 3 weeks of TA treatment (ANOVA P < 0.01, P < 0.001, respectively).

187 mTLE) on the volume of the left (univariate ANOVA F=29.6, p<0.001) and right (F=8.3, p<0.001) entorh

188 ion of a dual criterion based on univariate (ANOVA) and multivariate analyses (OPLS-DA) allowed us to

189 A dual criterion based on univariate (ANOVA) and multivariate analysis (PLS-DA) through the va

190 Data were analysed using ANOVA at p=0.05.

191 ces between the 2 groups were analyzed using ANOVA, Wilcoxon Rank Sum, chi, and Fisher Exact tests.

192 ions across diet groups were tested by using ANOVA, and a false discovery rate-controlling procedure

193 ically, and differences were tested by using ANOVA.

194 Statistical analyses were conducted using ANOVA with a post hoc analysis of Fisher's LSD.

195 en alpha- and beta-cells were detected using ANOVA and in silico replications of mouse and human isle

196 ferences between means were determined using ANOVA and least significant difference with hay (5), bal

197 tatistical significance was determined using ANOVA.

198 Data were evaluated using ANOVA, post-hoc Tukey and PCA.

199 Confidence Interval (CI), the p-value using ANOVA have been computed.

200 nvironmental and anthropogenic drivers using ANOVAs.

201 and compared between cognitive groups using ANOVAs, adjusted for age, gender, and body mass index.

202 rapy (R(2)Y=0.67; Q(2)=0.54; cross-validated ANOVA P=2.6x10(-5)).

203 eeks (R(2)Y=0.65; Q(2)=0.61; cross-validated ANOVA P=5.4x10(-8)).

204 rapy (R(2)Y=0.74; Q(2)=0.66; cross-validated ANOVA P=7.0x10(-8)) and combination therapy (R(2)Y=0.67;

205 ML-IRIS latency; (2) an analysis of variance ANOVA to investigate their impact on IRIS duration; and

206 compared using analysis of normal variance (ANOVA).

207 used existing method - analysis of variance (ANOVA) - and show that ANOVA assumptions are often viola

208 The results of analysis of variance (ANOVA) and correlation showed that the second-order poly

209 re analysed using both analysis of variance (ANOVA) and heritability adjusted-genotype main effect pl

210 Analysis of variance (ANOVA) and kinetic modeling were also employed.

211 ults were evaluated by analysis of variance (ANOVA) and principal component analysis (PCA).

212 Analysis of variance (ANOVA) and receiver operating characteristic (ROC) analy

213 ly analyzed by one-way analysis of variance (ANOVA) and Student Newman Keuls's post hoc test at alpha

214 techniques along with Analysis of variance (ANOVA) and Taguchi optimization method for optimizing th

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

216 subjected to a one-way analysis of variance (ANOVA) and, if significant differences were revealed, th

217 requencies, we applied ANalysis Of VAriance (ANOVA) based on a linear mixed model and found that cons

218 Univariate statistical analysis of variance (ANOVA) established that individual patterns positively c

219 s by repeated-measures analysis of variance (ANOVA) for intra-observer repeatability.

220 e developed a Bayesian analysis of variance (ANOVA) model that decomposes these 8mer data into backgr

221 Two-way analysis of variance (ANOVA) results reveal that most metabolites show signifi

222 An analysis of variance (ANOVA) reveals significant differences (p < 0.05) among

223 The results from analysis of variance (ANOVA) showed that similar numbers of compounds from tar

224 2 masked graders, and analysis of variance (ANOVA) tests were used to compare areas of RNP over time

225 e evaluated through an analysis of variance (ANOVA) tool revealing the next steps toward optimizing t

226 An analysis of variance (ANOVA) was performed to compare mean IOP values.

227 ned by t test, and the analysis of variance (ANOVA) was used to compare the data at the C-M-O regions

228 ent analysis (PCA) and analysis of variance (ANOVA) were performed to evaluate the differences betwee

229 lute deviation (MNAD), analysis of variance (ANOVA), and coefficient of variation (CV) were used for

230 s were determined with analysis of variance (ANOVA), Holm-Bonferroni corrected Pearson correlations,

231 or E+P treatment (all analysis of variance (ANOVA), P<0.01).

232 ometric tools, such as analysis of variance (ANOVA), principal component analysis (PCA), and ANOVA si

233 Analysis of variance (ANOVA), principal component analysis (PCA), and partial

234 was evaluated by using analysis of variance (ANOVA), the Kruskal-Wallis H test, and the Fisher exact

235 SRVS) approach or with analysis of variance (ANOVA)-based gene selection approach.

236 Kruskal-Wallis analysis of variance (ANOVA)-on-Ranks with post-hoc Mann-Whitney U-tests showe

237 eaks were subjected to analysis of variance (ANOVA)-simultaneous component analysis (ASCA).

238 of variation (CVs) and analysis of variance (ANOVA).

239 ed using a statistical analysis of variance (ANOVA).

