ライフサイエンスコーパス: statistical power

1 or varicella, which may have led to limited statistical power.

2 alysis as a population reference to increase statistical power.

3 les unbiased quantitative analysis with high statistical power.

4 t an hypothesized difference with sufficient statistical power.

5 ome previous eQTL studies have limited their statistical power.

6 ve the advantage of providing both speed and statistical power.

7 nsitivity and secondary analyses had reduced statistical power.

8 comes by disease and disease stage can limit statistical power.

9 n among treatments with prescribed levels of statistical power.

10 logical knowledge on SNPs and genes to boost statistical power.

11 not achieve their primary aims is inadequate statistical power.

12 t these results were not due to insufficient statistical power.

13 specific survival after PET/CT, with similar statistical power.

14 may either be true or the result of limited statistical power.

15 , earlier effects in this trial, and limited statistical power.

16 s1801198 and methylmalonic acid (MMA) lacked statistical power.

17 third study was inconclusive because of low statistical power.

18 both humans and model organisms and reduces statistical power.

19 d potential sex differences warrant stronger statistical power.

20 re homogenous phenotypes, which may increase statistical power.

21 curve (AUC), number of false discoveries and statistical power.

22 ically investigated in studies with adequate statistical power.

23 es are lost in WO samples because of reduced statistical power.

24 ative binomial distributions which increased statistical power.

25 the cranberry product, or lack of sufficient statistical power.

26 l sufficiently large to substantially affect statistical power.

27 ible with sample sizes required for adequate statistical power.

28 nd whole-body physiology with unusually high statistical power.

29 ormance in terms of Type I error control and statistical power.

30 and still have tractable dimensionality and statistical power.

31 umber of EC towers needed to achieve a given statistical power.

32 of the phenotypic model may lead to reduced statistical power.

33 ontrolled type I error but at the expense of statistical power.

34 alternative study designs, in terms of their statistical power.

35 its, and to detect underlying loci with high statistical power.

36 on a limited number of endpoints to achieve statistical power.

37 analyzing them separately reduces events and statistical power.

38 ible duration will often struggle to achieve statistical power.

39 amples that are too small to ensure adequate statistical power.

40 from all tumour regions to leverage greater statistical power.

41 f replication matters as it directly affects statistical power.

42 on studies (GWAS) may be attributable to low statistical power.

43 of experimental subjects, without affecting statistical power.

44 use GEO data to shape hypotheses and improve statistical power.

45 can reduce genetic heterogeneity and improve statistical power.

46 ny studies show acceptable or even exemplary statistical power.

47 nt of the outcome, and possible insufficient statistical power.

48 empirical evidence and thereby increases the statistical power.

49 conduct small studies that have only 10%-40% statistical power.

50 thousands of individuals to reach acceptable statistical power.

51 might be insufficient to achieve the desired statistical power.

52 he influence of outcome measurement error on statistical power.

53 ta sets due to their limited sample size and statistical power(3).

54 Despite the low statistical power (35%) due to the small sample size, th

55 Despite increased statistical power accorded by meta-analysis, the authors

56 generations of recombination, with the high statistical power afforded by a linkage-based design.

57 oped SEQPower, a software package to perform statistical power analysis for sequence-based associatio

58 upplementary analysis that may yield greater statistical power and additional insights.

59 ir of QTL with epistasis can suffer from low statistical power and also may lead to false identificat

60 oteome is challenging, mainly due to limited statistical power and an inability to verify hundreds of

61 We used simulated data sets to assess statistical power and bias for MR when exposure data are

62 rge-scale inference sets a high bar for both statistical power and biological interpretability.

63 s (cis-eQTL) studies are a trade-off between statistical power and computational efficiency.

64 n open-ended programming environment rich in statistical power and data-handling facilities) and Cyto

65 n open-ended programming environment rich in statistical power and data-handling facilities, such as

66 We discuss approaches for improving statistical power and describe one solution: an inexpens

67 ene-gene interactions in a way that enhances statistical power and discovery.

68 y provide nonquantitative readouts that lack statistical power and do not yield information on the he

69 s smaller scale evidence, providing improved statistical power and enhanced ability to explore the ge

70 trials was designed prospectively to improve statistical power and explore heterogeneity of treatment

71 GWAS) in order to significantly increase the statistical power and exponentially reduce expenses.

72 e unit over time, the HFS-TB vastly improves statistical power and facilitates the execution of time-

73 cise biomarker phenotypes may afford greater statistical power and identify novel variants.

