ライフサイエンスコーパス: sample size

1 usion of 266 patients (38% of the calculated sample size).

2 to limited statistical power (with too small sample sizes).

3 r(2)](1 - E[r(2)])/n, where n is the diploid sample size.

4 nded by indications for surgery or has small sample size.

5 e used in this area to gain power and reduce sample size.

6 ul for rare traits and diseases with limited sample size.

7 e two components in relation to the training sample size.

8 study did not reach its originally selected sample size.

9 pling can be achieved with a fraction of the sample size.

10 hat this conclusion is based on a very small sample size.

11 pe II errors may be present due to the small sample size.

12 -) genome-wide approaches due to the limited sample size.

13 of psychometric properties, and use of small sample size.

14 ce with dimension that grows faster than the sample size.

15 can be accurately evaluated even for a small sample size.

16 ifferences in LD, allele frequencies, and/or sample size.

17 d genome-wide association studies limited by sample size.

18 accuracy; despite the study's limitation of sample size.

19 d experiment to 25% of the normally required sample size.

20 cted first-degree relatives due to the small sample size.

21 ation and assessed for robustness to varying sample size.

22 scopy is time consuming and limited by small sample size.

23 ut 60%, equivalent to a 2.5-fold increase in sample size.

24 power for this was low because of the small sample size.

25 s, although our analysis is limited by small sample size.

26 for prediction must significantly exceed the sample size.

27 de more power and thus may require a smaller sample size.

28 ted individuals increases quadratically with sample size.

29 rmative heterogeneity, but requires enhanced sample-size.

30 ampling therefore require increasingly large sample sizes.

31 urnals did not report how they reached their sample sizes.

32 lyses could vary from 1 to 5 d for different sample sizes.

33 ll the necessary parameters to compute their sample sizes.

34 rials of pharmacotherapy, and they had small sample sizes.

35 power analyses to define minimally required sample sizes.

36 %) studies mentioned how they obtained their sample sizes.

37 ngle hemispheres, leading to higher required sample sizes.

38 ips and if they are exploitable at available sample sizes.

39 nce gain becomes more substantial for larger sample sizes.

40 dels in age/sex prediction across increasing sample sizes.

41 in Pharmacogenetics (PGx) studies with small sample sizes.

42 screen representative joint samples for each sampling size.

43 samples are successfully acquired from each sampling size.

44 es careful variant annotation and very large sample sizes(1).

45 timates of niche size and overlap at various sample sizes (10-40).

46 31 studies of complex human traits (average sample size 136,000), we show that the Baseline LD model

47 y (propensity weighted and matched effective sample size: 6037).

48 are often constrained by language and small sample sizes(7-13).

49 lop NExUS (Network Estimation across Unequal Sample sizes), a Bayesian method that enables joint lear

50 of eicosanoids on infant size due to limited sample size, along with the use of infant size at delive

51 olment of 302 episodes (68.6% of the planned sample size) an unplanned and funder-mandated interim an

52 he prevalence of SUP was 27.92% in the total sample size and 54.38% in peri-implantitis patients.

53 Further longitudinal studies with large sample size and an interventional arm are needed to subs

54 stioned, it has never been tested on a large sample size and at a broad taxonomic scale in mammals.

55 en published in recent years with increasing sample size and cellular resolution.

56 hether key elements of study design, such as sample size and choice of specific statistical tests, ha

57 gulant drugs and justify studies with larger sample size and different ethnic populations.

58 Given the limited sample size and discrepancies between previous results a

59 ently lacks statistical methods to calculate sample size and estimate power for RNA-Seq differential

60 This replication attempt used a larger sample size and fully blinded analysis.

61 were observed, which may be due to the small sample size and high variability of small sections of hu

62 ividuals were available; this would increase sample size and improve the statistical evidence of asso

63 a revealed this relationship with a moderate sample size and informed our understanding of the geneti

64 asure of LD), such as the bias due to finite sample size and its variance, were based on the special

65 However, a major problem is the large sample size and long follow-up required to demonstrate a

66 le avoiding artefactual relationship between sample size and network sparsity.

67 esearch question remains challenging because sample size and power calculations for mediation analyse

68 onger term monitoring would require a larger sample size and prospectively followed up data, focusing

69 rmation for optimizing the tradeoffs between sample size and sequencing depth with the same total rea

70 Here, we investigate the effect of sample size and shape on the moisture transport phenomen

71 However, the study is limited by a small sample size and short follow-up duration, and determinat

72 t a higher resolution is vastly dependent on sample size and statistical power.

