ライフサイエンスコーパス: generalized estimating equation

1 composite outcome after surgery (p < 0.05 by generalized estimating equation).

2 ar regression accounting for clustering with generalized estimating equation.

3 gh multivariable logistic regression using a generalized estimating equation.

4 d or dampness indicators were assessed using generalized estimating equations.

5 Data were analysed longitudinally using generalized estimating equations.

6 hort (n = 1456) using log-linear models with generalized estimating equations.

7 assessed using linear regression models with generalized estimating equations.

8 rginal, exact generalized linear models with generalized estimating equations.

9 ocardiographic measures were evaluated using generalized estimating equations.

10 fidence intervals were obtained using linear generalized estimating equations.

11 intent to treat with linear mixed models and generalized estimating equations.

12 els; cognitive impairment was compared using generalized estimating equations.

13 ifest refraction (MRx) using the t test with generalized estimating equations.

14 e from chronic infections was assessed using generalized estimating equations.

15 tions with participant characteristics using generalized estimating equations.

16 periods following stent implantation, using generalized estimating equations.

17 and total expenditures were estimated using generalized estimating equations.

18 ed between groups (intent-to-treat) by using generalized estimating equations.

19 ion and 30-day readmission was modeled using generalized estimating equations.

20 y using logistic regression without and with generalized estimating equations.

21 MRW were measured and compared by race using generalized estimating equations.

22 using the Cox proportional hazards model and generalized estimating equations.

23 ction, and comorbidities, using multivariate generalized estimating equations.

24 estimated using a phase of care approach and generalized estimating equations.

25 -class virologic failure were analyzed using generalized estimating equations.

26 and risk for methylation was assessed using generalized estimating equations.

27 were assessed using logistic regression with generalized estimating equations.

28 compared with that on PET/MR images by using generalized estimating equations.

29 e in stenosis scoring was evaluated by using generalized estimating equations.

30 Risks were quantified using generalized estimating equations.

31 Regression models used generalized estimating equations.

32 zone (PZ) and transition zone (TZ) by using generalized estimating equations.

33 e odds ratio (OR) of a CCI score >/= 4 using generalized estimating equations.

34 matory biomarker changes were compared using generalized estimating equations.

35 multiple IVF cycles in the same woman using generalized estimating equations.

36 ormed using segmented regression models with generalized estimating equations.

37 linear and logistic regression models using generalized estimating equations.

38 (CSF) and FC-CSF use were analyzed by using generalized estimating equations.

39 on to heredity and sex were calculated using generalized estimating equations.

40 (c) logistic random-effects models, and (d) generalized estimating equations.

41 baseline tests were estimated using Poisson generalized estimating equations.

42 evalence ratios (aPRs) were calculated using generalized estimating equations.

43 d disease characteristics was assessed using generalized estimating equations.

44 n was explored using logistic regression and generalized estimating equations.

45 using multivariable Poisson regression with generalized estimating equations.

46 onding index date for control subjects using generalized estimating equations.

47 Data analyses were conducted using generalized estimating equations.

48 with the Phansalkar method was analyzed with generalized estimating equations.

49 to sensitization ever were calculated using generalized estimating equations.

50 the Fisher exact test and linear models with generalized estimating equations.

51 ultivariable logistic regression models with generalized estimating equations accounting for hospital

52 Differences in quality were determined using generalized estimating equations adjusted for 8 physicia

53 Models based on generalized estimating equations adjusted for baseline c

54 Generalized estimating equations adjusted for correlatio

55 Generalized estimating equations adjusted for demographi

56 We used generalized estimating equation-adjusted regression to c

57 Analyses were by generalized estimating equations adjusting for childhood

58 is included modified Poisson regression with generalized estimating equations, adjusting for age, sex

59 Changes were compared using generalized estimating equations, adjusting for baseline

60 Generalized estimating equations allowed for clustered d

61 was assessed at baseline and follow-up using generalized estimating equation among 4,212 older Chines

62 of time since program initiation by logistic generalized estimating equation analyses (July 2009 thro

63 Linearized generalized estimating equation analyses related materna

64 Multivariable generalized estimating equation analyses were conducted

65 Multivariate generalized estimating equation analyses were conducted

66 tions between HPV types were investigated by generalized estimating equation analyses.

67 and neurologic function using bivariate and generalized estimating equation analyses.

68 ) subsequent annual changes in eGFR by using generalized estimating equation analyses.

69 Multivariate Generalized Estimating Equations analyses were conducted

70 Generalized estimating equations analyses were used to t

71 Univariate and multivariate generalized estimating equations analyses with correctio

72 We used generalized estimating equation analysis to examine the

73 Generalized estimating equation analysis was done to ass

74 malignant) nodules were compared by means of generalized estimating equations analysis.

