ライフサイエンスコーパス: type I error

1 bination rates and demography (i.e., has low Type I error).

2 a overestimate the presence of interactions (Type I errors).

3 e former yields more power while controlling type I error.

4 inter-gene correlations, resulting in a high Type I error.

5 ol false discovery rate (FDR) or family-wise type I error.

6 ses, thereby avoiding potential elevation of type I error.

7 ine 8 regions) have a 34% chance of making a type I error.

8 o develop methods with improved power at low type I error.

9 o to selected sampling, at least in terms of type I error.

10 hods while providing accurate control of the type I error.

11 n transcripts, with a resultant inflation in Type I error.

12 ent from the original source, constituting a type I error.

13 ting suggests that this association is not a type I error.

14 nflates the test statistic and increases the Type I error.

15 BD is the only method that properly controls type I error.

16 d, on the basis of proximity to the targeted type I error.

17 cutoff using the data will lead to inflated type I error.

18 hods, while always being able to control the type I error.

19 erms of power while providing control of the type I error.

20 onding association tests always have correct type I error.

21 methods in terms of both empirical power and Type I error.

22 the genetic association and inflation of the type I error.

23 ve the best results in terms of coverage and type I error.

24 primary trait model can severely inflate the type I error.

25 tep and may incur power loss and/or inflated type-I error.

26 with guaranteed (controlled or conservative) type-I error.

27 pe II errors greatly with little increase in type I errors.

28 or even all positive cGxE findings represent type I errors.

29 power while retaining similar, and reliable type I errors.

30 y calculation and comparison of power and of type I errors.

31 L) VC methods may produce a severe excess of type I errors.

32 sting methods, while maintaining the correct type I errors.

33 ng association methods will lead to inflated type I errors.

34 ntaining strong control over false-positive (Type I) errors.

35 se of weighted p-values does not inflate the type I error above what we see for the un-weighted tests

36 (denoted by h(2)) with a power gamma under a Type I error alpha in an F(2) or other mating designs wi

37 t the desired family-wise power at the given type I error and (standardized) effect size.

38 ic control-adjusted 2-df test has control of type I error and achieves reasonable power, relative to

39 ed that MiRKAT provides correctly controlled type I error and adequate power in detecting overall ass

40 how that the proposed tests properly control type I error and are more powerful than the extension of

41 stimates, resulting in an increased risk for type I error and erroneous rejection of the null hypothe

42 d and real NGS data, the RVS method controls Type I error and has comparable power to the 'gold stand

43 entropy-based approach has better control of type I error and higher power compared to the standard c

44 een the two haplotype blocks and has a lower type I error and higher power than TG.

45 is approach provides accurate control of the type I error and is as powerful as joint analysis of ind

46 e show that our method properly controls for type I error and is generally more powerful than other w

47 ew method still provides accurate control of type I error and is substantially more powerful than the

48 s in ophthalmic research results in inflated type I error and may produce unwarranted shifts in clini

49 We demonstrate empirically the desirable type I error and power characteristics of the new test.

50 We evaluate type I error and power for 77 targeted regions.

51 This paper compares the type I error and power of the one- and two-sample t-test

52 variety of purposes, including evaluation of type I error and power, for association methods includin

53 ty assumption has detrimental effects on the type I error and power.

54 valuate famLBL using simulation to gauge its type I error and power.

55 to study how genotype calling errors affect type I error and statistical power of transmission-based

56 ndent sampling can cause severe inflation of type I error and substantial loss of power in quantitati

57 The results suggest that the method has low type I error and that power approaches acceptable levels

58 We use simulation to evaluate the type I error and the power of all of the statistics, con

59 We then use simulation to study the type I error and the power of various statistics for EDS

60 principal components as covariates controls type I error and yields more power than the traditional

61 at well predict the phenotype again controls type I error and yields more power than the traditional

62 ta are generated from a normal distribution, type I errors and powers of the one-sample parametric t-

63 ing the false-positive error rates (alpha or type I error) and false-negative error rates (beta or ty

64 asets that the SEQCHIP method has controlled type-I errors, and is substantially more powerful than a

65 This bias typically inflates type I error; and can reduce statistical power in certai

66 een proposed in recent literature to control type-I error at the cost of either excluding some sequen

67 ling errors yielded not only an inflation of type I error but also a power loss of association tests.

