ライフサイエンスコーパス: multiple test

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

2 equivalent to P > 0.05 after accounting 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 can be reduced by pooling raw test data from multiple tests.

8 s complicated and expensive, often involving multiple tests.

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

10 tional statistical methodologies and require multiple tests.

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

12 this result did not withstand correction for multiple tests.

13 e no longer significant after correction for multiple tests.

14 ease following a conservative correction for multiple tests.

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

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

17 dependent SNPs was calculated to correct for multiple testing.

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

19 significance after Bonferroni correction for multiple testing.

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

21 ontrolling procedure was used to account for multiple testing.

22 coronary artery disease after correction for multiple testing.

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

24 ere significant for CVD after adjustment for multiple testing.

25 t associations remained after adjustment for multiple testing.

26 cance was based on Bonferroni correction for multiple testing.

27 selectivity, limited external validity, and multiple testing.

28 rank product statistic to adequately address multiple testing.

29 ion was not significant after correction for multiple testing.

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

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

32 significant after Bonferroni correction for multiple testing.

33 ce was set at less than 0.001 to control for multiple testing.

34 ith 15-PGDH expression, after adjustment for multiple testing.

35 ns were not significant after correction for multiple testing.

36 for correlated tests (P(ACT)) to account for multiple testing.

37 idually, only a few survived adjustments for multiple testing.

38 ansethnic meta-analysis after correction for multiple testing.

39 tatistical significance after correcting for multiple testing.

40 the opposite direction, after adjustment for multiple testing.

41 l SNPs were significant after correction for multiple testing.

42 groups were examined without corrections for multiple testing.

43 ropsychological measures after adjusting for multiple testing.

44 ion was significant following correction for multiple testing.

45 results are reassuring in an era of extreme multiple testing.

46 diabetes, only a few can pass adjustment for multiple testing.

47 ific IgE, respectively, after correction for multiple testing.

48 cant (p = 3.7 x 10(-4)) after correcting for multiple testing.

49 ter adjusting for a range of confounders and multiple testing.

50 atistically significant after correction for multiple testing.

51 tly associated with OAG after correction for multiple testing.

52 the false discovery rate (FDR) to adjust for multiple testing.

53 ry rate (FDR) method was used to correct for multiple testing.

54 Ps achieved significance after adjusting for multiple testing.

55 o overall disease risk, after adjustment for multiple testing.

56 ol, imputation and analysis issues including multiple testing.

57 lse discovery rates were used to account for multiple testing.

58 2x10(-3), respectively) after correction for multiple testing.

59 wo remained significant after adjustment for multiple testing.

60 e-treatment interactions after adjusting for multiple testing.

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

62 atistically significant after correction for multiple testing.

63 up remained significant after correcting for multiple testing.

64 dietary factor that are likely explained by multiple testing.

65 significance threshold after correction for multiple testing.

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

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

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

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

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

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

72 burdens of interpretation and penalties for multiple testing.

73 the replication cohort after correction for multiple testing.

74 onferroni correction was used to account for multiple testing.

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

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

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

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

79 After correction for multiple testing, 54 replicated metabolites significantl

80 After correction for multiple testing, a total of 14 SNPs were associated wit

81 f genome-wide significance that corrects for multiple testing across the genome (p<5x10(-8)).

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

83 were observed but were not significant after multiple testing adjustment.

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

85 dantly and significantly (when corrected for multiple testing) altered in tissue and serum, and cyste

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

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

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

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

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

91 uires association thresholds consistent with multiple testing, and finally evaluates novel candidates

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

93 r by using a strict threshold accounting for multiple tests, and no SNP overlapped among the analyses

94 ration of appropriate methods to control for multiple testing; and (v) replication strategies.

95 ere significantly associated (correcting for multiple testing): ANKK1 SNP rs877138 (most strongly ass

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

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

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

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

100 association with BMI survived correction for multiple testing at rs4140535 (beta = -0.04, 95% confide

101 significance after Bonferroni adjustment for multiple testing at the level of P0.0012 (0.05/42) excep

102 We introduce a multiple testing-based methodology which makes use of av

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

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

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

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

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

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

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

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

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

112 the replication sample after correcting for multiple testing, but the combined analysis of the two s

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

114 sing linear regression models, corrected for multiple testing by using Bonferroni correction and eval

115 After an adjustment for multiple testing, cadmium, cobalt, copper and iron remai

116 P2, PLEKHA1, SLC3A2 and SLC7A6 genes meeting multiple-testing corrected significance for replication

117 3.6 x 10(-5) and 6.1 x 10(-5), which met the multiple-testing corrected threshold of 7.3 x 10(-5)).

118 09104 on AAR (P = 1.1 x 10(-5), reaching the multiple-testing corrected threshold).

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

120 -based association method, which reached the multiple testing-corrected threshold of 10(-4); P = .003

121 2 x 10(-5) for rs12122228, which reached the multiple testing-corrected threshold) in EGEA using FBAT

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

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

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

125 After multiple test correction, PTSD associated with methylati

126 omosome 17p that were also significant after multiple testing correction (best P = 3.1 x 10(-6) for m

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

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

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

130 ted with cIMT at significance levels passing multiple testing correction at both stages (array-wide s

131 hese associations remained significant after multiple testing correction but were not significant in

132 e propose a novel algorithm, rapid and exact multiple testing correction by resampling (REM), to addr

133 However, multiple testing correction can lead to low statistical

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

135 Hence, multiple testing correction is often necessary to contro

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

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

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

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

140 ts using statistical hypothesis testing with multiple testing correction.