74 ient biological replicates, resulting in low statistical power and inefficient use of sequencing reso

75 in leveraging these pathways to improve the statistical power and interpretability in studying gene

76 experimental load with minimal compromise of statistical power and is of great potential in the field

77 ariants to complex traits is hampered by low statistical power and limited functional data.

78 s of experimental data are required to reach statistical power and make accurate predictions.

79 ssing the existence of spatial patterns lack statistical power and may fail to reveal existing spatia

80 tive cohorts can be replicated with adequate statistical power and novel phenotypic associations iden

81 n these studies as haplotypes could increase statistical power and provide additional insight.

82 ntitative assessment tool providing improved statistical power and reduced animal use.

83 Estimation of statistical power and sample size is a key aspect of exp

84 at analyzing multiple phenotypes can improve statistical power and that such analysis can be executed

85 ethod outperformed existing methods for both statistical power and the capability of identifying the

86 On the basis of the high statistical power and the consistent results across samp

87 order on personality with sufficiently high statistical power and to investigate whether effects eme

88 ure which show that GLANET has attained high statistical power and well-controlled Type-I error rate.

89 ient (ICC), reduces precision, yielding less statistical power and wider confidence intervals, compar

90 tion structure, (2) increase sample size and statistical power, and (3) elucidate the association bet

91 multivariate methods have remarkably similar statistical power, and (3) multivariate methods may offe

92 articipation might have less precision, less statistical power, and can have non-response bias.

93 analyzing related traits together increases statistical power, and certain complex associations beco

94 ppraise the rates of success, outcomes used, statistical power, and design characteristics of publish

95 he lack of sufficient exposure data, limited statistical power, and difficulty in the interpretation

96 d, assess biologic mechanisms, have adequate statistical power, and involve multiple acupuncturists.

97 is in terms of false discovery rate control, statistical power, and stability through simulation stud

98 sess in Australia and South Korea due to low statistical power, and we found little evidence of varia

99 ed P-value; and, (iii) the gLOD has the same statistical power as the widely used maximum Kong and Co

100 ug activity, we demonstrate that increase in statistical power associated with the use of TREG signat

101 They also maximize statistical power available for characterizing additiona

102 applied have the potential to: (a) increase statistical power; (b) decrease trait heterogeneity and

103 Poor fit will adversely affect statistical power, but more complex linear calibration m

104 to find differences was not due to a lack of statistical power, but rather was due to the lack of eve

105 Such approaches can increase statistical power by combining evidence for association

106 ta-analysis has been widely used to increase statistical power by combining results via regression co

107 nd cryptic relatedness and achieve increased statistical power by jointly modeling all genotyped mark

108 dings were potentially limited by inadequate statistical power, by the institution of some aspects of

109 xperimental design considerations, including statistical power calculation, we provide troubleshootin

110 common outcomes, unclear justifications for statistical power calculations, insufficient patient acc

111 Under some assumptions, the statistical power can be computed analytically given the

112 Despite an expectation of improving statistical power, combining superficial and deep/organ-

113 positive discoveries, while maintaining good statistical power compared to other ad hoc approaches fo

114 This provides significantly increased statistical power compared to regular LRT.

115 of neuroimaging studies resulting in limited statistical power, disease heterogeneity and the complex

116 are likely to produce better combination of statistical power, diversity capture and mapping resolut

117 more, objective measures provided additional statistical power due to their continuous nature.

118 Although flow cytometers have massive statistical power due to their single cell resolution an

119 everal methodological characteristics (e.g., statistical power, effect size).

120 ts of same or similar issue types to improve statistical power especially in trans-eQTL mapping.

121 size must be accurately modeled to estimate statistical power for a microbiome study that will be an

122 While these methods were shown to improve statistical power for association mapping compared to se

123 cy, automated data filtering, and ultimately statistical power for detection of metabolite correlates

124 ings in another study and/or to increase the statistical power for discovery of biomarkers or pathway

125 s on a few cells, and could provide stronger statistical power for drawing experimental observations

126 Nationwide cohorts provide sufficient statistical power for examining premature, cause-specifi

127 w-up of these cohorts will provide increased statistical power for future prospective analyses.