68 es; otherwise, only gene dropping controlled type I error but at the expense of statistical power.

69 ethod (BETASEQ), which corrects inflation of type-I error by supplementing pseudo-variants while keep

70 ut ignored in the test of bias equality, the type I error can exceed the prespecified error rate.

71 However type I errors can be properly controlled by obtaining p

72 In sibship reconstruction, type I errors come from the spurious fusion of two or mo

73 real scRNA-seq data, TASC achieves accurate Type I error control and displays competitive sensitivit

74 oach improve GWAS performance as measured by type I error control and power.

75 method achieves good performance in terms of Type I error control and statistical power.

76 enjamini-Hochberg false discovery rate (FDR) type I error control procedures.

77 s superior performance in terms of power and type I error control than other network recovery algorit

78 s superior performance in terms of power and Type I error control.

79 rithms that use the lasso and have bounds on type I error control.

80 the literature may, however, lead to loss of type I error control.

81 relative to no filtering, while maintaining type I error control.

82 any other genetic study, including stringent type I error control.

83 level, sequencing coverage and the choice of type I error control.

84 using empirical post-processing methods for type-I error control.

85 ign statistical significance after stringent type I error correction.

86 overy rate procedure was used to control for type I errors due to multiple comparisons.

87 rate (FDR) or positive FDR (pFDR) instead of type I error, e.g. family-wise error rate (FWER).

88 ry robust in terms of accurately controlling type I error evaluations, and are powerful by empirical

89 sis may be highly misleading and may produce type I errors far greater than the 5% that we expect.

90 The median type I error for all published trials was 0.05, and the

91 By using the observed median type I error for each disease, phase II studies have pos

92 proposed method maintains the nominal global type I error for final analyses on the basis of either p

93 In contrast, the corresponding type I error for LOD score 3.6 is.00191, giving a genome

94 Most of the statistics have correct type I error for selected samples.

95 ors did not observe any evidence of inflated type I error for these tests in their studies with 2,199

96 ee for P value estimation, produce excessive type I error (from 50% to 600% and higher) when they are

97 High nonpaternity rates can lead to inflated type I errors, highlighting the importance of identifica

98 atistics, which may lead to largely inflated Type I errors (i.e. false positives).

99 However, this approach leads to inflated type-I error if analyzed naively for rare variant associ

100 power than three other methods that control type I error in 396 of 400 (99 %) alternative settings f

101 rule, e.g., P >/= 95%, may even inflate the Type I error in both cases.

102 y reduced, with a corresponding reduction in type I error in comparison with the case-only analysis.

103 Our methods adequately control type I error in large and small samples and are computat

104 re that is required to correctly control the type I error in mapping populations with nested structur

105 nterest to determine the actual magnitude of type I error in realistic genetic situations.

106 factor they call E while maintaining nominal type I error in studying G-E interaction when informatio

107 <1.5% for both 5' and 3' EST clustering, the Type I error in the 5' EST case is approximately 10 time

108 Motivated by the failure of LMMs to control type I errors in a GWAS of asthma, a binary trait, we sh

109 ence and genotype data will lead to inflated type-I errors in rare-variant association analysis.

110 man-Elston method, (1) the same inflation in type I error, in the absence of an appropriate correctio

111 alysis performed twice and corrected for the type I-error increase due to multiple testing yields alm

112 This type I error increases both with an increase in sample s

113 erpretable end points to control the overall type I error) induces a core inefficiency in clinical tr

114 To ensure that type I error inflation does not occur when analyzing nex

115 hod, however, suffers from loss of power and type I error inflation in the presence of heteroscedasti

116 ciation tests as a control variable to limit type I error inflation or reduce loss of power due to po

117 Type I error is conservative when we consider variants w

118 We demonstrate that approximately 80% of the Type I error is due to insufficient overlap among siblin

119 ach of estimating sample size by controlling type I error is no longer applicable.

