ライフサイエンスコーパス: random-effects model

1 initial endoscopy) among BE cohorts, using a random effects model.

2 among the three groups was predicted using a random effects model.

3 Individual effect sizes were pooled using a random effects model.

4 disease burden, and mortality status using a random effects model.

5 as synthesized through meta-analysis using a random effects model.

6 Summary estimates were calculated using a random effects model.

7 ooled RRs, using maximally adjusted RRs in a random effects model.

8 , tumor response, and adverse events using a random effects model.

9 c relative risks (RRs) were combined using a random effects model.

10 alysis of the risk of adverse events using a random effects model.

11 med following MOOSE guidelines and using the random effects model.

12 Data were pooled using a random effects model.

13 re extracted and effect sizes pooled using a random effects model.

14 ummary relative risks were estimated using a random effects model.

15 d using aggregated data meta-analysis with a random effects model.

16 astle-Ottawa scale and meta-analyzed using a random effects model.

17 lated complications, and pooled them using a random effects model.

18 utcomes were derived using a binomial-normal random-effects model.

19 Study-specific outcomes were combined per random-effects model.

20 atio (RR) estimates were synthesized under a random-effects model.

21 ls with FAS, we did meta-analyses assuming a random-effects model.

22 Outcomes were pooled using a random-effects model.

23 Meta-analysis was performed using the random-effects model.

24 We calculated pooled VE using a random-effects model.

25 ooled the accuracy numbers using a bivariate random-effects model.

26 crude risk ratios were pooled (cpRR) using a random-effects model.

27 by 3 independent observers and combined by a random-effects model.

28 sus poor collaterals for outcomes based on a random-effects model.

29 across cohorts were pooled with the use of a random-effects model.

30 ific risk estimates were combined by using a random-effects model.

31 Study-specific results were pooled using a random-effects model.

32 to pool results with a hierarchical Bayesian random-effects model.

33 and 11 at p values less than 0.001 under the random-effects model.

34 ly combined into a pooled odds ratio using a random-effects model.

35 revalence estimates with a DerSimonian-Laird random-effects model.

36 .3% of the total variance in an unstructured random-effects model.

37 d by the inverse of variance and merged in a random-effects model.

38 We did meta-analyses using a bivariate random-effects model.

39 Summary RRs were estimated by using a random-effects model.

40 ps and related to a range of factors using a random-effects model.

41 s for the outcome using a profile-likelihood random-effects model.

42 y and alcohol intakes were pooled by using a random-effects model.

43 I(2) = 69.3%, P = 0.001) with the use of the random-effects model.

44 were pooled separately for CD and UC using a random-effects model.

45 sion in patients with MCI was pooled using a random-effects model.

46 pooled risk ratios (RR), and 95% CIs with a random-effects model.

47 Treatment effect was analyzed using a random-effects model.

48 ere pooled on their odds ratios (ORs) with a random-effects model.

49 dy-specific results were combined by using a random-effects model.

50 c estimates were subsequently pooled using a random-effects model.

51 ucted separately for each outcome by using a random-effects model.

52 c accuracy of various NITs using a bivariate random-effects model.

53 from individual studies were pooled using a random-effects model.

54 ive risk and 95% confidence interval using a random-effects model.

55 nces in lipid levels were calculated using a random-effects model.

56 and were subsequently meta-analyzed using a random-effects model.

57 lative risks (RRs) were calculated under the random-effects model.

58 pooled risk ratios (RR), and 95% CIs with a random-effects model.

59 ) and 95% confidence intervals (CIs) using a random-effects model.

60 ated from individual study estimates using a random-effects model.

61 calculated pooled odds ratios (ORs) using a random-effects model.

62 (RRs) for adverse events, were assessed in a random-effects model.

63 n vs. low of Mediterranean diet score with a random-effects model.

64 of 0.95 (95% CI, 0.96-0.99) using a 2-sample random-effects model.

65 anagement across studies was determined by a random-effects model.