128 We calculated sample size and statistical power for gene-metformin interactions (vs. p

129 ers to prevent false discoveries and improve statistical power for identifying promising individual m

130 he proposed model and the improvement of the statistical power for identifying the differentially exp

131 We then calculated statistical power for interactions between genetic risk

132 Single-institution study with inadequate statistical power for subgroup analyses and recall bias.

133 1 and 3 were included originally to increase statistical power for testing pregnancy outcomes.

134 The statistical power for the analysis of RV1 was lower than

135 We show that statistical power for the identification of climate chan

136 by first mapping those main-effect QTL, the statistical power for the second and third stages of ana

137 Lower-than-expected event rates also reduced statistical power for the trials.

138 ation by NHL subtype were observed, although statistical power for these comparisons was low.

139 n any of the secondary outcome measures, but statistical power for these end points was low.

140 Statistical power for this study was approximately 50%.

141 a statistical framework named CSSP (ChIP-seq Statistical Power) for power calculations in ChIP-seq ex

142 ence interval, 1.841-9.955; P=0.0003) with a statistical power >0.8.

143 Recently, evidence for endemically low statistical power has cast neuroscience findings into do

144 n studies: single-trial hypotheses requiring statistical power, hypotheses of population response str

145 To improve statistical power, I propose that researchers remove irr

146 Statistical power improves when the within-individual co

147 hey should allow clinical trials with higher statistical power, improving the evaluation of the inter

148 ects has yielded large data sets with higher statistical power in an effort to uncover new associatio

149 cross phenotypes, one could potentially gain statistical power in association analysis.

150 od demonstrates superior reproducibility and statistical power in both simulation studies and real da

151 ically inflates type I error; and can reduce statistical power in certain situations.

152 This may however be due to the limited statistical power in classifier testing.

153 an have several advantages such as improving statistical power in detecting associations and reducing

154 ll cycle data, CPchi(2) achieved much higher statistical power in detecting differential networks tha

155 s important feature of a gene set to improve statistical power in gene set analyses.

156 This new, more nuanced picture of statistical power in neuroscience could affect not only

157 ults present a more comprehensive picture of statistical power in neuroscience: on average, studies a

158 bstantially improved robustness and enhanced statistical power in peak calling.

159 To maximize statistical power in studies of mammographic density and

160 criticism by Guerrero-Bosagna regarding our statistical power in the above study, here we provide po

161 's Heart and Health Study), we find improved statistical power in the detection of previously reporte

162 The lack of statistical power in the previous literature prevents us

163 We observed that statistical power increases considerably when stratifica

164 We show that this heterogeneity in statistical power is common across most subfields in neu

165 We find that statistical power is extremely low for studies included

166 In such trials, statistical power is governed by the rate of disease eve

167 Although statistical power is limited, these data suggests that s

168 Previous evidence suggests that statistical power is low across the field of neuroscienc

169 Future research with greater statistical power is necessary to replicate these findin

170 However, if statistical power is not uniformly low, then such blanke

171 One potentially useful direction to increase statistical power is to incorporate functional genomics

172 Texts written in natural language show a statistical power law behaviour, Zipf's law, the exponen

173 This new study design yielded increased statistical power, leading to the discovery of six new E

174 Within statistical power limitations, overall toxicity and effi

175 On the basis of studies with limited statistical power, lipoprotein(a) [Lp(a)] is not conside

176 In terms of statistical power, modular methods that screen on the ba

177 ponse can increase treatment effect size and statistical power more efficiently than conventional enr

178 Due in part to limited statistical power, most studies identify only small numb

179 val using a two-sided alpha level of 0.2 and statistical power of 0.8.

180 esting can be applied widely to evaluate the statistical power of analyses to detect predation effect

181 ted change pattern and eventually reduce the statistical power of analysis.