120 We show using simulations that type I error is protected under different choices of wor

121 One approach to preserving the nominal type I error is to apply genomic control, which adjusts

122 T methods, using proper analysis strategies, type I error is well-controlled even when there are high

123 With a stronger control of type I errors, k-FWER in POD framework minimized confirm

124 ack of generalizability, high probability of type I error, major baseline imbalances between interven

125 simulation studies, CC-PROMISE controls the type I error (misleading significance) rate very near th

126 A Type I error occurs when ESTs from the same gene do not

127 tion study design, we compared the power and type I error of eight popular TD-based methods under dif

128 We assume that the test controls for the type I error of rejecting the true probabilities.

129 e presence of LD between the two blocks, the type I error of TC is higher than that of TH and TG, sin

130 tions, we examined the statistical power and type I error of the different approaches under several g

131 And furthermore simulations show the Type I errors of the non-linear statistics agree with th

132 The type I errors of TRANSMIT, SIBASSOC/STDT, and RCTDT were

133 onal experiments for assessing the power and Type-I error of its enrichment procedure which show that

134 as tested at a two-sided significance level (type I error) of 0.05 using an exact test for a binomial

135 ects of bias in estimation of R on the size (type I error) of the CLRT; (3) explore the robustness of

136 f selection within at least one breed (i.e., Type I error or false-positive rate) is low if highly va

137 The probability of type I error, or a false-positive result, increases as t

138 Then type I error probabilities are not nominal, and the erro

139 The type I error probabilities of our approach were also wel

140 value estimation results instead in correct type-I-error production even when the null data are gene

141 a comparative simulation study of power and type I error properties of 3 classes of procedures: 1) t

142 However, only Agriculture had an acceptable type I error rate (3-5%) to be considered biologically r

143 her conservative nor liberal with respect to type I error rate (false-positives), compared to a simil

144 At a type I error rate adjusted for multiple testing, this st

145 les, the method is found to give the correct type I error rate and an unbiased estimate of the propor

146 g for read counts per gene improves both the type I error rate and detection power of the test.

147 the proposed two-step procedure controls the type I error rate and increase the testing power under v

148 s showed that the new method can control the type I error rate and is a bit conservative when compare

149 s show that the proposed method controls the type I error rate and is more powerful than the marginal

150 We evaluate the type I error rate and power of the TDT(ae) under a varie

151 ethods, assessed on the basis of family-wise type I error rate and power, depends on underlying disea

152 simulation we confirm a properly controlled type I error rate and reasonable power of INRICH under d

153 ) the score-based threshold maintains proper type I error rate and tends to keep false discovery rate

154 In contrast the Type I error rate and the power of gene-level GSA tests

155 Our results demonstrate that the Type I error rate and the power of multivariate tests de

156 test, on the other hand, do not control the type I error rate and thus are not recommended.

157 et of the data are shown to have the correct type I error rate and to provide accurate estimates of t

158 ships of related subjects, which may inflate type I error rate and/or decrease power of statistical t

159 To control the overall type I error rate at 0.05, a hierarchical testing strate

160 In other words, our method had a 4.4% type I error rate at the 1.25-fold level.

161 ever, at higher levels of recombination, the type I error rate can be as high as 90%, especially when

162 to show that the proposed test can bring the type I error rate down to the prespecified level.

163 approach accurately controls the genome-wide type I error rate even under the large p small n situati

164 M is robust in that it maintains the nominal type I error rate even when the external reference panel

165 ference of sample means can have an inflated Type I error rate even when the means are equal.

166 (2) population admixture seriously elevates type I error rate for detecting genes underlying complex

167 We showed that this test has the correct type I error rate for random APCPs, even for quite small

168 This bias leads to an inflated type I error rate for the score test in regions of linka

169 sent in yeast populations can lead to a high type I error rate in GWA studies of quantitative traits,

170 rrors are well-known to impact the power and type I error rate in single marker tests of association.

171 results show that the HS-TDT has the correct type I error rate in structured populations and that, in

172 (SNVs) included in the test all increase the type I error rate in the presence of differential genoty

173 netics data show that the QSAT has a correct type I error rate in the presence of population stratifi

174 In addition, the overall type I error rate may not be preserved.

175 of 0.315 in favor of SRB, using a one-sided type I error rate of 0.05 with a sample size of 100 elig

176 ll proportion of variants across the genome (type I error rate of 3%), and 3) in an independent datas

177 sses the effects of genotyping errors on the type I error rate of a particular transmission/disequili

178 ts of population admixture on increasing the type I error rate of association studies under various s

179 Statistical simulations show that the type I error rate of Atkinson's analysis is hugely infla

180 plore the effects of numerous factors on the type I error rate of rare variant tests of association i

181 eir biological relationship can increase the type I error rate of the statistical test.

