ライフサイエンスコーパス: between-study

1 I test was used to evaluate heterogeneity between studies.

2 Effects on folate and vitamin A varied between studies.

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

4 ere not associated with survival differences between studies.

5 blication bias but significant heterogeneity between studies.

6 ntly standardized to allow valid comparisons between studies.

7 mutations in melanoma have been inconsistent between studies.

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

9 determine whether ADC repeatability differed between studies.

10 lows to ensure comparability both within and between studies.

11 e contributed to the differences in efficacy between studies.

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

13 etection power and improving the consistency between studies.

14 prevalence estimates of these disorders vary between studies.

15 e limitations inherent in comparing outcomes between studies.

16 ging due to high variability both within and between studies.

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

18 nditions, which provides greater consistency between studies.

19 alence of this disorder varies substantially between studies.

20 evention and treatment differed considerable between studies.

21 h the definition of never breastfed differed between studies.

22 able effect estimates and consistent results between studies.

23 g conditions to enable comparison of results between studies.

24 comparing standardized uptake values (SUVs) between studies.

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

26 eported measures of physical activity varies between studies.

27 ant characteristics, including age, differed between studies.

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

29 eotactic radiosurgery are imprecise and vary between studies.

30 activity distributions varied significantly between studies.

31 however, the direction of association varied between studies.

32 for each outcome and assessed heterogeneity between studies.

33 his bacterial community differs considerably between studies.

34 falciparum malaria that varied significantly between studies.

35 but have been hindered by lack of agreement between studies.

36 ever, its effectiveness significantly varies between studies.

37 er, the markers affected are often different between studies.

38 ces of heterogeneity in prevalence estimates between studies.

39 fy covariates that account for heterogeneity between studies.

40 th caution because of moderate heterogeneity between studies.

41 discovered phenotypes differed substantially between studies.

42 plained by differences in exercise intensity between studies.

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

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

45 Dose differences between study acquisitions and the influence of BMI were

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

47 strong with decreasing levels of association between studied and test items.

48 f results, because disease spectrum can vary between studies and affects relative test performance.

49 surveillance with consistent cluster naming between studies and allows for outbreak detection using

50 nover data were used to guide the comparison between studies and appropriate measures of central tend

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

52 ssessed the heterogeneity in slope estimates between studies and conducted additional sensitivity ana

53 n for the variation in vaccine effectiveness between studies and countries of vaccine effectiveness o

54 omparisons of pathogen transmission dynamics between studies and countries.

55 measures of quality, including heterogeneity between studies and evidence for publication bias.

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

57 dicitis in children, to reduce heterogeneity between studies and facilitate data synthesis and eviden

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

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

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

61 s for liver MR elastography in children vary between studies and may differ from thresholds in adults

62 RS analyses, which can lead to inconsistency between studies and misinterpretation of results.

63 re has limitations regarding reproducibility between studies and pathologists, potentially masking su

64 in G6PD activity measurements were compared between studies and pooled across the dataset.

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 nd provide a framework for comparing results between studies and reconciling observed differences in

69 is hindering the ability to compare results between studies and sometimes leading to errant conclusi

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

71 in mean change in total GA area at month 12 between study and fellow eyes (1.07 +/- 0.84 mm(2) vs. 2

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

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

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

75 forage and milk, in addition to differences between studied areas.

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

77 in PSA DT and median MFS were not different between study arms (18.9 v 18.3 months; hazard ratio [HR

78 Two-year MFS was not different between study arms (41.8% vaccine v 42.3%; P = .97).

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 ischarge was 3.4 g/kg/day and did not differ between study arms (Delta 0.0 g/kg/day; 95% CI -0.4 to 0

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 ), this was seen globally with no difference between study arms [odds ratio (OR) 0.96 (0.74-1.25)].

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

86 facility, we found no significant difference between study arms for any economic outcome.

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

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

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

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

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

92 There was no difference between study arms in terms of placenta malaria after ad

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

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

95 Differences between study arms were modeled using multivariable logi

96 first trimester did not differ significantly between study arms.

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

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

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

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

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

102 ficant difference in postoperative morbidity between study arms.

103 nown distribution of baseline mortality risk between study arms.

104 ponses against CEA was statistically similar between study arms.

105 delivery, antibody responses did not differ between study arms.

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

107 nitude of this association has been observed between studies, but sources of this variation are poorl

108 These differences between studies can manifest as effect size heterogeneit

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

110 The discrepancies between studies could be explained by limitations of the

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

112 e days through day 28, and (3) clinical cure between study days 7 and 10 for VABP; and (1) survival (

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

114 odological issues, focusing on the interplay between study design, analysis strategy, and the fact th

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

116 imary safety outcome was measured blood loss between study drug administration and transfer to the po

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

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

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

120 nd 43 (0.3%) of 16 369 matched controls died between study enrolment and the follow-up visit at about

121 a used, and this may account for differences between studies especially in some population groups suc

122 There was substantial heterogeneity between study estimates.

123 peak overlap in mRNAs varies from ~30 to 60% between studies, even in the same cell type.

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

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

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

127 hemical stress response and the associations between studied factors can advance algorithm developmen

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

129 studies, there was significant heterogeneity between studies for nearly all clinical outcomes.

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

131 cy of En/DMT and TCT maps in differentiating between studied groups.

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

133 score at 9 months (cross-sectional analysis) between study groups (group 2 vs group 1, difference in

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

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

136 polyamines or related metabolites were found between study groups after 26 wk of intervention and no

137 There was a significant interaction between study groups and changes over time for total ski

138 (P < 0.05), with no significant differences between study groups at all time-points (P > 0.05).

