ライフサイエンスコーパス: random-effects model

1 gnition were identified using meta-analysis (random-effects modeling).

2 identified by pooled incidence rate using a random effects model.

3 mate using the inverse variance method and a random effects model.

4 Results were pooled using a random effects model.

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

6 Summary estimates were calculated using a random effects model.

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

8 Summary RRs (95% CIs) were estimated using a random effects model.

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

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

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

12 ere meta-analyzed using an inverse variance, random effects model.

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

14 For meta-analysis, we used a random effects model.

15 e pooling of the data was undertaken using a random effects model.

16 Studies were meta-analysed using a random effects model.

17 ulated and combined for meta-analysis by the Random Effects model.

18 d risks were between 7.2% and 13.7% with the random effects model.

19 Meta-analysis of data was conducted using a random effects model.

20 Q test and I(2) test statistics based on the random effects model.

21 t network meta-analysis was conducted with a random-effects model.

22 ds ratio (OR) or mean difference (MD) with a random-effects model.

23 s or serum antibodies were calculated with a random-effects model.

24 were combined by using a profile likelihood random-effects model.

25 re pooled using an inverse-variance-weighted random-effects model.

26 vascular disease associated with HCV using a random-effects model.

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

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

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

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

31 Outcomes were pooled using a random-effects model.

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

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

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

35 nce intervals (CIs) were calculated with the random-effects model.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

50 Data were pooled using a random-effects model.

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

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

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

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

55 confidence interval) was performed using the random-effects model.

56 RFS), and overall recurrence rates using the random-effects model.

57 Effect sizes were pooled using a random-effects model.

58 Summary means were generated using a random-effects model.

59 Meta-analyses were conducted using a random-effects model.

60 The meta-analysis was performed with a random-effects model.

61 with 95% confidence intervals (CI), using a random-effects model.

62 e 0.76 (0.71-0.82) and 0.81 (0.73-0.89) in a random-effects model.

63 ence intervals (CIs) were calculated using a random-effects model.

64 We used a random-effects model.

65 ed mean differences (SMDs) with the use of a random-effects model.

66 were pooled with a generic inverse variance random-effects model.

67 sitivity and specificity) were pooled with a random-effects model.

68 We assessed pooled data using random-effects model.

69 luded studies pooled using DerSimonian-Laird random-effects model.

70 Summary estimates were calculated using a random-effects model.

71 utcomes between RDN and control groups using random effects models.

72 point estimates were then combined by using random effects models.

73 Meta-analyses used random effects models.

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

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

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

77 in inflammation markers were assessed using random effects models.

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

79 risk ratios (RR) for SSI were obtained using random effects models.

80 -response meta-analyses were conducted using random effects models.

81 to HIV-uninfected women were estimated using random-effects models.

82 tudies using restricted, maximum-likelihood, random-effects models.

83 ived pooled estimates using inverse-variance random-effects models.

84 s) were estimated by using DerSimonian-Laird random-effects models.

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

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

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

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

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

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

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

92 re generated using inverse-variance weighted random-effects models.

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

94 Correlation coefficients were pooled using random-effects models.

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

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

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

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

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

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

101 s test, and variance quantified using linear random-effects models.

102 s were performed using DerSimonian and Laird random-effects models.

103 s and pooled outcomes using fixed-effect and random-effects models.

104 Meta-analyses were performed using random-effects models.

105 tios (ORs) with 95% CI were calculated using random-effects models.

106 DPP-4 inhibitors, which were pooled by using random-effects models.

107 and summary estimates were determined using random-effects models.

108 The proportion of AEs was pooled using random-effects models.

109 Data were pooled with (inverse variance) random-effects models.

110 Summary estimates were calculated using random-effects models.

111 luded in multivariate linear regression with random effects modeling.

112 ctors using bivariate linear regression with random effects modeling.

113 ted, and a meta-analysis was performed using random-effects modeling.

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

115 alysis of proportions was performed by using random-effects modeling.

116 CV heart." Summary means were generated with random-effects modeling.

117 rse variance method and data were pooled via random-effects modelling.

118 izes with time-step fixed effects and clinic random effects (Model 1).

119 tep interaction term (Model 2) or individual random effects (Model 3).

120 Data were pooled using a random effects model (95% confidence interval), and the

121 Random effects models accounted for village clustering e

122 usted model, and -6.64 (-7.95 to -5.33) in a random-effects model accounting for cluster randomisatio

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

124 Data were synthesized with a random effects model and a frequentist approach.

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

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

127 ized through meta-analysis with the use of a random-effects model and data presented as standardized

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

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

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

131 kthrough reaction rates were determined with random-effects modeling and meta-regression.

132 k were included and data were analyzed using random-effects models and classified by the Grading of R

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

134 sing the generic inverse variance method and random-effects models and expressed as mean differences

135 linical outcomes were pooled with the use of random-effects models and heterogeneity was assessed wit

136 Percentage change in BMD was pooled using random-effects models and reported as weighted mean diff

137 pooled odds ratios (ORs) with 95% CIs using random-effects models and used meta-regression to invest

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

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

140 s (RRs) with 95% CIs were calculated using a random effects model, and Mantel-Haenszel method was use

141 We pooled the data using a random effects model, and reported efficacy and safety o

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

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

144 rent co-occurring conditions in autism using random-effects models, and descriptively compared these

145 The primary meta-analysis using a random effects model assessed AF recurrence stratified b

146 Random-effects models, based on inverse variance weights

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

148 Random-effects models compared FI score trajectories by

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

150 Data were analyzed using random-effects models controlling for potential confound

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

152 A random effects model demonstrated that ICD use was assoc

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

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

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

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

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

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

159 were pooled using the Dersimonian and Laird random-effects model for effects of PrEP on HIV infectio

160 differences in variability, we calculated a random-effects model for measures of variance ratios.