182 When the type I error rate of the test is high relative to the po

183 , we show that this will tend to inflate the type I error rate of the test.

184 confounding biases into account, the actual type I error rate of the uniformity test can be calculat

185 core 3.0 entails an estimated chromosomewide type I error rate of.00574, leading to a genomewide sign

186 n homozygote as the heterozygote inflate the type I error rate significantly more than errors classif

187 s was evaluated by subjecting the tests to a type I error rate simulation analysis, using the specifi

188 er statistical power with tighter control of type I error rate than its competitors.

189 deled as a binary trait to avoid an inflated type I error rate that the authors observed when the mai

190 show that ChIP-Enrich has a well-calibrated type I error rate using permuted ENCODE ChIP-seq data se

191 udy, we show that the proposed tests control type I error rate very well.

192 The bias was generally small, the type I error rate was correctly controlled, and the powe

193 opulations, resulting in an inflation of the type I error rate when testing for linkage by this metho

194 The proposed LR method has correct type I error rate with moderate to large sample sizes re

195 the nominal rate of false positive findings (type I error rate) while offering good statistical power

196 sher exact tests and logistic regression (5% type I error rate).

197 a range of conditions, estimation accuracy, type I error rate, and power.

198 The current estimation method has a valid type I error rate, but the power is compromised given th

199 discovery rate (FDR), instead of family-wise type I error rate, is controlled for the multiple testin

200 the existing SA tests in terms of model fit, type I error rate, power, precision and accuracy by appl

201 To control the type I error rate, we derive the joint distribution of t

202 differential genotyping errors increase the type I error rate.

203 ontrol genotyping error rates the larger the type I error rate.

204 ation do not provide adequate control of the type I error rate.

205 le pathway analysis methods while preserving type I error rate.

206 econdary endpoints could be done, to control type I error rate.

207 etection power while maintaining a specified type I error rate.

208 other methods, while maintaining the nominal type I error rate.

209 nsional PCA do similarly well to control the type I error rate.

210 etic tree can easily inflate the statistical type I error rate.

211 over SKAT while maintaining control over the type I error rate.

212 ed family data, thus maintaining the correct type I error rate.

213 n existing methods while controlling for the type I error rate.

214 hat include males are shown to have a better Type I error rate.

215 g from correlated data without inflating the Type I error rate.

216 ons suggest our method maintains the correct type I error rate.Finally, the TDT-HET statistic shows h

217 at our proposed methods have the anticipated type I-error rate and that they can be more powerful tha

218 d high statistical power and well-controlled Type-I error rate.

219 s others did not; and (b) that the degree of type I error-rate inflation appears to be directly relat

220 present, the NHE can be used without severe type I error-rate inflation, even at very small alpha le

221 Type I error rates (the proportion of false-positive res

222 the proposed tests appropriately controlled Type I error rates and appeared to be more powerful than

223 tions Tool (GREAT), can have highly inflated type I error rates and biases in ranking.

224 ows that the GCP can effectively control the type I error rates and have additional power over the ex

225 es show that the proposed method has correct type I error rates and is either the most powerful test

226 -based smoothed FPCA (SFPCA) has the correct type I error rates and much more power to detect associa

227 Type I error rates and power of the entropy test are eva

228 regression approach that explicitly controls Type I error rates and provide model over-fitting diagno

229 Because of appropriate type I error rates and reduction in the correlation betw

230 We use simulations to calculate the type I error rates and the power of nine alternative sta

231 ution of the entropy-based statistic and the type I error rates are validated using simulation studie

232 ions under this design, with the result that type I error rates can be inflated substantially.

233 re observed in all settings considered, with type I error rates closely tracking their nominal values

234 structure, and simulations suggest power and Type I error rates comparable to those of competitors.

235 t can also adjust for covariates and control type I error rates even when the case-control ratio is e

236 we show that the two models have reasonable type I error rates for a data set of moderate sample siz

237 s, especially because of their high level of Type I error rates for both, simulated and real data.