75 models, multiple linear regression using the generalized estimating equation and linear mixed-effect

76 Multivariable generalized estimating equation and mediation regression

77 c and pigmented melanoma were compared using generalized estimating equations and Cox regression mode

78 Generalized estimating equations and Cox regression were

79 surveillance mammography using multivariable generalized estimating equations and evaluated the impac

80 Generalized estimating equations and logistic regression

81 Generalized estimating equations and logistic regression

82 Generalized estimating equations and logistic regression

83 Logistic regression with generalized estimating equations and mediation analysis

84 aging features and DT were assessed by using generalized estimating equations and mixed model analyse

85 Data were analyzed with generalized estimating equations and mixed-model analyse

86 -sectional associations were estimated using generalized estimating equations and multivariable linea

87 Generalized estimating equations and multivariate logist

88 Generalized estimating equations and nonparametric boots

89 ferent CD4 strata; Poisson regression, using generalized estimating equations and robust standard err

90 estimated by Poisson regression models with generalized estimating equations and robust variance est

91 Logistic regression with generalized estimating equations and stabilized inverse-

92 , and peak on Mondays in ICD therapies using generalized estimating equations and Student t tests.

93 ed at sextant- and patient-based levels with generalized estimating equations and the Wilcoxon rank s

94 responses to environmental exposure using a generalized estimating equation approach that assumes ex

95 The generalized estimating equation approach was used to dea

96 ivariable logistic regression model with the generalized estimating equation approach.

97 that were compared between groups, using the generalized estimating equation approach.

98 We used logistic regression models under a generalized estimating equations approach to explore the

99 Generalized estimating equations assuming a negative bin

100 Poisson regression with generalized estimating equations calculated the relative

101 Generalized estimating equation clustering by hospital s

102 associated with the overreporter phenotype; generalized estimating equations compared 6MP intake by

103 (length of stay, length of ventilation) and generalized estimating equations (daily PICU cumulative

104 Logistic regression with generalized estimating equations estimated adjusted odds

105 Generalized estimating equations estimated relative risk

106 Logistic regression with generalized estimating equations estimated the odds rati

107 Generalized estimating equations examined associations o

108 mortality and morbidity were analyzed using generalized estimating equations for binary outcomes.

109 Generalized estimating equations for logistic regression

110 Generalized estimating equations for logistic regression

111 Citation counts were compared using generalized estimating equations for Poisson regression.

112 Linear mixed models and generalized estimating equations for repeated measuremen

113 a proportional odds logistic regression with generalized estimating equations (for Katz activities of

114 Results were adjusted (generalized estimating equations) for multiple episodes.

115 alysis, Cox proportional hazards models, and generalized estimating equation (GEE) analysis.

116 al DNA methylation and IR was examined using generalized estimating equation (GEE) and within-twin pa

117 analysed with Logistic regression using the Generalized Estimating Equation (GEE) approach.

118 sed on the Poisson multivariate longitudinal Generalized Estimating Equation (GEE) model, each 10 mg

119 Generalized estimating equation (GEE) models informed th

120 We used logistic regression and generalized estimating equation (GEE) models to evaluate

121 We fitted Generalized Estimating Equation (GEE) regression models

122 es/mL and rates of HIV drug resistance using generalized estimating equations (GEE) and extended Cox

123 A multivariate generalized estimating equations (GEE) model with a bino

124 We used generalized estimating equations (GEE) to examine associ

125 s of the primary outcome was conducted using generalized estimating equations (GEE) to examine the as

126 Generalized Estimating Equations (GEE) were used to comp

127 Generalized estimating equations (GEE) were used to esti

128 -2 plasma levels and PGD was evaluated using generalized estimating equations (GEE).

129 In this study, motivated by the well-known generalized estimating equations (GEEs) for longitudinal

130 Poisson models fit with generalized estimating equations (GEEs) were used to est

131 Remission status was analyzed with generalized estimating equations (GEEs), a patient-based

132 onsisted of related subjects, we implemented generalized estimating equations (GEEs), an extension of

133 The statistical models used included generalized estimating equations (GEEs), latent class gr

134 Secondary outcomes assessed with generalized estimating equations included gingival index

135 ific predictors of these phenotypes by using generalized estimating equations, latent class mixed mod

136 Generalized estimating equation log-linear models were u

137 , multivariate-adjusted odds ratio (OR) from generalized estimating equation logistic analysis compar

138 vel and was compared between groups by using generalized estimating equation logistic regression mode

139 ' characteristics by fitting a multivariable generalized estimating equation logistic regression mode

140 Generalized estimating equation logistic regression mode

141 rs of timely treatment were determined using generalized estimating equations logistic regression mod

142 Generalized estimating equations logistic regression was

143 Generalized estimating equations logistic regression was

144 able logistic regression analyses, using the generalized estimating equation method, to adjust for re

145 The generalized estimating equations method was used to calc

146 ete linear logistic regression modeling with generalized estimating equation methods to account for c

147 Generalized estimating equation methods were used for lo

148 ssion, multivariate logistic regression, and generalized estimating equation methods.

