ライフサイエンスコーパス: between-study

1 d AMS and accounted for 28% of heterogeneity between studies.

2 alence of this disorder varies substantially between studies.

3 evention and treatment differed considerable between studies.

4 h the definition of never breastfed differed between studies.

5 ere not associated with survival differences between studies.

6 able effect estimates and consistent results between studies.

7 g conditions to enable comparison of results between studies.

8 comparing standardized uptake values (SUVs) between studies.

9 and the need for care when comparing results between studies.

10 eported measures of physical activity varies between studies.

11 ant characteristics, including age, differed between studies.

12 blication bias but significant heterogeneity between studies.

13 gh there was a large amount of heterogeneity between studies.

14 eotactic radiosurgery are imprecise and vary between studies.

15 however, the direction of association varied between studies.

16 es and to assess the extent of heterogeneity between studies.

17 6 (95% CI, 0.54-0.58) with no heterogenicity between studies.

18 dertake meta-analysis due to the differences between studies.

19 that account for methodological differences between studies.

20 ival outcomes, despite protocol similarities between studies.

21 reased in patients in LAL-CL01 and increased between studies.

22 mulative dose of drug exposure differ widely between studies.

23 dological limitations restrict comparability between studies.

24 chniques explained part of the heterogeneity between studies.

25 ificant heterogeneity (I(2) = 56%; P = 0.03) between studies.

26 efinition, and average duration of follow-up between studies.

27 Category limits were arbitrary and varied between studies.

28 y procedures with a minimum 5-year follow-up between studies.

29 terval 1.94-2.95) with evident heterogeneity between studies.

30 stic accuracy and natural history of plaques between studies.

31 variability in measurements both within and between studies.

32 py (CRT) appears to vary between indices and between studies.

33 ts models to take into account heterogeneity between studies.

34 ue definitions introduce further variability between studies.

35 d associated organic enrichment, vary widely between studies.

36 the disparity being evident both within and between studies.

37 ntly standardized to allow valid comparisons between studies.

38 here was, however, significant heterogeneity between studies.

39 s ratios (ORs) and investigate heterogeneity between studies.

40 Substantial heterogeneity existed between studies.

41 ants that actually show varying effect sizes between studies.

42 e of increased concentrations varied greatly between studies.

43 ) under an assumption of varying effect size between studies.

44 mutations in melanoma have been inconsistent between studies.

45 ted disease effects on the brain vary widely between studies.

46 determine whether ADC repeatability differed between studies.

47 lows to ensure comparability both within and between studies.

48 e contributed to the differences in efficacy between studies.

49 and is temporally stable over the two years between studies.

50 etection power and improving the consistency between studies.

51 prevalence estimates of these disorders vary between studies.

52 e limitations inherent in comparing outcomes between studies.

53 ssociated with a score of 1 differs markedly between studies.

54 nditions, which provides greater consistency between studies.

55 fore EVAR and after the treatment (mean time between studies, 7.6 months).

56 as a significant difference in repeatability between studies-a difference that did not persist after

57 iate inside genes and sources of variability between studies aimed at identifying these RNAs.

58 yses were performed to explore heterogeneity between studies and assess effects of study quality.

59 omparisons of pathogen transmission dynamics between studies and countries.

60 lycerols there was significant heterogeneity between studies and evidence of publication bias.

61 There is, however, considerable variability between studies and important methodologic shortcomings.

62 re shortening may explain variability in LTL between studies and individuals.

63 Limited comparability between studies and lack of specification of biomarker c

64 However, owing to heterogeneity within and between studies and limited sample sizes, findings on th

65 th the number and size of QTL likely to vary between studies and populations.

66 There was moderate heterogeneity between studies and potential for bias from poor-quality

67 use of considerable heterogeneity in results between studies and potential publication bias.

68 k prostate cancer to allow better comparison between studies and provide a more homogeneous assessmen

69 under an assumption of the same effect size between studies and the random-effects model (RE) under

70 acterial species or culture positivity rates between study and control eyes.