161 We conducted separate meta-analyses using a random-effects model for mortality and hospital admissio

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

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

164 Effect sizes were calculated using random-effects models for cognitive outcomes classified

165 Prevalences were pooled using random-effects models for meta-analyses of binomial data

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

167 on all scales combined with both a standard random effects model: (g = 0.26; P = 0.02; k = 22; CI =

168 ong CS-born children (hazard ratio (HR) from random effects model, HR 1.10, 95% confidence interval (

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

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

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

172 The random effects model indicated that providing mobile hea

173 Results from the random-effects model indicated a significant standardize

174 nterval [CI]) for association with GD from a random-effects model is 1.23 (95%CI: 1.16-1.30) for fata

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

176 The random effects model meta-analysis revealed that both ph

177 Random effects model meta-analysis was performed, with e

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

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

180 eak coordinates to calculate effect sizes, a random-effects model meta-analysis was performed with th

181 A random-effects model meta-analysis was used for data syn

182 us outcome with 95% CI were calculated using random-effects model meta-analysis.

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

184 Random-effects models meta-analysis was used.

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

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

187 ed across studies with the DerSimonian-Laird random-effects model or a Bayesian meta-analysis model.

188 ence intervals (CIs) were calculated using a random-effects model, overall and by geographic region a

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

190 Pooled effect estimates were calculated with random effects models, risk of bias and strength of evid

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

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

193 We meta-analysed survival estimates using a random effects model stratified according to whether rec

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

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

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

197 als were included in the meta-analysis using random effects models through the generic inverse varian

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

199 We used a random effects model to analyse the overall effects of s

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

201 We used a random effects model to synthesise the rate data, and re

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

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

204 non-parametric bootstrapping and multilevel random effects models to estimate incremental mean costs

205 i-allelic inverse-variance-weighted fixed or random effects models to generate effect estimates and 9

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

207 We previously introduced a random-effects model to analyze a set of patients, each

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

209 We used a random-effects model to calculate disease-specific relat

210 We performed a meta-analysis using a random-effects model to calculate estimates of pooled in

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

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

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

214 We used a random-effects model to compare disparate outcome measur

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

216 The second method employs a random-effects model to estimate both the population and

217 We did a meta-analysis using a random-effects model to estimate overall hazard ratios (

218 We used a random-effects model to examine the relationship between

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

220 We used a random-effects model to obtain pooled HRs.

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

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

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

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

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

226 We used Linear spline random-effects models to estimate BP patterns across pre

227 We used random-effects models to estimate pooled relative risks.

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

229 We used random-effects models to obtain summary relative risks (

230 We used random-effects models to obtain weighted pooled estimate

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

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

233 We used random-effects models to summarize the studies.

234 The random effects model was equivalent to QRISK3 for discri

235 each parameter and state of accommodation, a random effects model was fitted to estimate differences

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

237 Comprehensive Meta-analysis 3.0 using the random effects model was used for data analysis.

238 Random effects model was used to analyze data and meta-r

239 Random effects model was used to calculate the effect si

240 The random effects model was used to combine the effect size

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

242 A random effects model was used to pool compliance.

243 The random effects model was used to pool the calculated eff

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

245 A random-effects model was applied to pooled estimates and

246 Random-effects model was conducted in the meta-analysis.

247 A random-effects model was used because of possible study

248 A random-effects model was used for statistical analysis.

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 random-effects model was used to calculate pooled effect

254 The random-effects model was used to estimate pooled RR and

255 e, and operations; the DerSimonian and Laird random-effects model was used to pool calculated risk ra

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

257 a was examined using I (2) statistics, and a random-effects model was used to summarize data.

258 A fixed- or random-effects model was used, along with subgroup and m

259 > 25%, Cochrane Q statistic p value < 0.05), random-effects model was used.

260 Using an inverse variance random effects model, we calculated the proportion of di

261 Using the random effects model, we computed the effect sizes (ESs)

262 Using random-effects models, we examined the overall associati

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

264 Random effects models were used for all analyses.

265 Random effects models were used for meta-analyses.

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

267 Linear random effects models were used to assess the associatio

268 Random effects models were used to estimate pooled odds

269 Random effects models were used to estimate summary rela

270 Random effects models were used to synthesize data.

271 e of R software and RevMan software based on random-effects model were used for analyses.

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

273 Random-effects models were calculated to estimated summa

274 Random-effects models were created separately for odds a

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

276 Meta-analyses using random-effects models were performed to identify the pro

277 Random-effects models were used to calculate pooled risk

278 Hedge g with random-effects models were used to determine pooled effe

279 Random-effects models were used to determine the pooled

280 Random-effects models were used to estimate pooled effec

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

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

283 Random-effects models were used.

284 Metaanalysis was performed using a bivariate random-effects model when at least 5 studies were includ

285 kelihood ratios across studies or univariate random-effects models when bivariate models failed to co

286 Random effects models with DerSimonian-Laird weights wer

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

288 Meta-analyses were performed using random effects models with inverse variance weighing.

289 gulation asymmetry were assessed using mixed random effects models with random intercept.

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

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

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

293 Meta-analyses with a fixed- and random-effects model with inverse variance method, DerSi

294 A random-effects model with Mantel-Haenszel weighting was

295 A random-effects model with the I(2) statistic was used to

296 Random-effects models with DerSimonian-Laird weights wer

297 lyses were performed using DerSimonian-Laird random-effects models with inverse variance weighting.

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

299 itative data synthesis was performed using a random-effects model, with standardized mean difference

300 ble-adjusted effect estimates were pooled by random-effects models, with credibility assessment accor