238 Type I error rates for Egger's regression test are highe

239 TDT(ae) always maintains correct type I error rates for the simulations considered.

240 articipated in a linkage study revealed that type I error rates for these statistics were generally s

241 f genetic data is used to avoid inflation in type I error rates in association testing due to populat

242 onnormality, such as leptokurtosis, produced type I error rates in excess of the nominal, or alpha, l

243 show that marker-marker LD does not inflate type I error rates of affected sib pair (ASP) statistics

244 cent in multipoint calculations and hence on type I error rates of different sib-pair linkage approac

245 In this article we compare the power and Type I error rates of minimum-spanning tree (MST)-based

246 ted extensive simulations and found that the Type I error rates of our tests are under control; howev

247 d methods for the correction of conservative type I error rates of SKAT family tests when the trait o

248 The null distribution and type I error rates of the LD-based statistic for testing

249 Type I error rates of the nonlinear tests are validated

250 Type I error rates of the proposed test statistics are c

251 ernative regression test has the appropriate type I error rates regardless of the size of the underly

252 t OADA and TADA have greater power and lower Type I error rates than available alternatives, and spec

253 hod lead to unbiased parameter estimates and type I error rates that reflect nominal levels, and (3)

254 Simulation studies show correct type I error rates under the null hypothesis and robust

255 lobal adjustment procedures yielded inflated Type I error rates when stratification is due to local a

256 we show that the proposed method can control type I error rates while replicating previously known as

257 yields unbiased parameter estimates, correct type I error rates, and improved power for testing linka

258 r moderate samples, the IMVT well controlled type I error rates, and so did existent mean heterogenei

259 by Weir and Cockerham, maintains the correct type I error rates, and, when comparisons are appropriat

260 e show that this chi2 test can have inflated type I error rates, even in relatively large samples (e.

261 d that the RV-GDT method has well-controlled type I error rates, even when applied to admixed populat

262 with heteroscedasticity, result in inflated Type I error rates.

263 mined the impact of sequence errors on their type I error rates.

264 of permutation testing will lead to inflated type I error rates.

265 ith either extremely conservative or liberal type I error rates.

266 ikelihood ratio test (LRT) severely inflated type I error rates.

267 the proposed method adequately controls the Type I error rates.

268 ulation-based methods effectively controlled type I error rates; otherwise, only gene dropping contro

269 However, Type-I error rates are controlled after applying the dat

270 dispersed, the NB regression shows inflated Type-I error rates but the Classical logistic and Bayes

271 data adaptive method appropriately controls Type-I error rates in RNA-Seq analysis.

272 ample size and low dispersion generally make Type-I error rates of all methods close to nominal alpha

273 tion of this assumption can lead to inflated type I error, reduced power, and biased parameter estima

274 influences on confidence interval coverage, type I error, relative bias, and other model performance

275 happens at a frequency equal to the nominal type I error risk specified by the user.

276 The statistical tests were two sided, with a type-I error set at alpha of .05.

277 readily distinguished from false positives (type I error) that fail to consistently replicate.

278 ing a true hazard ratio of 1.33 that limited type I error to 5% (two-tail) for the four comparisons.

279 ter than the other methods and maintains the type I error to its nominal level.

280 s with modified standard errors have correct type I error under the null.

281 The Monte Carlo experiments show that the type I error varies with the chromosome length, with the

282 rmly most powerful under all conditions, but type I error was appropriate for nearly every test stati

283 Through extensive simulations we showed that type I error was correctly controlled for rare variants

284 The type I error was in good agreement with Wald's likelihoo

285 for case control designs and controlled the type I error well.

286 ib pairs, analytical power and robustness to type I error were increased.

287 Type I errors were correct, but strategy 1 provided grea

288 ngle-arm trials, false-positive error rates (type I error) were 2 to 4 times those projected when mod

289 lse-positive indications of a disease locus (type I error) were examined by simulating an unlinked ca

290 hat TDT(std) shows a significant increase in type I error when applied to data in which inconsistent

291 for overlapping subjects can greatly inflate type I error when combining results from multiple studie

292 previous work, we evaluated the increase of type I error when maximizing over two or more genetic mo

293 stream association analysis will inflate the type I error when sequenced subjects are not a random su

294 case-only method, is resistant to increased type I error when the underlying assumption of independe

295 s that methylSig maintains a well-calibrated type-I error when the number of samples is three or more

296 e original QuSAGE method can not control for type-I error when these complexities exist.

297 stical testing procedures are susceptible to Type I error, which increases at fine sampling resolutio

298 s indicated that the FEL test had reasonable Type I errors, while REL might have been too liberal, su

299 es that have focused primarily on minimizing Type I error with little or no concern about concomitant

300 formance and compare results, e.g. power and Type I error, with other currently available methods bot