ライフサイエンスコーパス: multiple comparison

1 e likelihood ratio test after correction for multiple comparisons).

2 .02 x 10-5, significant after correction for multiple comparisons).

3 ; GMV differences, P < .001, uncorrected for multiple comparisons).

4 poral brain regions (p < 0.05, corrected for multiple comparisons).

5 a reduced risk of AD (p=0.017, corrected for multiple comparisons).

6 05 for all associations after correcting for multiple comparisons).

7 changes of hs-cTnT (p < 0.01, corrected for multiple comparisons).

8 ic examination (p < .05 after adjustment for multiple comparisons).

9 ficance level was set at 0.05/25 = 0.002 for multiple comparisons).

10 V (became non-significant when corrected for multiple comparisons).

11 04 for FIQ and p=0.01 for PIQ, corrected for multiple comparisons).

12 and healthy children (P < .05 corrected for multiple comparisons).

13 group (P < 1 x 10(-4), cluster corrected for multiple comparisons).

14 dial temporal lobes (P < 0.05, corrected for multiple comparisons).

15 -6.3, -1.6; significant after adjustment for multiple comparisons).

16 4.9, -0.1; not significant when adjusted for multiple comparisons).

17 p (P < .001 after Holms-Sidak correction for multiple comparisons).

18 D2/D3 BPnd interaction, P<0.05 corrected for multiple comparisons).

19 general linear model (P < 0.05 corrected for multiple comparisons).

20 n a scale of 100; P = 0.01, not adjusted for multiple comparisons).

21 erebellum and thalamus (P<0.05 corrected for multiple comparisons).

22 in the parietal lobe (P < .05, corrected for multiple comparisons).

23 and anterior insula (P < .05, corrected for multiple comparisons).

24 model with false discovery rate control for multiple comparisons.

25 itionally adjusting for fatness measures and multiple comparisons.

26 volume were significant after correction for multiple comparisons.

27 false discovery rate was used to adjust for multiple comparisons.

28 , parents, and siblings after correction for multiple comparisons.

29 and no significant impact when adjusted for multiple comparisons.

30 , or body mass index (BMI) and corrected for multiple comparisons.

31 rate correction was conducted to control for multiple comparisons.

32 correlations with Bonferroni correction for multiple comparisons.

33 evel of 7.7 x 10(-4) was used to account for multiple comparisons.

34 considering the number of SNPs analyzed and multiple comparisons.

35 iance and Pearson correlation, corrected for multiple comparisons.

36 , P = 0.96) in offspring after adjusting for multiple comparisons.

37 iteria for significance after adjustment for multiple comparisons.

38 vs glucose sessions following correction for multiple comparisons.

39 statistically significant when adjusting for multiple comparisons.

40 l or cognitive measures after correction for multiple comparisons.

41 erformed in a way to minimize the effects of multiple comparisons.

42 the Bonferroni method was used to adjust for multiple comparisons.

43 a false discovery rate q value to adjust for multiple comparisons.

44 ct-based spatial statistics, controlling for multiple comparisons.

45 ace/ethnicity, batch effects, inflation, and multiple comparisons.

46 genes with significance after adjusting for multiple comparisons.

47 icant after strict Bonferroni correction for multiple comparisons.

48 s were based on a small number of events and multiple comparisons.

49 lues are presented and are not corrected for multiple comparisons.

50 applied to obtain the corrected P value for multiple comparisons.

51 as performed with t tests and adjustment for multiple comparisons.

52 ation) were significant after adjustment for multiple comparisons.

53 e permutation tests were used to correct for multiple comparisons.

54 Bonferroni correction was used to adjust for multiple comparisons.

55 ethod, corrected for confounding factors and multiple comparisons.

56 cal analysis, with Bonferroni correction for multiple comparisons.

57 iscovery rate method corrected for voxelwise multiple comparisons.

58 tatistically significant after adjusting for multiple comparisons.

59 eshold of 215 voxels was used to correct for multiple comparisons.

60 morphometry with clusterwise correction for multiple comparisons.

61 s test followed by Bonferroni correction for multiple comparisons.

62 ns were not significant after adjustment for multiple comparisons.

63 ificant associations survived correction for multiple comparisons.

64 lcoxon signed-rank test, with adjustment for multiple comparisons.

65 al subdivisions with BPIT when corrected for multiple comparisons.

66 used the false discovery rate to account for multiple comparisons.

67 face; statistical results were corrected for multiple comparisons.

68 onfirmed by this study, after adjustment for multiple comparisons.