149 al data on PM<10 mum in diameter (PM10), and generalized estimating equations methods adapted for low

150 rs conducted between-group comparisons using generalized estimating equation, mixed-effects models, o

151 testing for categorical comparisons, and the generalized estimating equation model to control for non

152 A generalized estimating equation model was used to detect

153 A generalized estimating equation model with an interactio

154 timistic than the actual meaning (P < 0.001; generalized estimating equation model).

155 were compared between groups using a Poisson generalized estimating equation model.

156 ords and related to adverse outcomes using a generalized estimating equation model.

157 using backwards selection in a multivariable generalized estimating equation model.

158 The adjusted generalized estimating equations model that accounted fo

159 A generalized estimating equations model was used to analy

160 The generalized estimating equations model was used to analy

161 Statistical analysis was performed using a generalized estimating equations model, Wald chi(2) test

162 Data were analyzed using generalized estimating equation modeling adjusting for c

163 Multivariable logistic generalized estimating equations modeling demonstrated t

164 n time, 0.18 [0.1] vs 0.33 [0.09]; P < .001, generalized estimating equation models accounting for ag

165 Generalized estimating equation models accounting for fa

166 Using generalized estimating equation models adjusted for pote

167 ith GA incidence using eye-specific data and generalized estimating equation models adjusting for age

168 sted relative rates (ARRs) were generated by generalized estimating equation models and adjusted for

169 progression to late AMD were assessed using generalized estimating equation models and eye-specific

170 Generalized estimating equation models assessed lung fun

171 nt of first and second malaria episodes, and generalized estimating equation models estimated malaria

172 Factor analysis and generalized estimating equation models for binary repeat

173 Multivariable generalized estimating equation models of readmission ad

174 pared between FECD and control eyes by using generalized estimating equation models to adjust for age

175 We used generalized estimating equation models to examine the lo

176 Generalized estimating equation models were applied to e

177 Generalized estimating equation models were used to acco

178 Generalized estimating equation models were used to acco

179 Generalized estimating equation models were used to anal

180 Generalized estimating equation models were used to anal

181 Generalized estimating equation models were used to comp

182 Piecewise generalized estimating equation models were used to comp

183 Multivariate generalized estimating equation models were used to dete

184 treated for hypertension during pregnancy?" Generalized estimating equation models were used to esti

185 Multivariate generalized estimating equation models were used to esti

186 Generalized estimating equation models were used to esti

187 Generalized estimating equation models were used to gene

188 Using generalized estimating equation models, a relation betwe

189 variables that were identified in bivariate generalized estimating equation models, and maintained s

190 Generalized estimating equation models, which were adjus

191 In generalized estimating equation models, with whites and

192 omparisons between groups were made by using generalized estimating equation models.

193 tality in intensive care units, we performed generalized estimating equation models.

194 rated <12 months previously) were made using generalized estimating equation models.

195 with those of age-matched controls, by using generalized estimating equation models.

196 Changes after DSEK were analyzed using generalized estimating equation models.

197 Generalized estimating equations models were conducted t

198 Multivariable logistic generalized estimating equations models were constructed

199 Generalized estimating equations models were used to est

200 Paired t-tests, ANOVA and generalized-estimating-equations models were used to com

201 ividual studies and a meta-analysis, using a generalized estimating equation, on the entire data set.

202 Data were analysed with generalized estimating equations or logistic regression:

203 Generalized estimating equation Poisson models were used

204 We used a generalized estimating equation Poisson regression model

205 s and visual outcome were analyzed using the generalized estimating equations procedure.

206 alyzed by descriptive statistics followed by generalized estimating equation regression modeling.

207 Generalized estimating equation regression models were u

208 Generalized estimating equation regression models were u

209 using treatment x time interaction terms in generalized estimating equation regression models.

210 Generalized estimating equation regressions of polyp cha

211 is) were modeled using linear regression and generalized estimating equations, respectively.