71 difference in trimethoprim resistance rates between study and control eyes: Four of 14 study eyes (2

72 No differences were detected between study and external coverage estimates.

73 At day 28, interactions between study and treatment group were NS.

74 To assess between-study and within-study associations, we used met

75 little heterogeneity (p(het)=0.13) in effect between studies, and good agreement with the effect of d

76 improve data quality, increase comparability between studies, and help reduce false positive and fals

77 of hopanes/steranes, with large variability between study areas.

78 Circulating FFA concentrations differed between study arms (0.05 +/- 0.04 mmol/L [low FFA] versu

79 verall survival at 2 years were no different between study arms (53% vs 45%, P = .06; 53% vs 54%, P =

80 of renal replacement therapy did not differ between study arms (6.9% for protocolized care and 4.3%

81 here were no significant outcome differences between study arms (overall survival [OS], P = .71; dise

82 ed within both groups but were not different between study arms (P = .115); changes in glucose tolera

83 glucose tolerance and HgbA1C did not differ between study arms (P = .920 and P = .650, respectively)

84 gness to accept an IRD kidney did not differ between study arms at tests 1 and 3.

85 oportion of active cytomegalovirus infection between study arms could lead to false-negative rates (b

86 The difference between study arms for this primary end point did not re

87 oportion of patients preferring comfort care between study arms immediately after the intervention.

88 There was no significant difference between study arms in 52-week mean change in FEV1 slope

89 antation (HCT) recently showed no difference between study arms in acute GVHD-free survival.

90 although there were significant differences between study arms in change from baseline to week 2 for

91 was associated with a significant difference between study arms in the change from baseline to week 4

92 n <5 y old (secondary outcome) were compared between study arms using three cross-sectional household

93 in severity and did not significantly differ between study arms.

94 Anti-HIV antibody titers did not differ between study arms.

95 s, and postoperative opioid use were similar between study arms.

96 lation rate (secondary outcome) was compared between study arms.

97 ficant difference in postoperative morbidity between study arms.

98 nown distribution of baseline mortality risk between study arms.

99 ponses against CEA was statistically similar between study arms.

100 d number of transplant patients were similar between study arms.

101 Baseline characteristics were balanced between study arms.

102 oals of therapy did not differ significantly between study arms.

103 valuation zones, with no apparent difference between study arms.

104 istics to allow appropriate comparison of NO between studies as a function of material and intended a

105 performed the Cochran test for heterogeneity between studies, Begg's funnel plot, and Egger test to a

106 ngs there are inconsistent results, not only between studies, but also between the immune effects of

107 at might explain variation in ESs within and between studies by adding study or ES characteristics as

108 These differences between studies can manifest as effect size heterogeneit

109 s to the size of the effect itself through a between-study coefficient of variation (CVB).

110 ich allow inspection of individual files and between-study comparison to identify systematic bias.

111 ample handling, and microarray platforms but between-study comparisons showed stronger agreement with

112 post hoc category selection and facilitates between-study comparisons.

113 efined standard BMI groupings can facilitate between-study comparisons.

114 data and differences in evaluation criteria between studies could have introduced bias.

115 lthough the reported incidence varies widely between studies depending on patient population, start a

116 reclinical studies to evaluate relationships between study design and experimental tumor volume effec

117 Mesothelin levels were standardized for between-study differences and age, after which the diagn

118 sted epicatechin, which explains most of the between-study differences in classical meta-analyses.

119 Post hoc analysis, between-study differences in patient characteristics, us

120 ingested epicatechin, which may explain the between-study differences.

121 es and exposure definitions made comparisons between studies difficult.

122 ly because of the inconsistent use of assays between studies, difficulties in specimen collection, an

123 Overall yields and results likely vary between studies due to differences in evaluation techniq

124 Results likely vary between studies due to variability of specific exposure-

125 tgomery-Asberg Depression Rating Scale score between study end and baseline) was correlated with bloo

126 HSV-2 IgG enzyme-linked immunosorbent assay between study enrollment and exit.

127 ions comparable in order to allow meaningful between-study evaluation.

128 of outcome presented important heterogeneity between studies, except for those studies reporting an i

129 rison found that at month 12, the difference between study eye minus fellow eye improvement in group

130 At month 18, the difference between study eye minus fellow eye improvement in our ac

131 Mean IOP difference between study eyes and fellow eyes increased from baseli

132 There was significant heterogeneity between studies for azathioprine risk estimates and the

133 imates of accuracy were highly heterogeneous between studies for the HDS but less so for the IHDS.