73 memory, and (3) demonstrate how insufficient sample size and task duration reduce the likelihood of d

74 the combined role of crystalline structure, sample size and temperature on these processes, we perfo

75 ns to determine the relationship between the sample size and the accuracy of the sample mean and vari

76 as several limitations; some include limited sample size and various threats to internal validity.

77 hesize data from several studies, increasing sample size and, consequently, power to detect significa

78 ificant associations, suggesting that larger sample sizes and a homogenous collection of adipose tiss

79 ow that SPACox can efficiently analyze large sample sizes and accurately control type I error rates.

80 nes at Alzheimer's disease loci, but greater sample sizes and cell-type specific data are needed to m

81 However, prior data are limited by small sample sizes and cross-sectional designs.

82 Future studies with larger sample sizes and esophageal pH testing should be perform

83 eDNA-based detection in particular to reduce sample sizes and help bring clean trade into reach for a

84 ting the influence of network architectures, sample sizes and information content of tokens.

85 roduced mixed findings and mostly had modest sample sizes and measured the exposure during the third

86 study results are often limited due to small sample sizes and methodological differences, thus reduci

87 ciation study (GWAS) analyses, at sufficient sample sizes and power, have successfully revealed biolo

88 ublished in 2018 and 2019, which used larger sample sizes and proxy phenotypes from biobanks, have su

89 Using multiple methods, large sample sizes and sensitivity analyses, we find no eviden

90 e subfield and cellular alterations, but low sample sizes and subject heterogeneity impede establishm

91 HC features outperforms the CNN for smaller sample sizes and with increased interpretability.

92 Post hoc analysis, small sample size, and examination of only patients with 1-yea

93 e context of the single-centre design, small sample size, and lack of a placebo-only group.

94 of disease outcomes through study design and sample size, and ototoxicity endpoints should be harmoni

95 se of its restricted geographic scope, small sample size, and possible recall bias, which are typical

96 to analytical costs constraining the scope, sample size, and range of congeners analyzed, and variat

97 n nuclear signal and shape, limited training sample size, and sample preparation artifacts.

98 ad a high risk of bias, primarily due to the sample size, and statistical methods used to develop and

99 trials in this field are few, recruit small sample sizes, and experience deficiencies in design and

100 ferent experimental settings, data types and sample sizes, and includes graphical for experimental de

101 ave often been retrospective, have had small sample sizes, and the duration of follow-up has been sho

102 measures against random subsampling, varying sample sizes, and the number of clusters for each method

103 el-based experimental probes; recruit larger sample sizes; and use single case experimental designs f

104 In sum, linear models keep improving as the sample size approaches ~10,000 subjects.

105 Future studies with larger sample sizes are needed to confirm this statement (NCT02

106 Large sample sizes are often required to detect statistically

107 However, large sample sizes are required to uncover critical aspects of

108 associated with antidepressant response, the sample sizes are small and the results are difficult to

109 However, an adequate sample size as a statistical necessity for radiomics stu

110 Acknowledging our small sample size as an important limitation; our study might

111 mental parameters with significantly reduced sample size as compared to traditional laboratory-scale

112 This result was likely caused by lower sample sizes at the individual sites and the use of matr

113 causal T2D risk variants with the increased sample size available within UK Biobank (375,736 unrelat

114 collections, and have used relatively small sample sizes (averaging 12 BD cases and 15 controls).

115 field (VF) endpoints currently require large sample sizes because of the slowly-progressive nature of

116 at for such a transition one requires larger sample sizes because the amount of information per sampl

117 ce of the network structure increases as the sample size becomes smaller.

118 and analyses were repeated 1,500 times over sample sizes between 200 and 30,000 patients, with an in

119 Included studies had sample sizes between 6 and 5472 participants, with durat

120 With very large sample sizes, biobanks provide an exciting opportunity t

121 ifferences, caused by within-study grain and sample sizes, biodiversity measure, and choice of effect

122 ift, and flowMeans are impacted by increased sample size, but FlowSOM is relatively stable as sample

123 Although IGAP sought to increase sample size by recruiting additional clinical cases and

124 de the sufficient sample capacity for series sampling sizes by setting different propulsion spaces.

