ライフサイエンスコーパス: statistical power

1 WAS) in plants typically suffer from limited statistical power.

2 er of possible epistatic interactions erodes statistical power.

3 tect a hypothesized difference with adequate statistical power.

4 separate studies, many of which had limited statistical power.

5 tration was assessed by using simulations of statistical power.

6 ome previous eQTL studies have limited their statistical power.

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

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

9 ative binomial distributions which increased statistical power.

10 ible with sample sizes required for adequate statistical power.

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

12 from all tumour regions to leverage greater statistical power.

13 f replication matters as it directly affects statistical power.

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

15 of experimental subjects, without affecting statistical power.

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

17 can reduce genetic heterogeneity and improve statistical power.

18 ny studies show acceptable or even exemplary statistical power.

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

20 empirical evidence and thereby increases the statistical power.

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

22 thousands of individuals to reach acceptable statistical power.

23 r findings could be tested with considerable statistical power.

24 might be insufficient to achieve the desired statistical power.

25 he influence of outcome measurement error on statistical power.

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

27 alysis as a population reference to increase statistical power.

28 les unbiased quantitative analysis with high statistical power.

29 t an hypothesized difference with sufficient statistical power.

30 e control of type I errors and yielding high statistical power.

31 ntrol of false discovery rate and comparable statistical power.

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

33 nsitivity and secondary analyses had reduced statistical power.

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

35 n among treatments with prescribed levels of statistical power.

36 brain and blood, as these provide increased statistical power.

37 additional loci, likely because of increased statistical power.

38 ution is vastly dependent on sample size and statistical power.

39 ly utilize the potential sample size to gain statistical power.

40 iologically heterogeneous subsets and reduce statistical power.

41 observations were not supported by adequate statistical power.

42 e the required sample size while maintaining statistical power.

43 ure with sufficient variation would have low statistical power.

44 ing individual locus pairs, which undermines statistical power.

45 suffer from severe co-linearity and lack of statistical power.

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

47 e studies of a related hypothesis to improve statistical power, accuracy and reproducibility beyond i

48 erse populations, and will also leverage the statistical power afforded by longitudinal imaging studi

49 g processed data, interactive visualization, statistical power analyses, construction of differential

50 A statistical power analysis demonstrated that a sample si

51 the availability of existing calculators for statistical power analysis in genetic association studie

52 We also provide a statistical power analysis, which shows that, in specifi

53 With an 80% statistical power and a type I error probability of 0.1,

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

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

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

57 vious studies through significant additional statistical power and by addressing hypotheses of suscep

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

59 ssue study of schizophrenia brains increases statistical power and confirms that glutamatergic neuron

60 We demonstrate the statistical power and control of type I error of the STI

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

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

63 nal datapoints provided by DART-ID boost the statistical power and double the number of proteins iden

64 rior knowledge is challenging due to limited statistical power and effects of linkage disequilibrium.

65 EPIs can be useful in boosting statistical power and enhancing mechanistic interpretati

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

67 entive to broadly share raw reads for better statistical power and general research reproducibility.

68 is, we leveraged this enrichment to increase statistical power and identified 75 distinct genomic loc

69 that Supervised mode significantly increases statistical power and identifies additional GRNs and ass

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

71 Choosing an inappropriate method weakens statistical power and introduces opportunities for confo

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

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

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

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

76 are reduced to only complete cases, limiting statistical power and potentially causing bias.

77 s) of education and intelligence, increasing statistical power and resulting in the largest GWAS of i

78 ntly occur in multi-omics studies, weakening statistical power and risking false findings.

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

80 aims to provide an approachable overview of statistical power and sample size planning, with emphasi

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

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

83 have been inconsistent, partially due to low statistical power and the heterogeneity of depression.