134 nsiderable heterogeneity in prevalence rates between studies; for late AMD, 20% of the variability in

135 Considerable heterogeneity existed between studies given the various definitions of laborat

136 No significant difference was found between study group (I) and control group.

137 nts' engagement in activities did not differ between study groups (coefficient 1.44, 95% CI -1.35 to

138 s in males were also significantly different between study groups (P <0.004).

139 each 30 ms group; insignificantly different between study groups (P = .98).

140 ms and significant concentration differences between study groups emphasize the importance of control

141 -SEM differences in the change over 6 months between study groups for PWT (0.9+/-0.8 minutes; 95% con

142 und no statistically significant differences between study groups in 3-dimensional echocardiography m

143 There was no significant difference between study groups in 60-day in-hospital mortality (28

144 We detected a significant difference between study groups in mean SDS-15 score change from ba

145 There were no significant differences between study groups in resident-reported perception of

146 Although no difference was observed between study groups in the number of hospital admission

147 The primary outcome was the difference between study groups in the proportion of isotonic cryst

148 prevalence of the negative control outcomes between study groups that would suggest undetected confo

149 erence in clinical malaria or vector density between study groups.

150 ard models to assess differences in outcomes between study groups.

151 hemoglobin, or use of additional uterotonics between study groups.

152 al stay and readmission rates did not differ between study groups.

153 ng secondary infection rates, did not differ between study groups.

154 ized, unobserved differences may still exist between study groups.

155 nsion did not significantly differ over time between study groups.

156 stay, and in-hospital mortality were similar between study groups.

157 effects models were used to compare outcomes between study groups.

158 n grade 3-4 treatment-related adverse events between study groups; the most common grade 3-4 adverse

159 s have been performed, significant variation between studies has made it difficult to assess regulati

160 Heterogeneity between studies has unfortunately prohibited pooling of

161 xible and offers an appropriate treatment of between-study heterogeneities that frequently arise in t

162 There was evidence of between-studies heterogeneity regarding sensitivity and

163 5% confidence interval [CI], 0.45-0.71) with between-study heterogeneity (P-heterogeneity = 0.006; I(

164 bias and there was no evidence of important between-study heterogeneity (p=0.21).

165 individuals, 95% CI, 25.3%-32.5%), with high between-study heterogeneity (Q = 1247, tau2 = 0.39, I2 =

166 There was moderate between-study heterogeneity but no evidence of publicati

167 We estimated the between-study heterogeneity expressed by I(2) (defined a

168 fects meta-analyses were done to investigate between-study heterogeneity in percentage of late-stage

169 There was substantial between-study heterogeneity in secondary analyses of tri

170 There was marked between-study heterogeneity in the ER+ estimates in both

171 There was wide between-study heterogeneity in the percentage of late-st

172 There was strong evidence of between-study heterogeneity in the prevalence of PPH and

173 No indications of significant between-study heterogeneity or publication bias, respect

174 obust and unlikely to be notably affected by between-study heterogeneity or publication bias.

175 Very high between-study heterogeneity ruled out a fixed-effects mo

176 when underpowered studies were omitted; and between-study heterogeneity tended to decrease.

177 Bayesian mixed-effects model to account for between-study heterogeneity to estimate temporal indirec

178 r case-mix or cluster effects and quantified between-study heterogeneity using I.

179 Between-study heterogeneity was assessed by using the Co

180 Between-study heterogeneity was assessed using the I(2)

181 Between-study heterogeneity was assessed using the I2 st

182 ere employed to calculate summary estimates, between-study heterogeneity was evaluated using I(2) sta

183 Between-study heterogeneity was mild (I(2) < 50%).

184 Considerable between-study heterogeneity was observed for most outcom

185 Similarly, large between-study heterogeneity was observed for PR+ and HER

186 Between-study heterogeneity was partially explained by w

187 Notably, high levels of between-study heterogeneity were recorded for most cytok

188 P values for the tests for nonlinearity and between-study heterogeneity when there was strong confou

189 , methods for data pooling, investigation of between-study heterogeneity, and quality of reporting.