125 es was reported in only 5 studies (4.2%) for sample size calculation and 4 studies (3.4%) for statist

126 umber of methods and tools are available for sample size calculation for microarray and RNA-seq in th

127 Prior sample size calculation is essential to ensure that a ra

128 ly 2 studies (1.7%) performed an appropriate sample size calculation, and 46 (38.7%) chose appropriat

129 , and only 27 (22.7%) presented a replicable sample size calculation.

130 Sampled size calculation with a power of 80% and a 95% d

131 Explorative sample size calculations indicate that >=48 patients per

132 se findings provide critical information for sample size calculations of cluster-randomized trials an

133 Sample size calculations were performed to model the pow

134 The development of tools for power and sample-size calculations for mediation analysis has lagg

135 e online websites for carrying out power and sample-size calculations for mediation analysis.

136 Here we explore power and sample-size calculations to evaluate potential correlate

137 We also provide a power and sample size calculator, which facilitates decision makin

138 arguments of heterogeneity, methodology, and sample sizes can be invoked, heterogeneity is a feature,

139 date the technical noise, sparsity and large sample sizes characteristic of scRNA-seq data.

140 that the methodology and parameters used for sample size determination are inadequately reported in R

141 The sample size determination was based on standard practice

142 Subgroup analyses were performed for sex, sample size, displacement duration, visa status, country

143 eatment effect estimation, (3) volatility in sample-size distributions that can cause a nontrivial pr

144 ptimal network structure is data-driven, not sample size driven.

145 o the assigned study treatment and a reduced sample size due to the early termination of the study.

146 For datasets with moderate sample size (e.g., nCases = nControls = 30 or 50), limma

147 plasticity and towards comprehension of the sample size effects that limit the mechanical reliabilit

148 with large number of predictors and limited sample size, especially when handling binary outcomes.

149 Although we acknowledge modest sample size, estimated particle burden increased with bo

150 study to prevent decompensation resulted in sample-size estimates 3-to 4-fold lower than using a bin

151 Two-arm RCTs were reviewed to see if sample size estimation was mentioned in the text and if

152 ists document analysis for study power-based sample size estimations using preclinical mouse data to

153 tations included not achieving non-ART group sample size following change in ART treatment guidelines

154 Determining the sample size for adequate power to detect statistical sig

155 ering a longitudinal approach and sufficient sample size for analyzing gene-environment interactions

156 ented with MultiML, an algorithm to estimate sample size for machine learning classification problems

157 72 vs. 148) and 25% (36 vs. 142) of original sample size for MAIC of benralizumab vs. mepolizumab and

158 r of 0.13 mm/year and to reduce the required sample size for simulated interventional trials.

159 r variation and to derive upper estimates on sample sizes for clinical trials in HD.

160 of Health (NIH) criteria were included, the sample sizes for the subtype GWAS were small, and the GW

161 stry have limited data access based on small sample sizes from small geographic areas.

162 al large-scale rock joints, nine consecutive sampling sizes from 100 mm x 100 mm to 900 mm x 900 mm a

163 iche size and overlap were consistent across sample sizes >15.

164 modate general environment variables, modest sample sizes, heterogeneous noise, and binary traits.

165 Existing trials are hampered by small sample sizes, high attrition rates, and heterogeneity of

166 er method to estimate and assess the optimal sample size in a multi-omics experiment.

167 e, guiding appropriate subject selection and sample size in future interventional trial design.

168 compared with intermediate-risk cohorts, the sample size in these trials was smaller and the total nu

169 Sample sizes in cluster surveys must be greater than tho

170 Finally, the large sample sizes in GENOA allow us to construct accurate exp

171 At low sample sizes in the A/J mouse model, the mortality of De

172 rectomy to gastric bypass are limited by low sample size (in randomized trials) and selection bias (i

173 For the sample size included, TI > 0.36 signified statistical si

174 Future neuroimaging research of larger sample sizes, including global efforts, longitudinal des

175 imitations of this pilot study are the small sample size, inclusion of participants with relatively m

176 As GWAS sample sizes increase and PRSs become more powerful, PRS

177 le size, but FlowSOM is relatively stable as sample size increases.

178 Comparatively low effective sample sizes indicated substantial differences for patie

179 ilable strategies, especially at the smaller sample sizes investigated.

180 d good predictive models especially when the sample size is limited and the number of features is hig

181 s) times simpler than that of GEMMA when the sample size is way smaller than the number of markers.