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

85 d to be improved, particularly regarding low statistical power and underreporting of design and outco

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

87 iple clinical or genomic studies to increase statistical powers and ensure robust and accurate conclu

88 ation studies show that SBL often has higher statistical powers and the simulated true loci are often

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

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

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

92 sample and reads per cell could have similar statistical power, and choosing an appropriate design ca

93 vity in a multivariate framework, increasing statistical power, and identify 10 genomic loci which in

94 improved POAG incidence estimates, increased statistical power, and increased calculated treatment ef

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

96 Different outcome measures have different statistical power, and this can refine our approaches in

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

98 Although several factors influence statistical power, appropriate sample size planning is o

99 le the number of samples to achieve the same statistical power as MGC in a benchmark suite including

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

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

102 This data pooling increases statistical power, but poses challenges for automated WM

103 ch controls the FWER while greatly improving statistical power by combining dependent tests using gen

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

105 educe unmeasured confounding and to increase statistical power by reducing large inter-subject variab

106 ree goals at the same time: (i) increase the statistical power by substantially reducing the burden o

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

108 Based on an a priori statistical power calculation, a cross-sectional study w

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

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

111 and down-regulated genes within a gene set, statistical power can be improved, or at least maintaine

112 ture of DNAm data makes direct assessment of statistical power challenging.

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

114 dly open-source resources intended to inform statistical-power considerations, facilitate the correct

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 he negative microarray results reflected low statistical power due to small sample sizes.

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

121 by providing a landscape of type I error and statistical power for a wide range of sample sizes.

122 itations of this analysis include the modest statistical power for analyses of risk factors in relati

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

124 faster, more reliable analysis with improved statistical power for comparing designs.

125 ata, we demonstrate that PerturbNet improves statistical power for detecting disease-linked SNPs and

126 y analyzing multiple phenotypes may increase statistical power for detecting genetic variants associa

127 Post hoc statistical power for detecting treatment effect was 0.9

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

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

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

131 oteomics suffers from lack of redundancy and statistical power for generating high confidence dataset

132 erlap in common genetic variants and improve statistical power for genetic discovery.

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

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

135 We then calculated statistical power for interactions between genetic risk

136 GWAS summary results; it aims to both boost statistical power for new discoveries and enhance mechan

137 patients would be needed to achieve adequate statistical power for qfFN to be predictive of sPTB at a

138 ploiting ancestry-gene interaction can boost statistical power for rare variant association mapping i

139 subject to social desirability bias, and low statistical power for secondary IPV outcomes.

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

141 To boost statistical power for the identification of variants, he

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

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

144 Statistical power for this study was approximately 50%.

145 reased in rats treated with rTM although the statistical power for this was low because of the small

146 propose a novel approach, which enhances the statistical power for weak and spatially discontinuous e

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

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

149 tronauts exposed to space radiation, but low statistical power has frustrated inferences.

150 proposed method (MCMSeq) best combines high statistical power (i.e. sensitivity or recall) with main

151 k models with cell-type specificity and high statistical power, identifying candidate circuit and mol

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

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

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

155 By using tools to improve statistical power in combination with orthogonal analyse

156 curate marker sets can substantially improve statistical power in detecting cell-type specific expres

157 A being a prototypical one) exhibit superior statistical power in detecting pathway enrichment.

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

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

160 To improve statistical power in our rare-disease cohort (N = 104 ex

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

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

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

164 We observed that statistical power increases considerably when stratifica

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

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

167 d sample size planning, with emphasis on why statistical power is important for high-quality science.

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

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

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

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

172 ning to achieve a specified level of desired statistical power is often complex and the literature ca

173 Achieving a high level of statistical power is one particularly important domain i

174 ated with the sample size, number of events, statistical power, journal impact factor, use of intenti

175 Within statistical power limitations, overall toxicity and effi

176 ticenter, prospective trials with sufficient statistical power may further fortify these findings.

