ライフサイエンスコーパス: multiple test

1 equivalent to P > 0.05 after accounting for multiple testing).

2 ngle-nucleotide polymorphisms, corrected for multiple testing).

3 , 95% CI 0.53-0.84; p=0.0004; unadjusted for multiple testing).

4 ge, sex, and acquisition site (corrected for multiple testing).

5 e body of the corpus callosum (corrected for multiple testing).

6 comparison was positive (Hochberg method for multiple testing).

7 controls (p value < 0.05 after adjusting for multiple testing).

8 cant if P <= 0.01 (Bonferroni correction for multiple testing).

9 om HIV suppression to death and adjusted for multiple tests.

10 d against the practicality and cost of using multiple tests.

11 No correction was made for multiple tests.

12 can be reduced by pooling raw test data from multiple tests.

13 s complicated and expensive, often involving multiple tests.

14 and can be used in the presence of single or multiple tests.

15 tional statistical methodologies and require multiple tests.

16 n, followed with a Bonferroni correction for multiple tests.

17 of the other phenotypes after correcting for multiple testing.

18 gs remained significant after correction for multiple testing.

19 ree-cohort meta-analysis with adjustment for multiple testing.

20 dependent SNPs was calculated to correct for multiple testing.

21 e-treatment interactions after adjusting for multiple testing.

22 atistically significant after correction for multiple testing.

23 onferroni correction was used to account for multiple testing.

24 ion was not significant after correction for multiple testing.

25 ropsychological measures after adjusting for multiple testing.

26 ank-based statistical approach adjusting for multiple testing.

27 atistically significant after correction for multiple testing.

28 ues; and (4) uses simulations to account for multiple testing.

29 up remained significant after correcting for multiple testing.

30 dietary factor that are likely explained by multiple testing.

31 significance threshold after correction for multiple testing.

32 differed significantly after adjustment for multiple testing.

33 r SNP-phenotype pairs, after controlling for multiple testing.

34 e, BMI, and sex as covariates, corrected for multiple testing.

35 e proportions, and Bonferroni-correction for multiple testing.

36 oms from Weeks 12 to 36 after correction for multiple testing.

37 ants and the permutation test to correct for multiple testing.

38 s, but none of these survives correction for multiple testing.

39 burdens of interpretation and penalties for multiple testing.

40 the replication cohort after correction for multiple testing.

41 ly Parkinson's disease after controlling for multiple testing.

42 s association did not withstand allowing for multiple testing.

43 ted with UM risk, all passing adjustment for multiple testing.

44 more asthma phenotypes after correction for multiple testing.

45 sis (Cox regression) with no corrections for multiple testing.

46 strong statistical trend when correcting for multiple testing.

47 significance after Bonferroni correction for multiple testing.

48 ndings may be missed owing to correction for multiple testing.

49 ontrolling procedure was used to account for multiple testing.

50 coronary artery disease after correction for multiple testing.

51 ant analysis and paired t tests adjusted for multiple testing.

52 ere significant for CVD after adjustment for multiple testing.

53 t associations remained after adjustment for multiple testing.

54 cance was based on Bonferroni correction for multiple testing.

55 iations did not persist after adjustment for multiple testing.

56 selectivity, limited external validity, and multiple testing.

57 rank product statistic to adequately address multiple testing.

58 This leads to a problem of multi-dimensional multiple testing.

59 False discovery rate correction was used for multiple testing.

60 ; however, none would survive correction for multiple testing.

61 notypes, including Bonferroni correction for multiple testing.

62 g-term disease activity after correction for multiple testing.

63 ntion to treat, with Hochberg adjustment for multiple testing.

64 tatistical significance after correction for multiple testing.

65 ell carcinoma did not survive correction for multiple testing.

66 y, arsenic, and mercury, after adjusting for multiple testing.

67 and controls did not survive correction for multiple testing.

68 for eBMD GWAS variants after correction for multiple testing.

69 used the false discovery rate to account for multiple testing.

70 colorectal cancer risk after correction for multiple testing.

71 an explorative study, we did not adjust for multiple testing.

72 comparisons were made without adjustment for multiple testing.

73 ich was not significant after adjustment for multiple testing.

74 onferroni correction was used to correct for multiple testing.

75 x, technical covariates, medication use, and multiple testing.

76 the replication cohort after correction for multiple testing.

