ライフサイエンスコーパス: type I error

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

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

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

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

5 onding association tests always have correct type I error.

6 which were tested hierarchically to control type I error.

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

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

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

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

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

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

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

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

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

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

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

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

19 cal comparisons, some findings may represent type I error.

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

21 D pruning improved the power and reduced the type I error.

22 dependent time series data with controllable type I error.

23 er 90% power with relatively well controlled type I error.

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

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

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

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

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

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

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

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

32 revaccination titer, without controlling for Type I errors.

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

34 sting methods, while maintaining the correct 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 ic control-adjusted 2-df test has control of type I error and achieves reasonable power, relative to

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

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

40 imulations indicate the LDM provided correct type I error and can have comparable power to existing d

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

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

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

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

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

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

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

48 We also found that both type I error and power were driven by the number of case

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

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

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

52 utational inefficient or not able to control type I error and provide decent power for whole exome or

53 on study designs by providing a landscape of type I error and statistical power for a wide range of s

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

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

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

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

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

59 esis testing, providing effective control of type I errors and yielding high statistical power.

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

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

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

63 our trial, which may increase the risk of a type I error; and potential low statistical power to dem

64 rify the proposed methods rigorously control type I errors at the genome-wide significance level, and

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

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

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

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

69 Despite the more conservative type I error, C5.0 was observed to have higher power tha

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

71 False-positive (FP) or 'type I error' cases, and false-negative (FN) or 'type II

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 rge sequencing studies, providing calibrated type I error control and more power compared to the stan

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

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

77 n simulations, PMR-Egger provides calibrated type I error control for causal effect testing in the pr

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

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

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

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

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

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

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

85 retation) and performs causal inference with type I error control.

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

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

88 ign statistical significance after stringent type I error correction.

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

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

91 To control for type I error, efficacy outcomes were analyzed with a hie

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

93 than other tests, while also maintaining the type I error (false positive) rate at the nominal level.

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

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

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

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

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

99 ed cases and controls, while SKAT has higher type I error for unbalanced case-control scenarios.

100 ulations that both methods have controllable type I errors for dependent time series, while other app

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

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

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

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

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

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

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

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

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

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

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

112 eal WES data identified two major sources of type I error inflation in this case-only test: linkage d

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

114 In contrast, Census suffers from severe type I error inflation, whereas DEXSeq is more conservat

115 Type I error is conservative when we consider variants w

116 g extensive simulation studies, we show that type I error is controlled at the nominal level, the sta

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

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

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

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

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

122 rap thresholds, wpSBOOT comes out the lowest Type I error (less FP).

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

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

125 power of the trial was 80%, with a potential type I error of 0.05.

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

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

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

129 nstrate the statistical power and control of type I error of the STIR family of feature selection met

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

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

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

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

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

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

136 w that the proposed test attains more robust type I error performance and higher empirical power than

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

138 With an 80% statistical power and a type I error probability of 0.1, 48 patients were to be

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

140 Whole-brain Type I error protection was achieved through nonparametr

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

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

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

144 proposed method controls for the genome-wise type I error rate and accounts for the linkage disequili

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

160 nbalanced study design has an overall higher type I error rate for both burden and dispersion tests c

161 The overall type I error rate for multiple comparisons across active

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

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

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

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

166 A type I error rate of 0.05 and 90% power were specified w

167 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

168 less than or equal to 0.65, with a one-sided type I error rate of 10%.

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

170 produced unbiased inferences at the expected type I error rate of 5%.

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

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

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

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

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

176 I error rate whereas logic regression had a type I error rate that exceeded 5%.

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

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

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

180 y specified and common support was poor, the type I error rate was 1.6% for propensity score matching

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

182 C5.0 had a conservative type I error rate whereas logic regression had a type I

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

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

185 We show that SCATS has well controlled type I error rate, and is more powerful than existing me

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

187 coMethDMR offers well-controlled Type I error rate, improved specificity, focused testing

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

189 pwrEWAS reports the marginal power, marginal type I error rate, marginal FDR, and false discovery cos

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

191 formed other commonly used tools in terms of Type I error rate, true positive rate and reproducibilit

192 tthews correlation coefficient, F1 score and type I error rate, we also compared several additional c

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

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

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

196 differential genotyping errors increase the type I error rate.

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

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

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

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

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

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

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

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

205 to be tested in a fixed sequence to control type I error rate.

206 violation of which would result in inflated Type I error rate.

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

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

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

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

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

212 the unadjusted approach has greatly inflated type I error rates (90 times that of exome-wide sequenci

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

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

215 effects on quantitative traits have correct type I error rates and are more powerful than some exist

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

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

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

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

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

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

222 that our normality test has well-controlled type I error rates and reasonable power.

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

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

225 ve simulations are conducted to evaluate the type I error rates and to compare the power performance

226 We find that when the type I error rates are controlled to be the same for all

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

228 than the Firth's test, and SPAGE can control type I error rates at the genome-wide significance level

229 er than the Firth correction and can control type I error rates at the genome-wide significance level

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

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

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

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

234 at all the proposed SMMATs correctly control type I error rates for both continuous and binary traits

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

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

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

238 he CIRCOAST test provides superior power and type I error rates in characterizing intercellular coloc

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

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

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

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

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

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

245 Type I error rates of the nonlinear tests are validated

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

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

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

249 Our LABST properly controlled type I error rates under extensive simulations, suggesti

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

251 mulation studies show that MultP-PE controls type I error rates very well and has consistently higher

252 analyze large-sample data (N > 400,000) with type I error rates well controlled.

253 demonstrated in simulation studies that the type I error rates were controlled in both tests despite

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

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

256 GIGSEA has appropriate type I error rates, and discovers the plausible biologic

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

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

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

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

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

264 trol ratios, and this can cause inflation of type I error rates.

265 ze large sample sizes and accurately control type I error rates.

266 ever, this approach can dramatically inflate Type I error rates.

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

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

269 ations while controlling for the genome-wise type I error rates.

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

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

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

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

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

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

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

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

278 at the new method is more powerful with less Type I error than the other two methods.

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

280 al tests included t tests and F tests with a type I error threshold (alpha) of .05.

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

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

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

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

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

286 demonstrate that the proposed CKAT controls type I error well for PGx studies, is efficient for whol

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

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

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

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

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

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

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

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

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

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

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

298 Regression has an overall higher type I error with balanced cases and controls, while SKA

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