182 ly available beforehand, and they all have a statistical power of at least 90% to detect the original

183 acterial species, we are able to improve the statistical power of detecting associated bacterial spec

184 ean and variance signals in order to improve statistical power of detecting differentially methylated

185 Including these noisy CpGs will decrease the statistical power of detecting relevant associations due

186 f annotation specificity, however, limit the statistical power of enrichment methods and make it diff

187 We present a new strategy to boost the statistical power of hypothesis testing in metabolomics

188 The scale, resolution, and statistical power of microfluidic-based mini-metagenomic

189 th a fewer number of genes, should boost the statistical power of molecular genetic studies and clari

190 The increased statistical power of our phylogenetic model allows detec

191 we provide power calculations to clarify the statistical power of our study and to show the validity

192 From the statistical power of published genome-wide association s

193 able genetic resources, thereby boosting the statistical power of QTL discovery for important traits

194 ic influences on complex traits, and for the statistical power of samples recruited for genetic assoc

195 Carlo simulations were used to quantify the statistical power of selecting a leaking well.

196 Over time, the quality and statistical power of studies within this research area h

197 clusion requirements to achieve a sufficient statistical power of test and the identification of sign

198 gle-nucleotide polymorphisms, we improve the statistical power of the gene-based association test.

199 The statistical power of the method is still comparatively s

200 ascularization, with the aim to increase the statistical power of the study.

201 of misclassification, thereby increasing the statistical power of the study.

202 l variation in infection risk undermines the statistical power of the SWCT.

203 stration of our application, we compared the statistical power of the two tests, with and without str

204 issimilarity measures between sequences, the statistical power of these measures when two sequences a

205 ave a quantitative framework to evaluate the statistical power of these projects.

206 le G x E to be investigated and inherent low statistical power of traditional analytic methods for di

207 otype calling errors affect type I error and statistical power of transmission-based association test

208 alysis which has significantly increased the statistical power of treatment/outcome models in the UK'

209 nal but, if imposed correctly, can boost the statistical powers of the tests: 3) the gene-environment

210 it may often go undetected owing to lack of statistical power or lack of genome-wide scope of the ex

211 However, these results could be due to low statistical power or unknown confounders associated with

212 of rTMS in PD are mixed, mostly owing to low statistical power or variety in individual rTMS protocol

213 h trait dynamics, and demonstrates increased statistical power over existing methods.

214 e, and 2) comparable performance in terms of statistical power over other currently existing joint mo

215 uggest that the proposed method has improved statistical power over single-trait analysis in most of

216 Large sample sizes enhanced statistical power, particularly for pup weight and prena

217 mmon variants and variants with low MAF) and statistical power, particularly for the analysis of quan

218 If low statistical power plagues neuroscience, then this reduce

219 ns for non-replication, including inadequate statistical power, population stratification, and poor p

220 es within this field, including factors like statistical power, prestudy odds, and bias.

221 = 0.85, p = 2.2 x 10(-16)), indicating that statistical power prevents identification of AH in other

222 pic effects of genetic variants can increase statistical power, provide important information to achi

223 sivity analyses (often confounded by lack of statistical power) raise the possibility of functional r

224 order expression differentials for increased statistical power, regardless of the threshold.

225 for binning artifacts, and provides improved statistical power relative to a previously described met

226 ractions were true or caused by insufficient statistical power remains uncertain.

227 arious methodological factors, including low statistical power, researcher's degrees of freedom, and

228 For the purpose of maximizing statistical power, results from the two randomized trial

229 ersus Edwards SAPIEN XT Trial), with limited statistical power, revealed clinical outcomes after tran

230 , which accounts for pleiotropy but has less statistical power, suggests there might be no causal eff

231 on and direct maximum likelihood had greater statistical power than did analysis restricted to the va

232 is also observed to have consistently higher statistical power than MIC.

233 ICP-MS method provides greater precision and statistical power than possible with conventional tracer

234 s for fluorescence based t-tests has greater statistical power than the same probabilities from conce

235 Also, CC-PROMISE has better statistical power than three other methods that control

236 The ptau181-Abeta42 ratio has more statistical power than traditional modeling approaches,

237 ent cohorts are important for increasing the statistical power that will allow for the extraction of

238 00,000 fewer reads per sample for equivalent statistical power, the resulting differentially expresse

239 Because of its limited statistical power, the results of the DANTE (Detection A

240 One marker for reliability is statistical power: the probability of finding a statisti

241 However, analysis methods that optimize statistical power through simultaneous evaluation of tho

242 rol cohorts, necessary to achieve sufficient statistical power to assess associations between complex

243 , use of various d-dimer assays, and limited statistical power to assess failure rate.