77 significant after Bonferroni correction for multiple testing.

78 comes was alpha = 0.016 after adjustment for multiple testing.

79 tatistical significance after correction for multiple testing.

80 levated SHBG after Bonferroni correction for multiple testing.

81 ts with false discovery rate corrections for multiple testing.

82 albeit non-significant after correction for multiple testing.

83 h 11% of CRVO patients (P value adjusted for multiple testing = 0.0001).

84 ncreases of CSP ratios; P value adjusted for multiple testing = .03).

85 After correction for multiple testing, 54 replicated metabolites significantl

86 After correction for multiple testing, a significant genetic correlation was

87 these towards zero while, on P-value scale, multiple testing adjustment (MTA) shrinks P-values towar

88 The traditional SNP-wise approach along with multiple testing adjustment is over-conservative and lac

89 pooled by random-effects meta-analysis with multiple testing adjustment using the false discovery ra

90 bored at least one cis-eSNP after a regional multiple-test adjustment.

91 en we required statistical significance with multiple testing adjustments, phosphoglycerate dehydroge

92 ose an efficient approach for correcting for multiple testing and assess eGene p values by utilizing

93 ds as traits in trans-eQTL analysis to limit multiple testing and improve interpretability.

94 t achieves significance after correction for multiple testing and we do not detect any alleles of mod

95 tion (r > 0.3, P < 0.01 after correction for multiple testing) and discriminant [pcorr (1) > 0.3, VIP

96 ; p values are Sidak adjusted to account for multiple testing) and less likely to have normalising (p

97 tion analysis with rare variants, (2) severe multiple testing, and (3) time-consuming computations.

98 we can model unequal variances, control for multiple testing, and directly provide evidence of safet

99 t stringent significance thresholds to allow multiple testing, and it is useful only when studies hav

100 However, none of the current multiple testing approaches are applicable to LMM.

101 Such information is ignored by popular multiple testing approaches such as the Benjamini-Hochbe

102 y the software and (iii) low power of common multiple testing approaches.

103 More subtle forms of multiple testing are not as widely recognized, and abuse

104 the procedures available for accounting for multiple tests are either too conservative or fail to me

105 on, population screening and diagnosis using multiple tests are required to reduce H.pylori infection

106 riate individual gene analysis corrected for multiple testing as well as a standard a Gene Set Enrich

107 After correction for multiple testing, association analysis identified three

108 p-values that are additionally corrected for multiple testing based on the Benjamini-Hochberg method

109 cted for demographics, cell composition, and multiple testing (Benjamini-Hochberg) and verified hits

110 s were greater than .05 after correction for multiple testing) between MBL2 genotypes and any of our

111 s no longer significant after adjustment for multiple testing (Bonferroni corrected p = 1).

112 P values were corrected for multiple testing (Bonferroni).

113 ar regressions adjusting for confounders and multiple testing (Bonferroni: P < 1.71 x 10(-4)).

114 Approaches that minimize the multiple testing burden (eg, gene or pathway based) may,

115 ns (trans-eQTLs) has been limited by a heavy multiple testing burden and the proneness to false-posit

116 Moreover, the multiple testing burden of interaction scanning can pote

117 ncy in collinear NMR data sets to reduce the multiple testing burden, (ii) carry out robust and accur

118 erformed post hoc for existing GWAS, reduces multiple testing burden, and can prioritize genes for su

119 , especially sequence data, imply an immense multiple testing burden.

120 its of gene-based approaches such as reduced multiple-testing burden and a principled approach to the

121 ysis Workshop 19 (GAW19) sought reduction of multiple-testing burden through various approaches to ag

122 was associated with EFS after correction for multiple testing, but this analysis was underpowered.

123 ects of individual OTUs while accounting for multiple testing by controlling the FDR, and a connectio

124 We corrected for multiple testing by controlling the tail probability of

125 hesis weighting (IHW) and covariate adaptive multiple testing (CAMT) method are overall more powerful

126 e discoveries is explained both by a reduced multiple-testing challenge and a reduction in extraneous

127 ments and Main Results: After correcting for multiple testing, children with NEHI (n = 22) had 202 ap

128 GRS-disease outcome associations passing the multiple-testing corrected significance threshold (P < 1

129 3.6 x 10(-5) and 6.1 x 10(-5), which met the multiple-testing corrected threshold of 7.3 x 10(-5)).