182 of the joint surface morphology at different sampling sizes is measured by the 3D roughness parameter

183 cuity minimums, exclusion of bilateral eyes, sample size issues, demographics (age, gender, and ethni

184 ay be explained by potential selection bias, sample size issues, or a difference in underlying pathol

185 Limitations of the study include small sample size, its single-site nature, and the exclusion o

186 Overall, many studies were limited by their sample size, lack of clearly documented clinical staging

187 surrogate insulin sensitivity indices, small sample sizes, lack of blinding, and short follow-up dura

188 Larger sample sizes lead to more precisely wrong estimates.

189 methodologic limitations, including a small sample size leading to wide CIs and an overall lack of d

190 Larger sample sizes led to equally biased estimates with narrow

191 ust across different settings with different sample size, library size and effect size.

192 suited for high-dimensional data with small sample sizes like RNA-seq data.

193 enables previously infeasible studies due to sample size limitations.

194 ve trends using milk samples suggests larger sample sizes may validate the Lionex-test in accurately

195 vels of differential methylation thresholds, sample sizes, mean purity deviations and so on.

196 vide empirical support across eight studies (sample size N = 1,029,900) from the United States, Austr

197 rted difference would likely recur, with the sample size n used for statistical tests representing bi

198 lthough this finding is limited by the small sample size (N = 2).

199 in two independent datasets, given the total sample size (n = 84) and single setting, they warrant te

200 es: (a) matching by chronological age (human sample size: n = 562), or (b) matching by accounting for

201 However, the sample size necessary for animal studies requires increa

202 f suicidal behavior; however, collecting the sample sizes necessary for successful genetic studies ha

203 in a genetic dataset increases rapidly with sample size, necessitating relatedness modeling and enab

204 otential bias from factors such as different sample size, number of features, as well as class distri

205 .44%, and 13.11% over random selection for a sample size of 100, 500, and 1,000 in the MNIST dataset,

206 In 14 studies with a sample size of 108,416, MetS significantly increased the

207 These studies started with a small sample size of 1086 individuals in 2007, which was able

208 The sample size of 11 patients is small but was considered s

209 a difference of 2.5% between the 2 groups, a sample size of 1154 patients was needed to prove superio

210 For this non-inferiority trial, the total sample size of 198 is based on an expected local recurre

211 dy for male puberty timing with an effective sample size of 205,354 men.

212 One participant withdrew, leaving a sample size of 21.

213 A sample size of 332 participants was calculated to detect

214 a related study before reaching the planned sample size of 350 patients.

215 fourth interim analysis for futility with a sample size of 479 patients.

216 Using previous data, we determined that a sample size of 88 subjects would provide 90% power to de

217 a, consisting of seven studies, with a total sample size of 953 participants.

218 using a fixed-effects model weighted by the sample size of each data set.

219 The growing sample size of genome-wide association studies has facil

220 Power analysis is essential to decide the sample size of metagenomic sequencing experiments in a c

221 s; P(corrected) = 0.046), although the small sample size of these preliminary analyses warrants cauti

222 AS of clinical Alzheimer's disease to attain sample sizes of 388 324 and 534 403 individuals.

223 applications as the ancestral diversity and sample sizes of genome-wide association studies increase

224 Results were independent from the different sample sizes of mild/moderate and severe groups.

225 For the 83 (62%) cities with sample sizes of more than 200 participants in both surve

226 to published studies written in English with sample sizes of over 100.

227 research, especially in an era of very large sample sizes, often ignore the developmental context.

228 to illustrate the effects of task length and sample size on power to detect the acute effect of THC o

229 rovide new formulae for the effect of finite sample size on the observed r(2) values.

230 , kernel, and linear models as a function of sample size on UKBiobank brain images against establishe

231 ast, during adulthood, despite a much larger sample size, only two genes showed significant different

232 mal structure is predominantly determined by sample size or data nature.

233 rove these estimates because of insufficient sample size or selection biases.

234 Due to our small sample size, our results remain exploratory but forewarn

235 t data in epidemiological studies with small sample size, particularly when studying biomedical data

236 earby caves in a cave-dense region, (3) good sample sizes per cave, (4) multiple taxa, and (5) genome

237 While this survey is limited due to sample size, physician knowledge and treatment of PI-IBS

238 all impact potential, analytic approach, and sample size planning.

