ライフサイエンスコーパス: multiple comparison

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

2 -6.3, -1.6; significant after adjustment for multiple comparisons).

3 4.9, -0.1; not significant when adjusted for multiple comparisons).

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

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

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

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

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

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

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

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

12 res in the putamen (all p<0.05 corrected for multiple comparisons).

13 .02 x 10-5, significant after correction for multiple comparisons).

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

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

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

17 05 for all associations after correcting for multiple comparisons).

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

19 tal cortex and putamen (p<0.05 corrected for multiple comparisons).

20 and reward network (P < 0.005, corrected for multiple comparisons).

21 upramarginal gyrus (P < 0.005, corrected for multiple comparisons).

22 ht cingulate gyrus (P < 0.005, corrected for multiple comparisons).

23 atistically significant after adjustment for multiple comparison.

24 pass stringent significance threshold after multiple comparison.

25 of associations and Bonferroni corrected for multiple comparisons.

26 ividual enteropathogens after accounting for multiple comparisons.

27 l or cognitive measures after correction for multiple comparisons.

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

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

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

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

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

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

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

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

36 nd group-by-time interactions, corrected for multiple comparisons.

37 atistically significant after correction for multiple comparisons.

38 ty disorders after Bonferroni correction for multiple comparisons.

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

40 ith attention did not survive adjustment for multiple comparisons.

41 Bonferroni P value adjustment to correct for multiple comparisons.

42 genome-wide significance aftercorrecting for multiple comparisons.

43 paired tests with Bonferroni correction for multiple comparisons.

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

45 atistically significant after correction for multiple comparisons.

46 are often underpowered after adjustment for multiple comparisons.

47 Bonferroni correction was applied for multiple comparisons.

48 est with a Bonferroni correction applied for multiple comparisons.

49 tatistical significance after correction for multiple comparisons.

50 itionally adjusting for fatness measures and multiple comparisons.

51 volume were significant after correction for multiple comparisons.

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

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

54 and no significant impact when adjusted for multiple comparisons.

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

56 rate correction was conducted to control for multiple comparisons.

57 correlations with Bonferroni correction for multiple comparisons.

58 interactions were found after adjusting for multiple comparisons.

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

60 considering the number of SNPs analyzed and multiple comparisons.

61 iance and Pearson correlation, corrected for multiple comparisons.

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

63 iteria for significance after adjustment for multiple comparisons.

64 False discovery rate was used to adjust for multiple comparisons.

65 vs glucose sessions following correction for multiple comparisons.

66 statistically significant when adjusting for multiple comparisons.

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

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

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

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

71 genes with significance after adjusting for multiple comparisons.

72 icant after strict Bonferroni correction for multiple comparisons.

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

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

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

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

77 ation) were significant after adjustment for multiple comparisons.

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

79 Bonferroni correction was used to adjust for multiple comparisons.

80 ethod, corrected for confounding factors and multiple comparisons.

81 cal analysis, with Bonferroni correction for multiple comparisons.

82 iscovery rate method corrected for voxelwise multiple comparisons.

83 tatistically significant after adjusting for multiple comparisons.

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

85 morphometry with clusterwise correction for multiple comparisons.

86 s test followed by Bonferroni correction for multiple comparisons.

87 ns were not significant after adjustment for multiple comparisons.

88 al subdivisions with BPIT when corrected for multiple comparisons.

89 two disorders did not survive correction for multiple comparisons.

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

91 face; statistical results were corrected for multiple comparisons.

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

93 e associated with atopy after correction for multiple comparisons.

94 parately, and the P values were adjusted for multiple comparisons.

95 ctively, and Bonferroni method corrected for multiple comparisons.

96 tcome, but the results were not adjusted for multiple comparisons.

97 ns were not significant after correcting for multiple comparisons.

98 P values for significance were adjusted for multiple comparisons.

99 ar mixed models adjusting for covariates and multiple comparisons.

100 ed in mutation carriers after adjustment for multiple comparisons.

101 ing to a lack of prespecified adjustment for multiple comparisons.

102 ection factor was applied to account for the multiple comparisons.

103 Kruskal-Wallis test with Dunn post-test for multiple comparisons.

104 types with Benjamini-Hochberg correction for multiple comparisons.

105 gnificant in this study after adjustment for multiple comparisons.

106 very Rate (q < 0.05) was used to correct for multiple comparisons.

107 control and PEs groups after correction for multiple comparisons.

108 0.05 after family-wise error correction for multiple comparisons.

109 -significant when analyses were adjusted for multiple comparisons.

110 assigned as P<5x10(-8)) after correction for multiple comparisons.

111 wise error rate of 5% to adjust P values for multiple comparisons.

112 Findings were clusterwise corrected for multiple comparisons.

113 osteosarcoma (P < 0.05) after adjusting for multiple comparisons.

114 ndicative of a significant difference due to multiple comparisons.

115 one to false positives due to the problem of multiple comparisons.

116 model with false discovery rate control for multiple comparisons.

117 ificant associations survived correction for multiple comparisons.

118 ANOVA and Kruskal-Wallis tests corrected for multiple comparisons.

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

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

121 points; 95% CI, -4.8 to -0.7 [unadjusted for multiple comparisons]).

122 operative Stroop test ability (corrected for multiple comparisons, 5000 permutations).