66 ough meta-analysis with the application of a random-effects model.

67 fidence interval (CI) were estimated using a random-effects model.

68 inomial regression models and pooled using a random-effects model.

69 A meta-analysis was conducted using a random-effects model.

70 did a meta-analysis using a Mantel-Haenszel random-effects model.

71 We pooled all data using a random-effects model.

72 We conducted meta-analyses using a random-effects model.

73 ntervals were extracted and analyzed using a random-effects model.

74 e variance or Mantel-Haenszel methods with a random-effects model.

75 RRs and 95% CIs were pooled using a random-effects model.

76 oradiotherapy or resection, were pooled in a random-effects model.

77 Data were pooled using a random-effects model.

78 ervals (CI) were pooled across studies using random effects models.

79 in inflammation markers were assessed using random effects models.

80 ncluding all 35 studies were conducted using random effects models.

81 c inverse variance method and both fixed and random effects models.

82 tios (ORs) were estimated by either fixed or random effects models.

83 stratified by time periods and pooled using random effects models.

84 ies to estimate the pooled average SMD using random effects models.

85 intervals by performing meta-analysis using random effects models.

86 Meta-analyses used random effects models.

87 s for OS of LCC vs RCC according to fixed or random-effects models.

88 pooled odds ratios (ORs) for infection using random-effects models.

89 s for OS of LCC vs RCC according to fixed or random-effects models.

90 Effect size data were pooled using random-effects models.

91 ncidence and mortality were calculated using random-effects models.

92 on and pooled across cohorts with the use of random-effects models.

93 led with the use of generic inverse-variance random-effects models.

94 Summary relative risks were calculated using random-effects models.

95 es and moderator variables were tested using random-effects models.

96 s of the odds ratio (OR) were obtained using random-effects models.

97 omputed from studies and meta-analyzed using random-effects models.

98 ors, was used; pooled analyses were based on random-effects models.

99 mean differences (SMDs) were calculated with random-effects models.

100 -specific risk estimates were combined using random-effects models.

101 ratio [RR]) were calculated using fixed- and random-effects models.

102 assessed, with data pooled across RCTs using random-effects models.

103 ffect estimates were pooled using fixed- and random-effects models.

104 Data were pooled using random-effects models.

105 ns and deaths per disease category by use of random-effects models.

106 ontrol group were pooled across trials using random-effects models.

107 ps between PA and HF risk were assessed with random-effects models.

108 c symptoms were evaluated using longitudinal random-effects models.

109 We derived pooled risk ratios (RRs) with random-effects models.

110 l studies were meta-analyzed using bivariate random-effects models.

111 by hormonal contraception formulation using random-effects models.

112 tween the metformin and control groups using random-effects models.

113 zes of eligible studies were pooled by using random-effects models.

114 Main outcomes were pooled using random-effects models.

115 data by using inverse-variance methods with random-effects models.

116 ted at the subject level for both fixed- and random-effects models.

117 All pooled analyses were based on random-effects models.

118 s of continuous outcomes across trials using random-effects models.

119 ds ratios (ORs) were obtained using fixed or random-effects models.

120 analysis of binomial data and analysed using random-effects models.

121 eric inverse variance method with the use of random-effects models.

122 Data were pooled using fixed- and random-effects models.

123 d serotype-specific estimates using Bayesian random-effects models.

124 across studies for direct comparisons using random-effects models.

125 Meta-analyses were conducted using random-effects models.

126 Correlation coefficients were pooled using random-effects models.

127 Summary estimates were obtained using random-effects modeling.

128 the study-specific hazard ratios (HRs) using random-effects modeling.

129 meta-analysis was performed using a Bayesian random-effects model; 137 studies comprising 33,243 part

130 The random effects model adjusting for baseline BCVA was the

131 Linear random effects models, adjusting for the clustering of h

132 Where sufficient data were available, a random-effects model analyzed the standard mean differen

133 erization was analyzed with inverse variance random effects modeling and expressed as risk ratios and

134 th the unified model (comprising a bivariate random-effects model and a hierarchical summary receiver

135 alysed data by pairwise meta-analyses with a random-effects model and by network meta-analysis.