69 e associated with atopy after correction for multiple comparisons.

70 ld of .0063 was assumed after adjustment for multiple comparisons.

71 atistically significant after adjustment for multiple comparisons.

72 by using the Mann-Whitney test adjusted for multiple comparisons.

73 ected to semi-Bayes shrinkage adjustment for multiple comparisons.

74 o denote statistical significance to address multiple comparisons.

75 N = 1695, ages 2-7 yrs) after adjustment for multiple comparisons.

76 iscovery rate method was used to account for multiple comparisons.

77 y unreported associations were corrected for multiple comparisons.

78 ny metachronous adenoma after correction for multiple comparisons.

79 d, and a Dunnett-Hsu adjustment was made for multiple comparisons.

80 ing Student t and chi(2) tests corrected for multiple comparisons.

81 not remain significant after correction for multiple comparisons.

82 a significance level of 0.0001 to adjust for multiple comparisons.

83 two-sided statistical tests and correct for multiple comparisons.

84 iscovery rate approach is used to adjust for multiple comparisons.

85 loseq and DESeq2; P-values were adjusted for multiple comparisons.

86 statistical thresholding and correction for multiple comparisons.

87 discounting conditions after correcting for multiple comparisons.

88 ctivation to task at P < 0.01, corrected for multiple comparisons.

89 in PD-ICB, but not surviving correction for multiple comparisons.

90 sthma and atopic asthma after correcting for multiple comparisons.

91 s for linkage disequilibrium and statistical multiple comparisons.

92 ial analyses, and P values were adjusted for multiple comparisons.

93 using log means adjusted for confounding and multiple comparisons.

94 es or group of species, after accounting for multiple comparisons.

95 , P <0.05 corrected) was used to correct for multiple comparisons.

96 pproximation test that was also adjusted for multiple comparisons.

97 honest significant difference (HSD) test for multiple comparisons.

98 old for statistical significance considering multiple comparisons.

99 st remained significant after correction for multiple comparisons.

100 st by using a Holm-Bonferroni correction for multiple comparisons.

101 essed with a Fisher exact test corrected for multiple comparisons.

102 HDL cholesterol (0.31) after accounting for multiple comparisons.

103 atistically significant after correction for multiple comparisons.

104 ty disorders after Bonferroni correction for multiple comparisons.

105 or t test, with a Bonferroni correction for multiple comparisons.

106 Bonferroni P value adjustment to correct for multiple comparisons.

107 idered significant at p < 0.05 corrected for multiple comparisons.

108 genome-wide significance aftercorrecting for multiple comparisons.

109 paired tests with Bonferroni correction for multiple comparisons.

110 re significant (p<0.01) after correction for multiple comparisons.

111 atistically significant after correction for multiple comparisons.

112 are often underpowered after adjustment for multiple comparisons.

113 Bonferroni correction was applied for multiple comparisons.

114 est with a Bonferroni correction applied for multiple comparisons.

115 tatistical significance after correction for multiple comparisons.

116 pecified secondary outcomes not adjusted for multiple comparisons, 6 were significantly improved in t

117 ed with cancer risk, and after adjusting for multiple comparisons, 9 remained significant (Q-value </

118 To adjust for multiple comparisons, a significance level of P < .01 wa

119 tion (familywise error-corrected P < .03 for multiple comparisons across the whole brain).

120 Multiple comparison adjustment is a significant and chal

121 s the variance of false discovery number for multiple comparison adjustment to handle dependence amon

122 The results were sensitive to whether multiple comparison adjustment was applied to all gene-e

123 After multiple comparison adjustment, both African and Asian a

124 ssociated with CVD risk after correcting for multiple comparisons.Although the MedDiet interventions

125 by using binomial tests; with adjustment for multiple comparisons among different groups, differences

126 Multiple comparisons among genomes can clarify their evo

127 Using multiple comparisons (analysis of variance) with change

128 After adjustment for multiple comparisons and clinical predictors of mortalit

129 After adjustment for multiple comparisons and demographic characteristics, we

130 head motion, age and sex, and controlled for multiple comparisons and medication history.