244 The trial was designed with adequate statistical power to assess whether lixisenatide was non

245 the generation of data sets with sufficient statistical power to correlate neural activity with stim

246 and non-HLA genes, but they have lacked the statistical power to define the architecture of associat

247 There was sufficient statistical power to detect a 9% relative risk reduction

248 not account for ASM, our approach increases statistical power to detect associations while sharply r

249 Statistical power to detect associations with these phen

250 r simulations suggest that our study had the statistical power to detect at least one causal gene (a

251 Protein domain analysis enhances the statistical power to detect cancer-relevant mutations an

252 f data on potential confounders, the limited statistical power to detect differences in congenital an

253 equally, which can cause substantial loss of statistical power to detect differentially expressed fea

254 We sought to increase statistical power to detect disease loci by conducting a

255 ingly specific subphenotypes while retaining statistical power to detect genetic associations.

256 ove phenotypic accuracy and thereby increase statistical power to detect genetic associations.

257 dually, demonstrating that we had sufficient statistical power to detect known group differences.

258 e-related common phenotypes for which we had statistical power to detect large numbers of common vari

259 Post hoc evaluation showed limited statistical power to detect significant differences in F

260 ty to replicate GWAS signals and to increase statistical power to detect such signals through meta-an

261 s to be highly multiplexed and increased our statistical power to detect the effects of TFBS variants

262 treat-to-target trial designs had poor (<5%) statistical power to detect the HTEs, despite large samp

263 Low statistical power to detect the small effect-size allele

264 To date, no analysis has had adequate statistical power to determine whether thrombolytic ther

265 antly urban populations and did not have the statistical power to estimate the health effects in unde

266 Existing data are limited in statistical power to examine rarer outcomes and less com

267 However, prior studies had limited statistical power to examine sex-specific effects, and f

268 proaches have not been optimized to maximize statistical power to identify enriched functions/pathway

269 12 phenotypes shows that graph-GPA improves statistical power to identify risk variants compared to

270 tical methods have been developed to improve statistical power to identify risk variants for complex

271 lthough as with complex traits they lack the statistical power to identify the effects from rare gene

272 re, using only one single phenotype may lose statistical power to identify the underlying genetic mec

273 uencing on a few individuals, but these lack statistical power to identify variants associated with D

274 by the vast search space and as a result low statistical power to make new discoveries.

275 the remaining 72%, which may reflect the low statistical power to model rare taxa and/or species inse

276 ffects of genetic variability, with improved statistical power to model these effects on gene express

277 a larger sample and leveraged the additional statistical power to perform individual differences anal

278 wever, the majority of current GWAS lack the statistical power to test whether multiple causative gen

279 , gives an improved performance (in terms of statistical power) to detect eQTLs over the current eQTL

280 a patient-centered result and major gains in statistical power under certain conditions, but this app

281 ds and compared the false positive rates and statistical power using both simulated and real datasets

282 ds and compared the false positive rates and statistical power using both simulated and real datasets

283 g host-microbe interactions with appropriate statistical power using high-throughput sequencing, and

284 Statistical power was discussed in 135 RCTs (92%); 92 ci

285 Although statistical power was limited because of early terminati

286 ations did not differ by study arm, although statistical power was limited for these outcomes.

287 Despite good statistical power, we did not identify any other new low

288 To increase statistical power, we expanded the sample size via genot

289 To gain statistical power, we used innovative paired multivariat

290 to be conservative, leading to insufficient statistical power when the effect size is moderate at ri

291 simulations showed an increase up to 20% in statistical power when using QS in comparison to other f

292 it is useful only when studies have adequate statistical power, which depends on the characteristics

293 most less than 0.1%), substantially reducing statistical power, which was examined in detail.

294 thod outperforms current methods in terms of statistical power while maintaining validity.

295 global and local tests, exhibiting improved statistical power while retaining similar, and reliable

296 A better consideration of effect size and statistical power will lead to more robust biological co

297 lyzable patients were required to ensure 80% statistical power with alpha = 0.05.

298 We estimated a 20% gain in statistical power with long-term average (LTA) as compar

299 Super-delta was shown to have better statistical power with tighter control of type I error r

300 showed that inclusion of non-RCTs increased statistical power without biasing the calculated effect