141 f these were statistically significant after multiple testing correction.

142 so identified using Fisher's exact test with multiple testing correction.

143 d with rosuvastatin-induced CRP change after multiple testing correction.

144 that reach the significance threshold after multiple testing correction.

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

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

147 Several methods for multiple tests correction, including standard frequentis

148 on markers was based on HapMap II-CEU, and a multiple-test correction was applied (genome-wide thresh

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

150 Methods for multiple-testing correction in local expression quantita

151 be applied to a variety of resampling-based multiple-testing correction methods including permutatio

152 mplicated correlations present a challenging multiple-testing correction problem.

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

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

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

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

157 .05; .00], p = .047) and would not survive a multiple-testing correction.

158 s emerged, none of the associations survived multiple-testing correction.

159 height, but the associations did not survive multiple-testing correction.

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

161 associated with cardiovascular traits after multiple-testing correction.

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

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

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

165 ease imposes a statistical cost owing to the multiple testing corrections needed to avoid large numbe

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

167 After multiple testing corrections, only 1 prognostic marker c

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

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

170 espectively) that remained significant after multiple testing corrections.

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

172 Furthermore, multiple tests could be performed simultaneously with a

173 less of environmental familiarity and across multiple testing days.

174 A Bonferroni correction for multiple testing determined that a P value of 1.0 x 10(-

175 After adjusting for multiple testing, direct associations remained significa

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

177 ore effective compared with strategies using multiple tests, due to avoidance of false positives.

178 associated with AD even after correction for multiple testing (empirical P value 1 [EMP1], .0001; EMP

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

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

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

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

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

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

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

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

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

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

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

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

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

192 The issue of multiple testing is addressed by calculating false disco

193 including time-dependent variables; (5) how multiple testing is addressed; (6) distinction between s

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

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

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

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

198 es and expressed stably over multiple years, multiple test locations, and when the PrMC2-barnaseH102E

199 overcome the limitations of existing genomic multiple testing methods and robustly demonstrate signif

200 4) remained significant after correcting for multiple testing Methods developed in this study can be

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

202 After allowance for multiple testing none of the 120 comparisons yielded sig

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

204 After allowing for multiple testing, none of the SNPs examined were signifi

205 After correction for multiple testing, none of the SNPs were significantly as

206 7 or 17 (using a conservative correction for multiple testing of P < 1.03 x 10(-7)), suggesting resol

207 Using multiple tests of alignment performance we demonstrate t

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

209 antly correlate with T2D after adjusting for multiple testing; of these, 22 were previously reported

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

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

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

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

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

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

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

217 14); P(trend) = 0.0017] after adjustment for multiple testing (P(adj) = 0.024).

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

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

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

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

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

223 cy using bootstrap resampling to account for multiple tests per child.

224 Here we present a method for constructing multiple testing plasmids which express small hairpin RN

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

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

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

228 have the advantage of strongly reducing the multiple testing problem, while increasing the probabili

229 d biological significance while managing the multiple testing problem.

230 sing a severe inefficiency in the underlying multiple testing problem.

231 ssociated with clinical outcomes invokes the multiple testing problem.

232 s shortcoming reflects the combinatorics and multiple-testing problem associated with many-body biolo

233 edures is a standard approach to address the multiple-testing problem in eQTL studies.

234 nection, an approach that is aimed at easing multiple testing problems associated with recovering den

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

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

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

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

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

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

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

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

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

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

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

246 Several multiple-testing procedures such as Bonferroni procedure

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

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

249 ous limitations and none naturally integrate multiple test results.

250 Multiple test-retest studies have been performed to asse

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

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

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

254 It is shown on multiple test sets that the MetaGeneTack FS detection pe

255 After correction for multiple tests (significant P = 0.008), the genotype eff

256 After statistical correction for multiple tests, significant associations with HIV acquis

257 Correcting for multiple tests, single nucleotide polymorphism (SNP) rs1

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

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

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

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

262 Pointwise (P(emp)) and multiple-test study-wise (P(multi)) significance levels

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

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

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

266 pathways, which withstood FDR correction for multiple testing that were identified using both the cur

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

268 ctors identified in the discovery cohort and multiple testing, the homozygote minor allele of rs31761

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

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

271 At a type I error rate adjusted for multiple testing, this study had 99% power to detect a S

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

273 variation and (iii) allowing adjustment for multiple testing to control false discovery rate (FDR) o

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

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

276 After accounting for multiple testing, two tag SNPs in the glucocorticoid rec

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

278 entire cortical surface with correction for multiple testing using permutation analysis.

279 We adjusted for multiple testing using Simes's method.

280 ficance based on simulation and controls for multiple tests using the false discovery rate.

281 Multiple testing was Bonferroni-corrected.

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

283 Correction for multiple testing was performed by permutation testing.

284 he single K8 variants after a correction for multiple testing was performed.

285 P value of .008 or less, which accounts for multiple testing, was considered to indicate a significa

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

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

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

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

290 Statistical approaches for controlling for multiple testing were used, both with and without prescr

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

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

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

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

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

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

297 te gene studies, such as power, sample size, multiple testing within and between studies, publication

298 nd remained significant after adjustment for multiple testing within the region.

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