131 ugh interpretation should be cautious due to multiple comparisons and small sample size, these result

132 Limitations include power to control for multiple comparisons and that MRI landmarks approximate

133 using multivariate analysis of variance with multiple comparisons and/or paired t tests and regressio

134 Multivariate analysis of variance with multiple comparisons and/or paired t tests and regressio

135 ys (P < .05, family-wise error corrected for multiple comparisons) and cervical cord (P < .001) in pa

136 challenge with CCK-4 (p<.005, corrected for multiple comparisons) and increased functional connectiv

137 We test for molecular clock violations using multiple comparisons, and conclude that the global molec

138 meta-analysis with Bonferroni correction for multiple comparisons, and conducted metabolic pathway an

139 P-values were adjusted for multiple comparisons, and permutation testing was used t

140 n Monte Carlo simulations that corrected for multiple comparisons, and subsets of "methamphetamine de

141 hod works accurately for any large number of multiple comparisons, and the computational cost for eva

142 Error rates under multiple comparisons are not fully considered.

143 n the right caudate (P < .001, corrected for multiple comparisons) as well as with functional activit

144 ain, mixed-effects ANOVA with correction for multiple comparisons at currently recommended thresholds

145 y changed while simultaneously adjusting for multiple comparisons at the codon level.

146 Results of the statistical analyses (multiple comparison Bonferroni test, Spearman rank corre

147 s 11 of 73 [15%], p=0.032, not corrected for multiple comparison), but this was the only difference i

148 nd remained significant after adjustment for multiple comparisons, but it was not significant in wome

149 ent SNPs in eight genes after correction for multiple comparisons by a false discovery rate <0.20.

150 iation became marginal after controlling for multiple comparisons by permutation test (P = 0.08 on th

151 1057841 and serum L withstood correction for multiple comparisons by permutation testing (P<0.01) and

152 for potential confounding and correction for multiple comparisons by permutation testing.

153 After adjustments for multiple comparisons, changes in the other outcomes were

154 d using single-step linear regressions, with multiple comparisons controlled using permutation analys

155 onal anisotropy, independent of age and sex (multiple-comparisons corrected: false discovery rate cri

156 Multiple comparison correction was applied, with cluster

157 Within this framework, a Bonferroni multiple comparison correction was used to assess pseudo

158 Results significant at p </= .05, following multiple comparison correction, are reported.

159 least five P values, only 5% of these used a multiple comparison correction.

160 y associated with CM-specific survival after multiple comparison correction.

161 A multiple comparisons correction must be applied to deter

162 FTLD cohort was significantly (p<0.05 after multiple comparisons correction) associated with grey ma

163 After applying multiple comparisons correction, 10 (3 upregulated and 7

164 d not unequivocally remain significant after multiple comparisons correction, but exhibited a similar

165 res analysis of variance with Tukey pairwise multiple comparisons correction.

166 not hampered by signal loss and the need for multiple comparisons correction.

167 the brain, but this effect did not survive a multiple comparisons correction.

168 Using two multiple-comparison correction methods, 47% of the seque

169 in five different RNA segments that, after a multiple-comparison correction, had statistically signif

170 B retention, using the Bonferroni method for multiple-comparison correction.

171 The paucity of multiple comparison corrections in ophthalmic research r

172 eries within a single experiment, like other multiple comparison corrections it may be an inappropria

173 study discusses recent guidelines involving multiple comparison corrections, calculates the prevalen

174 stical comparisons (P values) and for use of multiple comparison corrections.

175 est p < 0.05) for three out of 10 considered multiple comparisons, DCT IOP and OPA showed statistical

176 ant finding after Holms-Sidak correction for multiple comparisons (effect coefficient, 0.49; 95% CI,

177 was not significant following correction for multiple comparisons (false-discovery rate, 0.12).

178 cific linear regression models corrected for multiple comparisons for both athletes and control parti

179 is a statistical method used to correct for multiple comparisons for independent or weakly dependent

180 dium concentration, P < 0.05 uncorrected for multiple comparisons for intracellular sodium concentrat

181 s achieved significance after correcting for multiple comparisons ([Formula: see text]).

182 After correction for multiple comparisons, GABA/Cr did not correlate signific

183 F differences, P < .05, after correction for multiple comparisons; GMV differences, P < .001, uncorre

184 , although not significant after considering multiple comparison, has a plausible biological explanat

185 Hypothesis-generating analyses involving multiple comparisons identified a small number of associ

186 al methods have been proposed to account for multiple comparisons in genetic association studies.

187 (P < .05, false discovery rate corrected for multiple comparisons in small volumes).

188 gistic regression models with correction for multiple comparisons in the Rwandan sample.

189 his was significant (P < 0.05, corrected for multiple comparisons) in 13/22 language tasks.