190 However, there was considerable between-study heterogeneity, which could not be fully ac

191 dom-effects meta-analysis, which allowed for between-study heterogeneity.

192 a-regression to explore potential sources of between-study heterogeneity.

193 warrants caution because of the considerable between-study heterogeneity.

194 fixed-effects models according to tests for between-study heterogeneity.

195 isk using random-effects models to allow for between-study heterogeneity.

196 ts (P = 0.03), and there was low evidence of between-study heterogeneity.

197 Multivariable models varied between studies; however, most reported a further reduct

198 Genomic data are well correlated between studies; however, the measured drug response dat

199 as control variable) explained heterogeneity between studies (I(2) 89.3%; p<0.0001).

200 0.78-0.88) with no evidence of heterogeneity between studies (I(2) = 1.0%, P = 0.416).

201 nstances explained <15% of the heterogeneity between studies (I(2) = 36-75%).

202 27, p=0.020), with significant heterogeneity between studies (I(2)=77%, p<0.0001), including signific

203 with CI-AKI, although the effect size varied between studies (I(2)=93.5%).

204 There was substantial heterogeneity between studies in both the pooled sensitivity and speci

205 patients promise to fill an important niche between studies in humans and model organisms in deciphe

206 Two methods of quantifying heterogeneity between studies in meta-analysis were studied.

207 There was marked heterogeneity between studies in study design (cross-sectional versus

208 ferences by study design or study quality or between studies in Western and non-Western countries.

209 Prognostic factors that varied between studies included age, comorbid anxiety and depre

210 (IQR, 20-52), with no significant difference between study intervals (P = .18).

211 Rates were compared between study interventions (prednisone, mitoxantrone, a

212 analysis and the comparison of AP parameters between studies is hindered by the lack of standardized

213 However, consistency between studies is lacking.

214 d quality control practices, the low overlap between studies is primarily due to false negatives rath

215 Discrepancies between studies may be caused by differences in study de

216 analyses were used to identify associations between study measures and site and participant characte

217 A significant amount of heterogeneity between study methodology and results restricted the sco

218 ndividually, pooling serotype data within or between studies (models 2 and 3).

219 rgeting of this region can be quite variable between studies of appetitive behavior, even within the

220 Minimal heterogeneity was apparent between studies of Asian populations.

221 affect heat mortality fills an important gap between studies of individual susceptibility to heat and

222 lence estimates did not significantly differ between studies of only preclinical students and studies

223 These findings strengthen the connection between studies of theta-band activity in rodents and hu

224 This hinders comparison between studies of this widely used quality improvement

225 ar outcomes differed significantly (P<0.001) between studies of whites not undergoing PCI (relative r

226 The work fills the missing gap between studies on enzyme biophysics and network level d

227 There was good agreement between studies on the sequence of treatment using the l

228 cells (Tregs) may explain the discrepancies between studies on Tregs in physiology and pathology.

229 t is difficult to compare clustering results between studies or to identify the key experimental or d

230 The relationship between study outcome and publication status was examine

231 There was substantial variation between studies (p<0.001 across all variables), and most

232 60 (95% CI 0.40-0.89), with no heterogeneity between studies (p=0.52).

233 Variations in relative mortality risks between study participants and the general population co

234 immunoglobulin classes varying considerably between studies, perhaps because of different detection

235 Two-month mortality was also similar between study phases among 252 TB cases (17% vs. 14%, di

236 ric TB treatment rates, and disease severity between study phases.

237 d ICU-free days did not significantly differ between study phases.

238 There were observed differences between studied potatoes with respect to dry matter, sta

239 , including survival bias, and heterogeneity between studies preclude statistical comparisons concern

240 and three for breast cancer, but differences between studies precluded combining the data for meta-an

241 the concordance index was very heterogeneous between studies, principally because of differing age ra

242 cardial infarction, elevated troponin levels between studies, prior coronary artery bypass grafting,

243 er, sample size, multiple testing within and between studies, publication bias and the expectation th

244 After testing for heterogeneity between studies (Q = 5.61; P = .23), we pooled findings

245 There was significant heterogeneity between studies regarding SCC risk estimates for aspirin

246 ant to the comparison of h and pi within and between studies remain to be assessed.