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

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

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

142 ere no statistically significant differences between study groups in any of the secondary participant

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

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

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

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

147 There was no significant difference between study groups in the rate of change of low-lumina

148 no differences in questionnaire return rates between study groups or between women who did and did no

149 red lower respiratory tract symptoms (LRTSs) between study groups over the first 4 days of infection.

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

151 m, intrapartum, and postpartum complications between study groups using hierarchical logistic regress

152 s (ie, age at administration of the vaccine) between study groups were included in the analyses, beca

153 erence in viral suppression was not superior between study groups, an a-priori test for non-inferiori

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

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

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

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

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

159 om the at-risk population at different rates between study groups.

160 n pregnancy or infant outcomes were observed between study groups.

161 re made to maintain differences in treatment between study groups.

162 Baseline characteristics were well balanced between study groups.

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

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

165 of moderate or low quality, and substantial between-studies heterogeneity remained unexplained.

166 OR 2.63 [95% CI 1.13-6.14], p=0.026) with no between-study heterogeneity (I(2)=0%, chi(2) p=0.91).

167 rences in length of follow-up explained most between-study heterogeneity (inital I(2) ranged from 0 t

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

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

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

171 We observed considerable between-study heterogeneity for both end points (I2 > 90

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

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

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

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

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

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

178 We also quantitate the large between-study heterogeneity that exists in this literatu

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

180 n exposure-response curve (ERC) and quantify between-study heterogeneity using all available quantita

181 Between-study heterogeneity was assessed with Cochran's

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

183 Substantial between-study heterogeneity was found.

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

185 Considerable between-study heterogeneity was observed for most outcom

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

187 rs associated with prevalence and sources of between-study heterogeneity were determined using meta-r

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

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

190 led estimates is limited by the considerable between-study heterogeneity.

191 ssion analysis was also conducted to examine between-study heterogeneity.

192 mmon in cohort studies but with considerable 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 as control variable) explained heterogeneity between studies (I(2) 89.3%; p<0.0001).

199 0.78-0.88) with no evidence of heterogeneity between studies (I(2) = 1.0%, P = 0.416).

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

201 27, p=0.020), with significant heterogeneity between studies (I(2)=77%, p<0.0001), including signific

202 ver, we will only be able to compare results between studies if we use a common set of Ez-based metri

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

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

205 There was also heterogeneity between studies in the nature of the control group utili

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

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

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

209 ated with respect to posology and comparison between studies is facilitated.

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

211 A comparison between studies is problematic, due to differences in th

212 y survey is unique, but the main commonality between studies is response rate.

213 For this reason, the contrast between study items that are later recollected with thei

214 The variation in laboratory methods between studies made comparisons difficult.

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

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

217 We explore niche partitioning between studied NE cats and the contemporary native Euro

218 at baseline and follow-up with a median time between studies of 1.5 months.

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 n for seemingly conflicting results obtained between studies of different brain diseases where P2Y(1)

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

227 60 (95% CI 0.40-0.89), with no heterogeneity between studies (p=0.52).

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

229 There were 3 transmission linkages between study participants.

230 methodological differences limit comparison between studies, particularly for immunity and risk fact

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

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

233 The analysis was limited by heterogeneity between study populations and lack of data from very low

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

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

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

237 While birth length (103-110 cm) was similar between study regions, TL estimates at 1, 3, 12, and 25

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

239 itions, is required to improve comparability between study settings and to demonstrate the influence

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

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

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

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

244 s of inbreeding depression are also observed between study sites with different night-light intensity

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

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

247 ave failed to find statistically significant between-study spatial convergence, other than transdiagn

248 = 0.12 (SE = 0.03, n(studies) = 34), with a between-study standard deviation tau = 0.16.

249 6.5% (95% CI, 42.7%-50.3%), with significant between-study statistical heterogeneity (I2 = 99.5%; tau

250 etrospectively investigated the relationship between study subject characteristics and rates of nonco

251 The mean polygenic risk scores (PRSs) between study subjects with and without rare CNVs were c

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

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

254 Low concordance between studies that examine the role of microbiota in h

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

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

257 sed algorithms to model dynamic interactions between study units within and across levels and are cha

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

259 We distinguish between studies using ventilated or nonventilated caging

260 Crucially, the observed between-study variability is accounted for by design qua

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

262 Moderators of between-study variability were assessed using mixed-effe

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

264 six DE genes identified by the w(P6)weighted between study variance model could be potentially down-r

265 the DSLR2w(P6)weighted and the w(P6)weighted between study variance models.

266 with high precision, while the w(P6)weighted between-study variance models were appropriate for detec

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

268 or OS and DFS hazard ratios (HR), estimating between-study variance with restricted maximum likelihoo

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

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

271 rithms, weighted common effect, and weighted between-study variance.

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

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

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

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

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

277 for lower airway microbiota (median 35 days between study visits) in the largest longitudinal study

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

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

280 Heterogeneity between studies was assessed by Cochran's (Q) and I2 sta

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

282 Heterogeneity between studies was assessed using Cochrane Q test and I

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

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

285 Heterogeneity between studies was estimated using the I(2) index.

286 Risk of bias between studies was generally low.

287 : 0.52, 0.72; P < 0.0001), but heterogeneity between studies was high (I2 = 73.8%).

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

289 was 80% (95% CI 75.5-84.8) and heterogeneity between studies was moderate (I(2)=56.96%).

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

291 Publication bias and heterogeneity between studies were assessed.

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

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

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

297 s a systematic framework for closing the gap between studies with purified enzymes and their effects

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

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

300 The concentrations of components varied between study years, indicating strong effects of enviro