239 cross heterogeneous sub-populations, varying sample sizes pose a challenge in the estimation and infe

240 the order of hundreds of thousands, yet low sample sizes, poses great challenge for machine learning

241 s of sample manipulation, so obtaining large sample sizes presents a major challenge as it can be lab

242 Future research utilizing larger sample sizes, prospective data collection, and measureme

243 The large sample size provided 80% power to detect effects of the

244 By artificially inflating the sample size, pseudoreplication contributes to irreproduc

245 tratified by reported trauma exposure (final sample size range: 24,094-92,957).

246 included studies published in 2003-2018, the sample size ranged from 3 to 551 participants.

247 ) were enrolled in the seven studies and the sample sizes ranged from 17 to 86 individuals.

248 Sample sizes ranged from 27 to 10,777,210.

249 were included in the systematic review with sample sizes ranging from 2-457, an average success rate

250 ive power can be explained entirely by large sample sizes rather than by specificity for MDD.

251 ased method, to provide power evaluation and sample size recommendation for single-cell RNA-sequencin

252 Larger samples sizes, refined phenotypes and higher-resolution

253 valuation and visualization of the power and sample size relationship.

254 We project that sample sizes required to explain 80% of GWAS heritabilit

255 ariation to estimate with 95% confidence the sample sizes required to observe 90% variability of the

256 d OCT endpoints led to a 31-33% reduction in sample size requirements compared to using VF endpoints

257 Accounting for sample size requirements, a phase 3 program using SPCD w

258 onstrained by destructive sample treatments, sample-size restrictions and lengthy scan times.

259 Limitations included a relatively small sample size, retrospective nature, and only a single lap

260 The large sample size revealed previously uncharacterized hotspot

261 l trade-offs between marker-specific biases, sample size, sequencing costs, and the ability to resolv

262 d lack of overfitting; however, in the small-sample size setting, AJIVE provided the most stable resu

263 order to generalise these findings, a larger sample size should be analysed in the future.

264 rption mechanisms manifest in testing of all sample sizes, some of these mechanisms were so subtle th

265 Seven trials prespecified adaptive sample size strategies that might have mitigated this is

266 estimated control group mortality, adaptive sample size strategy, plausibility of predicted treatmen

267 we show that - due to the prevalence of low sample size studies - neither the overall frequency by w

268 or medical images), and less affected by the sample size, suggesting that the optimal network structu

269 nd heterozygous 'knockout' humans will await sample sizes that are approximately 1,000 times those pr

270 Past studies of cognitive aging included sample sizes that tended to be underpowered, were not su

271 le these studies have the advantage of large-sample sizes, they are unable to quantify the cellular b

272 Despite the small sample size, this is the first controlled experiment to

273 Sample size to detect 60% treatment effect on spinal cor

274 The sample size to detect a 5% change in lung involvement wi

275 and hence do not fully utilize the potential sample size to gain statistical power.

276 s' retained accuracy after filtering SNPs by sample size to mitigate potential bias.

277 st to us all but require exceptionally large sample sizes to study genetically.

278 is unbiased with appropriate CI coverage at sample sizes typically encountered in epidemiologic stud

279 k of recent data of TFA intake and the small sample sizes used to estimate intakes in subgroups.

280 Sample sizes varied from 943 in Anguilla to 27,988 in Ar

281 The original sample size was 440 suspected UTI episodes, to detect a

282 The total sample size was 45,149 depression cases and 86,698 contr

283 Effective sample size was 49% (72 vs. 148) and 25% (36 vs. 142) of

284 The combined sample size was 5,034 adolescents.

285 The planned sample size was 510 patients, with interim analyses plan

286 The resulting sample size was 96 (48+48) patients.

287 When the sample size was large and the proportion of outliers was

288 as shorter than the whole MMP cohort and the sample size was small.

289 endations for further research, insufficient sample size was the most predominant methodological issu

290 The total sample size was up to 322 154.

291 Sample-size was estimated was statistical analysis was d

292 Extrapolated required sample sizes were compared between analyses of single an

293 For RCTs with effect size, sample sizes were recomputed and compared with the repor

294 le and intuitive, it tends to require larger sample size which is costly.

295 sue characteristics with relatively moderate sample sizes, which is often the case with experimental

296 Future studies with larger sample sizes will improve identification and quantificat

297 A shift toward larger sample sizes with adequate statistical power to overcome

298 Sample sizes (with tracheostomy) ranged from 10 to 3,320

299 rogeneity of results indicates that a larger sample size would be beneficial.

300 use mortality dynamics suggested that larger sample sizes would lead to significantly higher deaths i