190 differences (P < .01 to partially adjust for multiple comparisons) in adverse and serious adverse eve

191 lotype in AHI1 with ASD after correction for multiple comparisons, in a region of the gene that had b

192 h healthy controls, following adjustment for multiple comparisons, in interconnected regions of the c

193 n prespecified subgroups after adjusting for multiple comparisons, including ST-elevation myocardial

194 tatistically significant after adjusting for multiple comparisons, indicating that the finding could

195 tion, but is hampered by the large number of multiple comparisons inherent in such studies.

196 ing a one-way analysis of variance and Tukey multiple-comparison intervals with alpha = 0.05.

197 (FDR) has been widely adopted to address the multiple comparisons issue in high-throughput experiment

198 To reduce multiple comparison issues, we initially used principal

199 p<0.014, respectively, after correction for multiple comparisons), less precuneus and posterior cing

200 atistically significant after correcting for multiple comparisons (mean concentration ratio = 2.8; 95

201 After adjusting for multiple comparisons, NET variant rs2242446 (T-182C) was

202 traditional direct comparison meta-analyses, multiple comparisons (network) meta-analyses, and trial

203 Accounting for multiple comparisons, none of the HRs of < 1.0 or >1.0 w

204 placebo when examined without protection for multiple comparisons (odds ratios, 1.63-2.34).

205 class using analysis of variance, Bonferroni multiple comparisons of means tests, and multivariable l

206 Tukey contrasts for multiple comparisons of the mean and linear regression a

207 Statistical analyses included adjustment for multiple comparisons.Of 333 metabolites, we identified 1

208 while controlling for error attributable to multiple comparisons on the level of the peaks identifie

209 After accounting for multiple comparisons, one of these included a statistica

210 In fact, even without correction for multiple comparisons, only 5 of 154 statistical comparis

211 y obstetric outcome (odds ratio adjusted for multiple comparisons [OR] 0.86, 95% CI 0.61-1.22) or neo

212 t on the additive scale, when accounting for multiple comparisons, or when using other definitions of

213 After statistical correction for multiple comparisons, our data do not support a substant

214 After accounting for multiple comparisons, overall improvement approached sta

215 ew subgroups in toto]) while controlling for multiple comparisons (P < .002 indicated a significant d

216 associations after gene-wise adjustment for multiple comparisons (p < .0026).

217 Analysis of single tSNPs, corrected for multiple comparisons (p < 0.00485), identified allele +1

218 differentially expressed after adjusting for multiple comparisons (P <9.15 x 10(7)).

219 After Bonferroni correction for multiple comparisons (P = 0.05 / 657 metabolites), 29 se

220 teatosis after adjusting for confounders and multiple comparisons (P=0.02).

221 unadjusted, P < 0.001 for both; adjusted for multiple comparisons, P < 0.02 for both) and inversely w

222 To adjust for multiple comparisons, P</=0.01 was considered statistica

223 When corrected for multiple comparisons, patients with visual hallucination

224 After correction for multiple comparisons, performed using the false discover

225 Although the sample size and multiple comparisons preclude a definitive statement abo

226 oped numerous corrections to account for the multiple comparison problem.

227 iferation of techniques aimed at solving the multiple comparisons problem, techniques that have focus

228 genes or proteins simultaneously, where the multiple comparison problems occurs.

229 lysis of variance model and the Tukey-Kramer multiple comparison procedure were used to assess the ef

230 way analysis of variance and Duncan post-hoc multiple comparison procedures.

231 tion between tumors following correction for multiple comparisons (Q < 0.05); 61% had higher methylat

232 P remaining significant after adjustment for multiple comparisons (rs11079657, joint p value = 2.6 x

233 in a multivariate model after adjustment for multiple comparisons (rs2239182: odds ratio = 2.17, P =

234 s remaining significant after adjustment for multiple comparisons (rs228883 and rs1005651, joint p va

235 , 95% CI: 0.56, 0.94) without adjustment for multiple comparisons, significantly increased promoter a

236 After correction for multiple comparisons, single-nucleotide polymorphisms in

237 atistically significant after adjustment for multiple comparisons, SNPs in CYP1B1 were strongly assoc

238 sk in the primary model after correction for multiple comparisons, subsequent exploratory analysis us

239 ons remained significant when correcting for multiple comparisons, suggesting that further validation

240 analyzed by using analysis of variance with multiple comparisons, t tests, or nonparametric statisti

241 skal-Wallis analysis of variance with Dunn's multiple comparison test and multiple regression models.

242 the Kruskal-Wallis test followed by the Dunn multiple comparison test.

243 alysis of variance and a post hoc Bonferroni multiple comparison test.