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

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

125 The overall type I error rate for multiple comparisons across active treatment doses was c

126 Tests were corrected for multiple comparisons across all channels and time points

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

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

129 After multiple comparison adjustment, both African and Asian a

130 were those associated with ARV therapy after multiple comparisons adjustment.

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

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

133 Multiple comparisons among genomes can clarify their evo

134 al-Wallis one-way ANOVA with Dunn's test for multiple comparison and generalized linear models to adj

135 I, -2 to 16]; P = .004) after adjustment for multiple comparisons and 2 co-primary outcomes.

136 After adjustment for multiple comparisons and demographic characteristics, we

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

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

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

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

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

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

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

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

145 parametric Kruskal-Wallis tests adjusted for multiple comparisons, and multiple logistic regression.

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

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

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

149 ions (P <= .05, corrected for the voxel-wise multiple comparisons) between FA values and multiple BSI

150 nd introducing a technique that corrects for multiple-comparison bias in functional networks.

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

152 itudinal analyses were used, controlling for multiple comparisons (Bonferroni significance threshold,

153 were no longer present after correction for multiple comparisons but targeted analysis with qPCR sho

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

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

156 0% vs. -13.9%; p < .001 after correcting for multiple comparisons by Bonferroni [effect size (Cohen's

157 1057841 and serum L withstood correction for multiple comparisons by permutation testing (P<0.01) and

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

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

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

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

162 Multiple comparison correction was applied, with cluster

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

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

165 were statistically significant and survived multiple comparison correction.

166 ower by substantially reducing the burden of multiple comparison correction; (ii) employ brain annota

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

168 inferior parietal volume (all p < .05 after multiple comparisons correction).

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

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

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

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

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

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

175 The paucity of multiple comparison corrections in ophthalmic research r

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

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

178 eFC, though only the former effect survived multiple comparison corrections.

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

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

181 nication system lesion marks of each reader, multiple comparison examinations, and clinical data.

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

183 nce interval, 1.07 to 1.79, not adjusted for multiple comparisons), favoring placebo.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

200 To reduce multiple comparison issues, we initially used principal

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

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

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

204 In analyses adjusted for multiple comparisons, no statistically significant assoc

205 After adjustment for multiple comparisons, no variants were statistically sig

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

207 Based on multiple comparisons of 6 primary end points, 99% confid

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

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

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

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

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

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

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

215 thresholded at P < 0.05 after correction for multiple comparisons over the whole brain or within pre-

216 After accounting for multiple comparisons, overall improvement approached sta

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

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

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

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

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

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

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

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

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

226 A likely cause of poor replication is the multiple comparisons problem.

227 oscopic remission at week 12 or 16 using the multiple comparison procedure and modeling and the Cochr

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

229 e effects of varying doses of neladenoson, a multiple comparison procedure with 5 modeling techniques

230 A significant dose-response relationship (multiple comparison procedure-modeling, 2-sided p < 0.00

231 .69 (95% CI, -0.73 to 4.12 [not adjusted for multiple comparisons]), respectively.

232 graders, though in both cases adjustment for multiple comparisons resulted in failure to reject the n

233 Multiple comparisons Scheffe test showed no significant

234 After correction for multiple comparisons, sepsis was significantly associate

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

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

237 ost hoc pairwise comparisons controlling for multiple comparisons, standard dose (0.5 mg/kg) and high

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

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

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

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

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

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

247 Multiple comparison tests, using Tukey honestly signific

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

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

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

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

252 atistically significant after adjustment for multiple comparisons, the large effect sizes warrant fut

253 Controlling for multiple comparisons throughout the brain, CHR subjects

254 tient-reported outcomes, with adjustment for multiple comparisons to control for false-discovery rate

255 atient-reported outcomes with adjustment for multiple comparisons to control for the false discovery

256 -7, P < 10-6 family-wise error corrected for multiple comparisons) to a single location in left extra

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

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

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

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

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

262 tween groups using an ANOVA and adjusted for multiple comparisons using false discovery rate.

263 and spatial specificity while correcting for multiple comparisons using nonparametric permutation met

264 Results were corrected for multiple comparisons using the false discovery rate (FDR

265 ortical thickness (Monte Carlo corrected for multiple comparisons, vertex-wise cluster threshold of 1

266 cognition, scan interval, and corrected for multiple comparisons via false discovery rate (FDR).

267 icantly upregulated following correction for multiple comparisons (VTN , C1RL , C8B , C8A , CFH , and

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

269 LA variants were estimated, a correction for multiple comparisons was applied, and significant varian

270 Correction for multiple comparisons was carried out.

271 Correction for multiple comparisons was performed by computing null hyp

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

273 After correction for multiple comparisons we did not find any significant rel

274 To account for multiple comparisons, we applied a family-wise error rat

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

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

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

278 After correction for multiple comparisons, we observed 383 differentially exp

279 7210 at the HNF1B locus was significant when multiple comparisons were accounted for (adjusted P = 0.

280 Multiple comparisons were adjusted with the false discov

281 ing characteristic curve (AUCs) adjusted for multiple comparisons were determined to discriminate fra

282 tistically significant after corrections for multiple comparisons were made.

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

284 Permutation tests with multiple comparisons were performed to assess the dose d

285 Multiple comparisons were performed with two-way analysi

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

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

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

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

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

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

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

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

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

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

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

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

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

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

300 RR) associate with PTSD after adjustment for multiple comparisons, with lower DNA methylation in PTSD