247 was mixed, and there was high heterogeneity between study results, possibly due to variation in food

248 There was high heterogeneity between study results, possibly due to variation in stud

249 ance of the effect estimate due to variation between studies (RI), and the other calibrated the varia

250 The high heterogeneity between studies should be explored further using improve

251 ntroduced to model effect size heterogeneity between studies should help future GWAS that combine ass

252 , with statistically significant differences between study sites and sources of information used to l

253 Commonalities between study sites lead us to propose a five-phase mode

254 nfall, explains differences in Hg deposition between study sites located in the eastern United States

255 omic assemblage of vegetation differs widely between study sites, a functional classification of plan

256 nd quality of cataract surgeries vary widely between study sites, often with no obvious explanation.

257 ficile pathogens will increase comparability between studies so that important epidemiologic linkages

258 However, the between-study standard deviation of 0.18 (95% CrI: 0.10,

259 = 0.12 (SE = 0.03, n(studies) = 34), with a between-study standard deviation tau = 0.16.

260 6.5% (95% CI, 42.7%-50.3%), with significant between-study statistical heterogeneity (I2 = 99.5%; tau

261 regression was used to estimate association between study success rate and total citation count, adj

262 etected associations show higher consistency between studies than recently proposed methods.

263 we aim to sharpen the analytic distinctions between studies that directly evaluate policies and thos

264 fferent definitions, complicating comparison between studies that use DGF as an endpoint.

265 Related to methodological differences between studies, the cost of pressure ulcer prevention a

266 (RI), and the other calibrated the variance between studies to the size of the effect itself through

267 However, inconsistencies exist between studies using these animal models, specifically

268 HCV testing interval could account for most between-study variability in the estimated probability o

269 , a random effects regression indicated that between-study variability was not significantly accounte

270 ion including 5 variables explained 99.6% of between-study variability, revealing an association betw

271 ssion explained approximately one-quarter of between-study variance in effect size.

272 b) = 11, n(field) = 23), as are estimates of between-study variance tau(2) (tau(lab)(2) - tau(field)(

273 , geographical location explained 57% of the between-study variance, with CTT significantly longer in

274 In meta-regression, age explained 52% of the between-study variance, with older age associated with l

275 was 18% (95% CI, 14%-21%), with significant between-study variation (I2 = 96.8%; P < .001).

276 d meta-regression model explained 51% of the between-study variation in the 25 included risk estimate

277 d by Genovese et al. to incorporate tests of between-study variation into the meta-analysis context.

278 hese analyses is that the data exhibit small between-study variation or that this heterogeneity can b

279 t 6-month intervals, at study visits, and in between study visits during the trial (P < .01 for all).

280 e and ears at semiannual study visits and in between study visits was recorded.

281 he number of ranibizumab retreatments at and between study visits were also analyzed.

282 mples at each study visit, the time interval between study visits, the requirement of an additional v

283 during telephone calls at 6-month intervals between study visits.

284 the Newcastle-Ottawa Scale and heterogeneity between studies was also evaluated.

285 The risk of bias and variability between studies was assessed to be low.

286 effect modeling, and extent of heterogeneity between studies was determined with the Cochran Q and I(

287 Extent of heterogeneity between studies was determined with the I(2) test.

288 The heterogeneity between studies was high, and participation rates varied

289 The large heterogeneity between studies was partially addressed by creating a st

290 dren to HFSS food advertising did not change between study weeks 1 and 2 (odds ratio (99% confidence

291 l viewers to HFSS food advertising increased between study weeks 1 and 2 (odds ratio (99% confidence

292 In cross-study analysis, models transferred between studies were in some cases less accurate than mo

293 Images with the longest interval between studies were selected for further review.

294 in mean nonheme-iron absorption (0.7-22.9%) between studies, which depended on iron status (diet had

295 f selected VOCs allowed 100% differentiation between studied wines, showing that high levels of 1-hex

296 Performance measures were compared between studies with low BPE (mild or minimal) and those

297 There was no difference in infection rates between studies with low or high baseline rates (P = .18

298 There were no significant differences between studies with low versus high BPE in sensitivity

299 There was disagreement between studies with regard to cardiac output because of

300 Trials where compared between study years and geographical regions.

301 ng isolates, there was a lack of PFT overlap between study years, combat zones, and military treatmen