177 tially be leveraged to improve study design, statistical power, measurement, and availability of evid

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

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

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

181 ual variability is essential to increase the statistical power necessary for building more accurate p

182 t a finer taxonomic resolution, provided the statistical power needed to detect change at the landsca

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

184 y of etiological differences and reduces the statistical power of analyses of associations to genetic

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

186 distinct data sets, and thereby increase the statistical power of analyses, have been hampered by a p

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

188 Here we show that given the same budget, the statistical power of cell-type-specific expression quant

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

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

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

192 It is possible to increase the statistical power of gene set enrichment analyses that e

193 rror is controlled at the nominal level, the statistical power of identifying associated genes is gre

194 anscriptomic data to showcase their improved statistical power of identifying gene-trait associations

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

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

197 We used our model to estimate the statistical power of possible treatment effects for anat

198 e full spectrum of experimental data, in the statistical power of QTL detection, and accuracy of QTL

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

200 to leverage genetic overlaps to increase the statistical power of risk-gene identification.

201 ts of enrichment can be used to increase the statistical power of such analyses to detect association

202 rom resting state fMRI may lead to increased statistical power of task-fMRI analyses.

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

204 DRAMS in multi-omics studies will strengthen statistical power of the study and improve quality of th

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

206 We leveraged the statistical power of the UK Biobank (n = 193,860) to ide

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

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

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

210 equired to detect a given QTL with a certain statistical power or calculate the statistical power wit

211 Although the calculation of statistical power or sample size is an important first s

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

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

214 ither treat each SNP independently, limiting statistical power, or combine SNPs across gene annotatio

215 of multiple phenotypes could have increased statistical power over analyzing each phenotype individu

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

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

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

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

220 If low statistical power plagues neuroscience, then this reduce

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

222 = 0.85, p = 2.2 x 10(-16)), indicating that statistical power prevents identification of AH in other

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

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

225 sive simulations show substantially improved statistical power, replication and causal mapping rate f

226 publicly informed properties to increase the statistical power, reproducibility, and generalizability

227 ithout first establishing that they have the statistical power required to detect a meaningful effect

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

229 s is partly because of the severe erosion of statistical power resulting from the massive number of s

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

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

232 Importantly, MoDentify shows higher statistical power than classical association analysis.

233 y flattened average approach provides higher statistical power than Fieldtrip cluster inference.

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

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

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

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 conflicting results, have lacked sufficient statistical power to assess many lymphoma subtypes, or h

245 The 45 studies had insufficient statistical power to compare the techniques conclusively

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

247 he risk of a type I error; and potential low statistical power to demonstrate significant between-gro

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

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

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

253 nonlinear growth scenarios but also improves statistical power to detect interregimen differences whe

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

255 of these two correlated diseases may improve statistical power to detect novel genetic factors for ce

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

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

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

259 Limitations of the study include limited statistical power to examine factors associated with poo

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

261 ort is not too small, we show that increased statistical power to identify associations between biolo

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

263 To increase statistical power to identify genes associated with comp

264 lar disorder and schizophrenia would enhance statistical power to identify novel genetic loci for dep

265 12 phenotypes shows that graph-GPA improves statistical power to identify risk variants compared to

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

267 with few individuals give substantially more statistical power to identify SNP effects than the rever

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

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

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

271 used change point based methods can increase statistical power to identify variants, it remains chall

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

273 ift toward larger sample sizes with adequate statistical power to overcome small effect sizes and dis

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

275 myocardial infarction, but the trial lacked statistical power to show a difference in MACE.

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

277 endpoint incidence, treatment efficacy, and statistical power using all-cause endpoints and POAG end

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

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

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

281 Statistical power was enhanced by adding 1959 external c

282 Although statistical power was limited because of early terminati

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

284 etic regions associated with OSA and improve statistical power, we applied admixture mapping on three

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

286 to 2 weeks after the final dose; to maximize statistical power, we evaluated this end point in the pl

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

288 To gain statistical power, we used innovative paired multivariat

289 However, they may suffer loss of statistical power when different directions of effects e

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 S patients and healthy controls and improved statistical power, when compared with a conventional seg

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

294 dpoint, Alive and Ventilator Free, preserves statistical power while improving face validity.

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

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

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

298 a certain statistical power or calculate the statistical power with given sample size and known value

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

300 sis cannot be rejected simply due to limited statistical power (with too small sample sizes).