244 The Bonferroni multiple comparisons test was applied, and generalized l

245 s of analysis of variance followed by Dunn's multiple comparisons test.

246 analysis of variance with the Sidak or Tukey multiple comparisons test.

247 by the Fisher's least significant difference multiple-comparison test.

248 is and Friedman tests), followed by the Dunn multiple-comparisons test.

249 ormal and OA ACVs by Holm-Sidak analysis for multiple comparison testing.

250 s, Intraclass Correlation Coefficient (ICC), multiple comparison tests with Analysis of Variance and

251 Multiple comparison tests, using Tukey honestly signific

252 with one-way analysis of variance and Tukey multiple comparisons tests.

253 Multiple-comparison tests performed on the "laser" facto

254 ecified area; P = 0.0006 with adjustment for multiple comparisons) that spread to other areas of the

255 After correction for multiple comparisons, the additive interactions between

256 After correcting for multiple comparisons, the G allele of FZD4:rs713065 disp

257 itional CVD risk factors, and accounting for multiple comparisons, the high ABI group had significant

258 yses and after adjusting the probability for multiple comparisons, there was no statistically signifi

259 After adjustment for multiple comparisons, there were significant additive in

260 Controlling for multiple comparisons throughout the brain, CHR subjects

261 h repeated-measures analysis of variance and multiple comparisons Tukey tests (P <0.05).

262 After adjustment for multiple comparisons, tumor necrosis factor alpha remain

263 ignificance disappeared after correcting for multiple comparisons using Bonferroni analysis, or after

264 or statistical significance was adjusted for multiple comparisons using Bonferroni correction.

265 analyses included analysis of variance with multiple comparisons using Dunnett or Tukey methods and

266 and completed suicides after correction for multiple comparisons using the stringent Bonferroni corr

267 BMI and survived Bonferroni corrections for multiple comparison was then replicated in 2 independent

268 Correction for multiple comparisons was carried out.

269 Correction for multiple comparisons was performed by computing null hyp

270 paired categorical data with adjustments for multiple comparisons was used to compare adverse event r

271 s of variance with Bonferroni adjustment for multiple comparisons was used to compare differences in

272 Fisher's exact test, with correction for multiple comparisons, was used to compare phenotype freq

273 After correction for multiple comparisons, we did not find a statistically si

274 After adjusting for multiple comparisons, we found significant (P </= 0.05)

275 After correction for multiple comparisons, we identified a statistically sign

276 iscovery rate (FDR) correction to adjust for multiple comparisons, we observed that 85 transcripts we

277 7210 at the HNF1B locus was significant when multiple comparisons were accounted for (adjusted P = 0.

278 Multiple comparisons were adjusted with the false discov

279 the desired false positive error rates when multiple comparisons were considered.

280 tistically significant after corrections for multiple comparisons were made.

281 Although small samples sizes and multiple comparisons were of concern, many of the above

282 Multiple comparisons were performed with two-way analysi

283 Friedman test with Dunn's post hoc test for multiple comparisons were used for statistical analysis.

284 f variance with post hoc tests corrected for multiple comparisons were used to assess parameter chang

285 (volume >8 cm(3)) and P < .05 (corrected for multiple comparisons) were considered significant.

286 rty and typically do not properly adjust for multiple comparisons when selection needs to be assessed

287 ive statistical literature on adjusting for 'multiple comparisons' when testing whether these biomark

288 ssociations in big data faces the problem of multiple comparisons, wherein true signals are difficult

289 ed for confounding factors and corrected for multiple comparisons while minimizing recall bias.

290 ROC curves were compared using the method of multiple comparison with the best.

291 Unpaired t tests (corrected for multiple comparisons with a false discovery rate of 0.05

292 This tool provides access to multiple comparisons with false discovery correction, hi

293 nd safety of 'rubber band ligation including multiple comparisons with other interventions, though th

294 ormation were used, which were corrected for multiple comparisons with the Bonferroni method.

295 tatistically significant after adjusting for multiple comparisons with the Bonferroni-corrected signi

296 We controlled for multiple comparisons with the use of a false discovery r

297 for possible confounders and correction for multiple comparisons (with every 1g/L: odds ratio 0.92,

298 for possible confounders and corrections for multiple comparisons (with every 1mg/L: odds ratio 1.01,

299 r volumes of interest (P<0.05, corrected for multiple comparisons), with a generally symmetric patter

300 covariance family-wise cluster corrected for multiple comparisons, with a threshold P value of less t