130 09104 on AAR (P = 1.1 x 10(-5), reaching the multiple-testing corrected threshold).

131 pling technique to approximate empirical and multiple testing-corrected P-values.

132 ositives but makes it difficult to reach the multiple testing-corrected significance threshold.

133 2 x 10(-5) for rs12122228, which reached the multiple testing-corrected threshold) in EGEA using FBAT

134 Considering a multiple-testing-corrected significance threshold of P <

135 Rac1 pathway, although it failed to pass the multiple test correction (FDR = 0.11).

136 Due to the multi-locus nature, no multiple test correction is needed.

137 After multiple test correction, PTSD associated with methylati

138 etically determined serum urate levels after multiple testing correction (p < 3.35 x 10-4).

139 les, no CpGs were associated with AUDs after multiple testing correction (q > 0.05).

140 We provide an efficient and accurate multiple testing correction approach for linear mixed mo

141 est is considered to be the gold standard in multiple testing correction as it accurately takes into

142 es in the IL33-IL1RL1 pathway after applying multiple testing correction in the meta-analysis: 2 IL33

143 ump hunting approach and a permutation-based multiple testing correction method.

144 Compared to other commonly used multiple testing correction procedures, our method is fa

145 ted using supervised learning methods, and a multiple testing correction technique is used to control

146 or longitudinal associations, respectively; multiple testing correction was based on false discovery

147 After covariate adjustment and multiple testing correction, we identified 38 CpG sites

148 tic and the Spearman correlation metric with multiple testing correction.

149 that reach the significance threshold after multiple testing correction.

150 er of detecting relevant associations due to multiple testing correction.

151 e CLDN14 gene (rs170183, Pfdr = 0.015) after multiple testing correction.

152 rate (FDR) control has been widely used for multiple testing correction.

153 2 associated variants were significant after multiple testing correction.

154 ditional benefit of covariate adjustment and multiple testing correction.

155 uch as inadequate sample size and absence of multiple testing correction.

156 sed the P-value threshold after a Bonferroni multiple-test correction using a single locus test in fr

157 After multiple-testing correction (alpha=1.3x10(-4)), we found

158 and statistically underpowered due to heavy multiple-testing correction burden.

159 in or placebo, but none of them achieved the multiple-testing correction for significance.

160 The latter survives multiple-testing correction for the number of recurrent

161 Methods for multiple-testing correction in local expression quantita

162 ovides a simpler, more efficient approach to multiple-testing correction than existing methods and fi

163 significant or suggestive associations after multiple-testing correction were evaluated for replicati

164 For performing multiple-testing correction, a permutation test is widel

165 these differences were nonsignificant after multiple-testing correction, suggesting genetic heteroge

166 ignificantly associated with PD status after multiple-testing correction.

167 associated with cardiovascular traits after multiple-testing correction.

168 rican replication sample survived gene-level multiple-testing correction.

169 fied 1 significant locus, GRM7, which passed multiple test corrections for 2 hypertension-derived tra

170 nd rs11918967, was associated with BMI after multiple testing corrections (combined P = 2.20 x 10(-4)

171 Enrichment findings were robust to multiple testing corrections and to sensitivity analyses

172 Statistical tests with multiple testing corrections demonstrated temporal under

173 Meta-analyses were performed and multiple testing corrections were carried out using the

174 After multiple testing corrections, only 1 prognostic marker c

175 rticipants, were no longer significant after multiple testing corrections.

176 hesis tests, most researchers rely on simple multiple testing corrections.

177 likely hidden among signals discarded by the multiple testing corrections.

178 ols, statistical group comparisons (P < .01; multiple-test corrections) identified 3376 differentiall

179 Furthermore, multiple tests could be performed simultaneously with a

180 less of environmental familiarity and across multiple testing days.

181 Using correction for multiple testing detected 236 genes with an altered DNA

182 After adjusting for multiple testing, direct associations remained significa

183 s as a simple and efficient means to isolate multiple test domains on a single test strip, which faci

184 ise type I error rate, is controlled for the multiple testing error in RNA-seq data analysis.

185 ciated with incident AF after correction for multiple testing (FDR < 0.05).