240 sing repeated measures analysis of variance (ANOVA).

241 tively assessed by the analysis of variance (ANOVA).

242 Analyses of variance (ANOVAs) found 3,669 protein-coding genes that were diffe

243 Univariate analyses of variance (ANOVAs) revealed a significant effect of SNP rs4576072 i

244 om 4 to 12 injections (analysis of variance [ANOVA] P = .027) and compared with patients who received

245 al size did not (2-way analysis of variance [ANOVA] size versus frequency P = 0.03).

246 s were compared (1-way analysis of variance [ANOVA]) with those of controls.

247 and our statistical treatment of variation (ANOVA) were critical for effective use of high-throughpu

248 ed a significantly lower final tumor volume (ANOVA, p = 0.008) and growth rate than control groups -

249 cally significant difference (Kruskal-Wallis ANOVA) was found among hyper-autofluorescent, patchy, an

250 Kruskal-Wallis ANOVA-on-ranks with post hoc Mann-Whitney U tests showed

251 uated by principal component analysis, 1-way ANOVA (significant p-value < 0.05), hierarchical cluster

252 A 1-way ANOVA was performed over voxel size, bin size, and lesio

253 ere identified by using Kruskal-Wallis 1-way ANOVA with Bonferroni P value adjustment to correct for

254 t testing for longitudinal imaging and 2-way ANOVA for the (18)F-FFNP tissue biodistribution assay.

255 The 2-way ANOVA model suggested a highly significant effect of bot

256 A 2-way ANOVA revealed a significant main effect of method as we

257 lant incorporation and storage time, a 2-way ANOVA was used to process the results, further analysed

258 ) ob/ob HU and the results analyzed by 2-way ANOVA.

259 s versus teamwork interventions, using 2-way ANOVA.

260 Data were analyzed by one-way ANOVA followed by Tukey's HSD test.

261 kinson's disease and focal dystonia (one-way ANOVA p < 0.001).

262 One-way ANOVA pointed successfully on the existing statistical d

263 One-way ANOVA was performed with Prism (version 7).

264 t, paired t test, and Kruskal-Wallis one-way ANOVA were used to analyze the data.

265 cal analyses included Kruskal-Wallis one-way ANOVA with Dunn's test for multiple comparison and gener

266 One-way ANOVA with post-hoc Dunnett's test was used in which eac

267 One-way ANOVA with the post hoc Bonferroni test was used for gro

268 ically detected compounds (P < 0.05, one-way ANOVA), enabling oils' differentiation.

269 Hierarchical Cluster Analysis (HCA), One-way ANOVA, and calculation of biological accumulation factor

270 he clusters were performed using the one-way ANOVA, Kruskal-Wallis and chi-squared tests.

271 using ELISA, and data analyzed with one-way ANOVA, logistic regression analysis and receiver-operati

272 on were not statistically different (one-way ANOVA, p > 0.05).

273 Results, as determined by one-way ANOVA, show that there were no differences between the r

274 visual acuity was established with a one-way ANOVA.

275 fatigue, or both were assessed with one-way ANOVA.

276 low- and high-grade gliomas based on one-way ANOVA.

277 n the TBS groups were analyzed using one-way ANOVA.

278 /- 0.05 (mean +/- SE); p < 0.001 via one-way ANOVA].

279 A three way ANOVA with fatigue (high and low), task (movement time,

280 aries induction were analyzed with three-way ANOVA at alpha = 0.05.

281 Two-way ANOVA analyses indicated that hypoxia reduced AMPK activ

282 Furthermore, two-way ANOVA analysis showed that the bioluminescent method and

283 Two-way ANOVA by group and sex (with P < 0.05) indicated ejectio

284 a obtained from the application of a two-way ANOVA evaluation at 95% confidence level.

285 Two-way ANOVA found statistically significant seasonal and solve

286 isolated psychiatric presentations (two-way ANOVA p=0.6-0.9).

287 Two-way ANOVA showed no significant difference between the tradi

288 The results of two-way ANOVA showed that interactions between groups were obser

289 Two-way ANOVA with a Holm-Bonferroni correction was used to dete

290 e two factors, statistical analyses (two-way ANOVA) were performed.

291 canning microscopy and analyzed with two-way ANOVA.

292 One-way ANOVAs were conducted to compare thickness and collagen

293 When ANOVA with Bonferroni post-hoc comparisons was used in t

294 A/C results were statistically analyzed with ANOVA and Tukey tests (alpha = 0.05).

295 The groups were compared with ANOVA, Kruskal-Wallis test, t-test, Mann-Whitney test an

296 , DBP, OPP were calculated and compared with ANOVA.

297 at different time-points were compared with ANOVA.

298 We analysed F0 data with ANOVA and the F1 and F2 data using mixed models, with gr

299 from the three groups were interrogated with ANOVA and Kruskal-Wallis tests corrected for multiple co

300 Statistical analyses were carried out with ANOVA and multiple linear regression.