212 and clinical factors were examined by using generalized estimating equations separately for CE spect

213 Modeling using generalized estimating equations showed that methylation

214 analysis of the total study population using generalized estimating equations showed that the twins w

215 Generalized estimating equations suggested that PERG amp

216 Generalized estimating equations (that adjusted for diag

217 mpared for residual stool and fluid by using generalized estimating equations; the Mann-Whitney test

218 We used a generalized estimating equation to evaluate associations

219 Logistic regression with generalized estimating equations to account for clusteri

220 Patient-level analyses were conducted using generalized estimating equations to account for clusteri

221 Multiple logistic regression analysis (with generalized estimating equations to account for inter-ey

222 We used generalized estimating equations to account for potentia

223 score to adjust for confounding, as well as generalized estimating equations to account for repeated

224 rated using Poisson regression estimated via generalized estimating equations to account for repeated

225 hma by using logistic regression models with generalized estimating equations to calculate adjusted o

226 We used generalized estimating equations to calculate the preval

227 We used generalized estimating equations to compare outcomes bet

228 We also used generalized estimating equations to compare the adjusted

229 We used generalized estimating equations to compare the groups o

230 We used generalized estimating equations to determine associatio

231 We used generalized estimating equations to determine census tra

232 We used log-linear models with generalized estimating equations to estimate adjusted re

233 We used generalized estimating equations to estimate association

234 ed measures, we used linear mixed models and generalized estimating equations to estimate association

235 However, when using generalized estimating equations to estimate CQS control

236 We used generalized estimating equations to estimate the odds ra

237 We used logistic regression with generalized estimating equations to examine the associat

238 We used log-binomial regression and generalized estimating equations to examine the associat

239 We used generalized estimating equations to examine the relation

240 We used generalized estimating equations to examine treatment in

241 ated from log-linked Poisson regression with generalized estimating equations to explore differences

242 We used generalized estimating equations to model shared and all

243 We used generalized estimating equations to test associations of

244 dex, and Kellgren/Lawrence grade, as well as generalized estimating equations, to evaluate the effect

245 through January 1, 2016, and analyzed using generalized estimating equations (Tweedie log-link for t

246 The generalized estimating equation was used for 3 years of

247 Generalized estimating equation was used to identify opt

248 Logistic regression with a generalized estimating equation was used to provide risk

249 tivariable linear regression with the use of generalized estimating equations was applied to evaluate

250 Multivariable logistic regression with generalized estimating equations was used to assess pred

251 Logistic regression with generalized estimating equations was used to calculate t

252 multivariable logistic regression model with generalized estimating equations was used to examine whe

253 Multiple logistic and linear regression with generalized estimating equations was used to explore the

254 Logistic regression with generalized estimating equations was used to model assoc

255 A population-averaged model (generalized estimating equations) was used to model the

256 With generalized estimating equations, we analysed the effect

257 Using logistic regression with generalized estimating equations, we included associated

258 Using Poisson regression with generalized estimating equations, we measured the associ

259 Generalized estimating equations were applied to ascerta

260 Marginal models using generalized estimating equations were applied.

261 Multivariate linear regression analyses with generalized estimating equations were performed after pr

262 Generalized estimating equations were performed to asses

263 analysis and multiple linear regression with generalized estimating equations were performed to deter

264 Generalized estimating equations were performed to ident

265 ortality and morbidity associations, whereas generalized estimating equations were used for CD4 T cel

266 , morbidity, and nutritional outcomes, while generalized estimating equations were used to analyze CD

267 Generalized estimating equations were used to analyze de

268 was used to analyze percentiles of BMI, and generalized estimating equations were used to analyze th

269 Linear models fit with generalized estimating equations were used to assess the

270 Generalized estimating equations were used to compare ad

271 Generalized estimating equations were used to compare me

272 Generalized estimating equations were used to compare th

273 Generalized estimating equations were used to compare th

274 Multivariable log-binomial models with generalized estimating equations were used to compute ri

275 Generalized estimating equations were used to determine

276 Generalized estimating equations were used to estimate a

277 Generalized estimating equations were used to estimate o

278 variable log-binomial regression models with generalized estimating equations were used to estimate R

279 Linear and binomial regression with generalized estimating equations were used to estimate t

280 Multivariable logistic regressions with generalized estimating equations were used to estimate t

281 Linear models with generalized estimating equations were used to estimate t

282 Generalized estimating equations were used to evaluate t

283 Generalized estimating equations were used to examine th

284 Generalized estimating equations were used to examine th

285 Generalized estimating equations were used to examine th

286 Logistic regression and generalized estimating equations were used to identify f

287 Generalized estimating equations were used to identify f

288 Linear models with generalized estimating equations were used to identify r

289 Generalized estimating equations were used to model asso

290 Linear mixed models and generalized estimating equations were used to model cont

291 Generalized estimating equations were used to model the

292 Generalized estimating equations were used to test predi

293 Generalized estimating equations were used to test the a

294 Logistic regression model-based generalized estimating equations were used.

295 We used generalized estimating equations with a logit link to ev

296 Generalized estimating equations with adjustment for age

297 We used linear mixed models or generalized estimating equations with adjustment for pot

298 RRs and 95% CIs were estimated by using generalized estimating equations with log-binomial model

299 Analyses were done by using generalized estimating equations with logistic regressio

300 es for each modality were estimated by using generalized estimating equations with logit link functio