186 Multiple testing following the Benjamini-Hochberg false

187 Future studies should attempt to use multiple tests for each cognitive domain and feature pop

188 ctional classes and using the consensus from multiple tests, for identifying candidates for selection

189 Multiple testing found 2 genes, PTGFR and MMP-1, were re

190 After correcting for multiple tests, four of the SDG-aligned targets (antiret

191 t implementation, a novel approach to tackle multiple testing from a Bayesian perspective through pos

192 redictive beyond chance after correcting for multiple testing genome wide.

193 significance of the seasonal trends, because multiple testing has not been taken into account.

194 iminate less promising tests and thus reduce multiple testing, have been widely and successfully appl

195 By combining multiple tests, IA can be excluded or confirmed, highlig

196 he significance threshold when corrected for multiple testing in ExWAS, and none was selected with th

197 action effect after stringent correction for multiple testing in Hispanic Americans (HA) (rs1514175 (

198 near regressions adjusted for covariates and multiple testing in the larger population.

199 including failure to consider the effects of multiple testing, inattention to clinical significance,

200 two sources of false discoveries, one due to multiple testing involving several pairwise comparisons

201 To maximize the power while addressing the multiple testing issue, we implement filters to remove d

202 ore new application domains with even larger multiple testing issue.

203 This article first discusses how multiple tests lead to an inflation of the alpha level,

204 Confounding factors and the burden of multiple testing limit the ability to map distal trans e

205 tly associated with LTL after adjustment for multiple testing: lysophosphatidylcholine acyl C17:0 (ly

206 Multiple test methods and parameter estimation approache

207 7, and miR-142-3p) also after correction for multiple testing; most of them were previously associate

208 on remains a diagnostic challenge due to the multiple tests necessary for diagnosis.

209 After correction for multiple testing, no significant interaction between the

210 After allowing for multiple testing, none of the SNPs examined was signific

211 7 or 17 (using a conservative correction for multiple testing of P < 1.03 x 10(-7)), suggesting resol

212 significance of features and adjustment for multiple testing of Relief-based scores.

213 reatment resulted in improved performance in multiple tests of motor function and behavior.

214 -specific QOL impact performed better across multiple tests of validity.

215 sing infection is determined indirectly from multiple tests on peripheral clinical specimens, which m

216 grafts (P < 0.05) but, after correction for multiple testing, only miR-505-3p remained significant.

217 r, after false discovery rate correction for multiple testing, only the associations of GIMAP4 with a

218 c findings, however, survived correction for multiple testing (p > .05).

219 f four were significant after adjustment for multiple testing (P < 0.0012): rs2476601 in PTPN22 (haza

220 re abundant among cases after controling for multiple testing (p = 0.011).

221 nce with false discovery rate correction for multiple testing (P<0.05) identified 26 genes after 12 w

222 ontrol subjects (n=121) after correction for multiple testing (P<7.3e-5) and confounding factors, inc

223 e interactions remained after correction for multiple testing (P(interaction) >0.17).

224 ated with DN after Bonferroni correction for multiple testing (P=0.0001 and 0.00025, respectively), w

225 rcentage time SaO2 < 90% after adjusting for multiple tests (P < 8 x 10-6).

226 significant after Bonferroni correction for multiple tests (p = 9 x 10(-4) 2 x 10(-3)).

227 To account for multiple testing, p-values were adjusted according to th

228 g studies, including how best to control for multiple testing, particularly in the presence of popula

229 (p = 0.0006, Bonferroni-Dunn correction for multiple testing [Pc] = 0.0192, odds ratio [OR] 1.92, 95

230 When we included multiple tests per building, concentrations declined wit

231 ease status of badger social groups requires multiple tests per group.

232 Due to a very high number of multiple tests performed we employed the procedure propo

233 After adjustment for multiple testing, playing football did not have a signif

234 It also addresses the multiple testing problem endemic to multiple sample SNV

235 sample size, the rare event nature, and the multiple testing problem, as many variables are monitore

236 d biological significance while managing the multiple testing problem.

237 We propose a multiple testing procedure that categorizes genes into e

238 s in a data-driven manner; (iii) they take a multiple testing procedure to control the overall false

239 Additionally, we applied a multiple testing procedure to infer the differential net

240 TreeQTL implements a hierarchical multiple testing procedure which allows control of appro

241 r in genomic data analysis, we propose a new multiple testing procedure, named Bon-EV, to control fal

242 To let one choose an appropriate multiple-testing procedure in practice, we develop an al

243 oosing a C-value, one can realize a specific multiple-testing procedure.