136 We did pair-wise meta-analyses using the random-effects model and then did a random-effects netwo

137 Results were pooled using a random-effects model and used to calculate 5-year recurr

138 ed effect size of efficacy, according to the random-effects model and weighted for the number of pati

139 ing the generic inverse-variance method with random-effects models and expressed as mean differences

140 use of generic inverse-variance methods with random-effects models and expressed as risk ratios with

141 Final analyses using random-effects models and I2 to assess heterogeneity wer

142 c relative risks (RRs) were aggregated using random-effects models and were grouped by study-level ch

143 l data from population-based studies using a random effects model (and quantified inconsistency using

144 Meta-analyses on aggregated study data (random-effects model) and individual patient data (IPD)

145 Data were summarized using the random effects model, and heterogeneity was explored usi

146 assessed study quality, pooled data using a random-effects model, and performed subgroup and sensiti

147 between 1997 and 2014 were pooled by using a random-effects model, and potential heterogeneity was ex

148 Pooled analysis was done using a random-effects model, and quality of the studies was ass

149 Data were analyzed using a random-effects model, and represented by pooled odds rat

150 strategies were pursued: marginal modeling, random-effects modeling, and fixed-effects modeling.

151 Meta-analyses were conducted using random-effects modeling, and heterogeneity assessed usin

152 Seroconversion risk data were pooled using random-effects models, and associations explored through

153 oled relative risks (RRs) with 95% CIs using random-effects models, and did subgroup analyses by part

154 fidence intervals (CIs) were estimated using random-effects models, and heterogeneity was assessed us

155 tive traits, the use of fixed-effects versus random-effects models, and the removal of shadow associa

156 with standardised mean differences (SMD) and random-effects models, and used Stata (version 12) for m

157 A Bayesian spatial random-effects modeling approach was used to analyze inf

158 milar, meta-analysis was performed using the random-effects model by DerSimonian and Laird.

159 Random-effects models compared FI score trajectories by

160 In random-effects models controlling for between-person dif

161 ith the use of weighted mean differences and random-effects models.Data were extracted from 14 trials

162 A random effects model demonstrated that ICD use was assoc

163 Pooled analysis using a random-effects model demonstrated a significant improvem

164 We used a logistic random-effects model designed to test within-person and

165 We calculated pooled effect estimates with a random effects model, evaluated the risk of bias using a

166 and 95% CIs were estimated with the use of a random effects model for high-intake compared with low-i

167 We used the bivariate random effects model for quantitative meta-analysis of t

168 Meta-analysis using a random effects model for weighting individual effect siz

169 ty using the Jadad scoring system and used a random-effects model for pooled data analysis.

170 We used a random-effects model for the meta-analyses of specific p

171 We used a random-effects model for the statistical analyses.

172 We used random-effects models for all meta-analyses.

173 We generated random-effects models for analysis and evaluated for pub

174 ta-analyses were carried out with the use of random-effects models for the lumbar spine and femoral n

175 erm improvements than did the control diets (random-effects model) for waist circumference (mean diff

176 Pooling results from a bivariate random-effects model gave sensitivity and specificity es

177 al [ CI confidence interval ]: 50.0%, 64.1% [random-effects model]; I(2) = 90.9%; P < .0001).

178 Data were pooled using a random-effects model in a Bayesian setting.

179 itical appraisal, data were analyzed using a random-effects model in a Mantel-Haenszel test or invers

180 ted for each day and each biomarker, using a random-effects model in cases of heterogeneity.