244 Improving stability of multiple testing procedures can help to increase the con

245 framework we discuss provides a platform for multiple testing procedures covering situations involvin

246 y's q-value procedures are two commonly used multiple testing procedures for controlling false discov

247 Despite much progress in multiple testing procedures such as false discovery rate

248 or count data with bespoke normalization and multiple testing procedures that account for specific st

249 Stability of multiple testing procedures, defined as the standard dev

250 an be used as an indicator of variability of multiple testing procedures.

251 Several multiple-testing procedures such as Bonferroni procedure

252 ose a general method for generating a set of multiple-testing procedures.

253 ntly associated with SZ after correction for multiple testing (rate in SZ, 33 [0.16%]; rate in contro

254 n genomic features of interest in single and multiple test/reference studies.

255 ous limitations and none naturally integrate multiple test results.

256 Multiple test-retest studies have been performed to asse

257 D] age, 52 [10] years), after correction for multiple testing, rs2070951 in the NR3C2 gene was signif

258 -coding disease-specific risk variants under multiple testing scenarios; among all the features, hist

259 ties of the same MUs can be monitored across multiple testing sessions.

260 Over 80% of multiple-tested siRNAs and shRNAs targeting CD95 or CD95

261 ormation more comprehensively by integrating multiple test statistics, each of which has relatively l

262 e of permutation-based FDR over other common multiple testing strategies.

263 aches are complex due to the availability of multiple testing strategies.

264 d SCZ being significant after correction for multiple testing, suggesting a high baseline risk for se

265 Our techniques have been applied to multiple test systems and compare favorably to thermodyn

266 y to happy faces (all P-values corrected for multiple tests) than offspring of non-bipolar parents an

267 d model and algorithms for normalisation and multiple testing that are specifically adapted to CHi-C

268 hypothesis, failure to account for forms of multiple testing that arise in model selection, abuse of

269 ancer samples, however, after correction for multiple testing the difference was significant only for

270 After correcting for multiple testing, the 3 genetic instruments for systemic

271 After adjusting for multiple testing, the meta-analysis revealed that two in

272 Following correction for multiple testing, the PRS significantly predicted the pr

273 besity-related metabolic markers, but due to multiple-testing, the secondary exploratory outcomes sho

274 ndividually significant after correction for multiple testing, this group of genes continued to show

275 After correction for multiple testing, three of the 78 microRNAs remained sig

276 sociated with HN and HT above the Bonferroni multiple-test threshold.

277 variants in cis with a gene and corrects for multiple testing to obtain a gene-level p value.

278 f correlated hypotheses, while adjusting for multiple testing, to screen for associations between dru

279 ted with 20 metabolites after correction for multiple testing (TPPFP < 0.05), and positive associatio

280 d common variation and, after correction for multiple testing, two gene sets were associated with sch

281 eighted burden test approach, accounting for multiple testing using a Bonferroni correction.

282 P values were corrected for multiple testing using false discovery rate (<0.05).

283 No correction for multiple testing was performed because only genes with a

284 Correction for multiple testing was performed by permutation testing.

285 After adjustment for covariates and multiple testing, we found 8664 differentially methylate

286 After accounting for multiple testing, we identify 16 food items and 37 nutri

287 After correcting for multiple testing, we observed significant associations b

288 After correction for multiple testing, we observed statistically significant

289 were insignificant following correction for multiple testing, we predict that few of the genetic dif

290 hizophrenia after genome-wide correction for multiple testing, we strengthen the evidence that rare e

291 ciations that did not survive correction for multiple testing were observed for NPSR1 rs324891 (T all

292 ignificant associations after correction for multiple testing were observed for three variants, TMEM1

293 l survival and proliferation, and Holm-Sidak multiple tests were used to assess tumor growth and perf

294 were identified that survived correction for multiple testing when current financial hardship was use

295 VWF and PDGFB with VTE after correction for multiple testing, whereas only weak trends were observed

296 se methods require Bonferroni correction for multiple tests, which often is too conservative when the

297 repeated measures; results were adjusted for multiple testing with Bonferroni correction.

298 BUA and seven with VOS after correction for multiple testing, with one novel locus for BUA at FAM167

299 oup (n = 54) after Bonferroni correction for multiple testing; within these compounds, the phosphatid

300 We corrected for multiple testing, yielding a significance threshold of 0