181 The random effects model indicated that providing mobile hea

182 Meta-analysis with a random-effects model indicated a significant advantage f

183 nificant, positive elasticity for fixed- and random-effects models: lower-income Europe, India and th

184 erived using cross-sectional or longitudinal random-effects models may be biased due to unmeasured co

185 We performed random effects model meta-analyses and meta-regressions.

186 A random effects model meta-analysis showed a significant

187 Random-effects model meta-analyses of effect sizes were

188 f bias of included studies was appraised and random-effects model meta-analyses were performed to syn

189 Random-effects model meta-analyses were used to estimate

190 th versus without anti-HBs were estimated in random-effects model meta-analyses.

191 rom the eligible studies were pooled using a random-effects model meta-analysis, with heterogeneity a

192 Subgroup analysis and multivariate random-effects model meta-regression was also implemente

193 The authors used the random effects model of meta-analysis to combine the stu

194 We used random-effects models of the odds ratio (OR) based on a

195 A meta-analysis (random-effects model) of data from 19 of 25 trials (n =

196 is was performed using the DerSimonian-Laird random effects model (OpenMetaAnalyst 10.10 for OS.X).

197 1A methylation status and HNSCC risk under a random-effects model (OR = 2.93, 95% CI: 1.58-5.46).

198 Using random effects models, outlier sites were identified bas

199 With the use of a linear random-effects model, prenatal MM-exposed children susta

200 We previously developed a modified random effects model (RE2) that can achieve higher power

201 o estimate Cohen's d, which was entered into random effects models (REM) to compare CC with NC, CC wi

202 ic resonance imaging data and compared using random effects model selection.

203 Random-effects model showed that, although there is a tr

204 We did sensitivity analyses using random-effects models, stratifying by iron-folic acid do

205 We used pooled random-effects models, subgroup comparisons, and meta-re

206 number needed to treat=8), according to the random-effects model, suggesting a relative advantage in

207 Results were robust in sibling random effects models that account for family background

208 performing a complete-case analysis using a random-effects model that includes IV-confounders.

209 In random-effects models that included the 5 higher-quality

210 a 2-level meta-meta-analytic approach with a random effects model to allow for intra- and inter-meta-

211 We used a random effects model to calculate summary estimates for

212 We used random effects models to analyse cross-sectional associa

213 We used random effects models to analyze overall survival (OS) a

214 nge was related to group attendance, we used random effects models to assess associations between out

215 Meta-analysis was by the random-effects model to account for the substantial vari

216 ling with meta-analysis, we pooled using the random-effects model to analyze the relative risks.

217 For each of these eight PCBs, we used a random-effects model to apportion total variation into r

218 did a meta-analysis with a DerSimonian-Laird random-effects model to calculate a pooled estimate of h

219 We used a random-effects model to calculate overall estimates of e

220 eta-analysis of available trial data using a random-effects model to calculate overall hazard ratios

221 te vs post-acute care hospitals), and used a random-effects model to calculate pooled statistics (pro

222 We did the meta-analyses with a random-effects model to calculate standardised mean diff

223 We used random-effects model to compare pooled outcomes and test

224 of each algorithm were then analyzed with a random-effects model to derive Bland-Altman-type limits

225 We used a random-effects model to derive overall excess risk.

226 s and 1,829,256 control participants, used a random-effects model to find no significant association

227 Then, we used the random-effects model to obtain the overall OR and its 95

228 We used a random-effects model to pool odds ratios.

229 We used a random-effects model to pool risk ratios.

230 ork meta-analyses (NMA) were performed using random-effects modeling to obtain estimates for study ou

231 systematic review and used fixed-effects and random-effects modeling to undertake meta-analyses.

232 isks to produce a pooled relative risk using random-effects models to allow for between-study heterog

233 ), we estimated separate logistic regression random-effects models to assess whether patterns of expo

234 We used random-effects models to derive pooled relative risk (RR

235 We used random-effects models to derive pooled relative risk (RR

236 morrhagic stroke using DerSimonian and Laird random-effects models to model any alcohol intake or dos

237 We performed meta-analyses using random-effects models to obtain pooled estimates, strati

238 identify baseline predictors of outcome, and random-effects models to pool estimates in a meta-analys

239 We used the DerSimonian and Laird random-effects models to pool hazard ratios (HRs) with 9

240 We used random-effects models to provide point estimates (95% co

241 range of meta-analysis methods, such as the random effects model, to account for overlapping subject

242 A random effects model was created, and statistical hetero

243 timate, when available, were reported, and a random effects model was run to account for clustering o

244 A random effects model was used and odds ratios (ORs) were

245 Random effects model was used to calculate the effect si

246 For the meta-analysis, a bivariate random effects model was used to jointly model sensitivi

247 onal studies, the pooled odds ratio from the random-effects model was 1.18 (95% CI, 1.06-1.30), with

248 A fixed- or random-effects model was used depending on heterogeneity

249 A random-effects model was used for the analyses.

250 A random-effects model was used for the analysis.

251 A random-effects model was used in all the analysis.

252 A random-effects model was used to calculate a pooled esti

253 A fixed-effects or random-effects model was used to calculate the pooled od

254 A random-effects model was used to estimate the average in

255 A random-effects model was used to pool outcomes across st

256 Random-effects model was used to pool risk estimations.

257 Random-effects modeling was used for all analyses.

258 Linear regression random-effects modeling was used to examine associations

259 Using a random effects model, we examined the impact of sociodem

260 Using random-effects models, we assessed the effects of nonpol

261 Using random-effects models, we computed separate pooled estim

262 Meta-analysis with a random effects model were conducted to examine an overal

263 In the second stage of the meta-analysis, random effects models were applied using summary-level e

264 Random effects models were constructed to pool HIV incid

265 Meta-analyses using random effects models were used to analyse the data.

266 Linear random effects models were used to assess the associatio

267 Meta-analyses using random effects models were used to calculate overall est

268 Random effects models were used to generate pooled risk

269 Meta-analyses that used the random-effects model were performed on diet quality, die

270 etecting influenza A from Bayesian bivariate random-effects models were 54.4% (95% credible interval

271 Random-effects models were employed to calculate summary

272 ng the Newcastle-Ottawa Scale, and fixed- or random-effects models were implemented.

273 Random-effects models were used to compare healthy subje

274 Random-effects models were used to compare outcomes betw

275 Random-effects models were used to estimate pooled effec

276 DerSimonian and Laird random-effects models were used to estimate relative ris

277 DerSimonian and Laird random-effects models were used to report pooled risk ra

278 Random-effects models were used to summarize relative ri

279 Random-effects models were used to summarize the risk ra

280 ts) or euglycemic clamp (three cohorts), and random-effects models were used to test the association

281 Random-effects models were used.

282 Fixed-effect models were used, and random-effects models where significant heterogeneity wa

283 The proposed method relies on random effects modeling with maximum likelihood estimate

284 Random effects models with DerSimonian-Laird weights wer

285 In the first stage, we used random effects models with individual patient data to as

286 Statistical analysis comprised a random-effects model with associated heterogeneity analy

287 A meta-analysis was performed using a random-effects model with effect sizes weighted by the s

288 pooled for all studies by using a bivariate random-effects model with exploration involving subgroup

289 A random-effects model with Mantel-Haenszel method was use

290 A random-effects model with restricted maximum-likelihood

291 Random-effects models with DerSimonian-Laird weights wer

292 We used both fixed-effects and random-effects models with inverse variance weighting fo

293 surement following topical application using random-effects models with inverse variance weighting to

294 and 95% CIs were pooled by fixed-effect and random-effects models with inverse variance weighting.

295 Meta-analysis was conducted using random-effects models with quality-of-evidence assessmen

296 For all meta-analyses we used random-effects models with the exact binomial likelihood

297 in rankings occurred after adjusting with a random-effects model, with lower volume hospitals moving

298 e performed meta-analysis of factors using a random-effects model, with results expressed as odds rat

299 e performed meta-analysis of factors using a random-effects model, with results expressed as odds rat

300 -analysis was performed, using the fixed- or random-effects model, with Review Manager 5.1.