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 be dealt with using standard methods (e.g., generalized estimating equations).

3 gh multivariable logistic regression using a generalized estimating equation.

4 ar regression accounting for clustering with generalized estimating equation.

5 differences among groups were compared using generalized estimating equation.

6 dietary pattern and ECC were estimated using generalized estimating equation.

7 ts in INS; associations were estimated using generalized estimating equations.

8 y using multivariable regression models with generalized estimating equations.

9 ed using a negative binomial regression with generalized estimating equations.

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

11 Data analyses were conducted using generalized estimating equations.

12 ocardiographic measures were evaluated using generalized estimating equations.

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

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

15 baseline tests were estimated using Poisson generalized estimating equations.

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

17 d disease characteristics was assessed using generalized estimating equations.

18 and posttreatment uBPA concentrations using generalized estimating equations.

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

20 using multivariable Poisson regression with generalized estimating equations.

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

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

23 to sensitization ever were calculated using generalized estimating equations.

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

25 Data were analysed longitudinally using generalized estimating equations.

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

27 assessed using linear regression models with generalized estimating equations.

28 es in QoL was assessed for each metric using generalized estimating equations.

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

30 fidence intervals were obtained using linear generalized estimating equations.

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

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

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

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

35 tions with participant characteristics using generalized estimating equations.

36 s for each VMI feature were determined using generalized estimating equations.

37 periods following stent implantation, using generalized estimating equations.

38 and total expenditures were estimated using generalized estimating equations.

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

40 was assessed via univariate and multivariate generalized estimating equations.

41 offspring asthma status were assessed using generalized estimating equations.

42 nical variables on %Delta was evaluated with generalized estimating equations.

43 en exposure and outcome were estimated using generalized estimating equations.

44 ty was assessed by logistic regression using generalized estimating equations.

45 virally suppressed PLWH were assessed using generalized estimating equations.

46 ed using a negative binomial regression with generalized estimating equations.

47 level, with a negative binomial model using generalized estimating equations.

48 using multivariable logistic regression with generalized estimating equations.

49 d using multivariate Poisson regression with generalized estimating equations.

50 eline age = 22.97 years) were analyzed using generalized estimating equations.

51 nally measured behaviors was estimated using generalized estimating equations.

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 ses using log-linked Poisson regression with generalized estimating equations adjusted for design cov

55 ol subjects was assessed using multivariable generalized estimating equations, adjusted for age, sex,

56 Using distributed-lag generalized estimating equations, adjusted for seasonali

57 sociation with CMV endpoints were made using generalized estimating equation-adjusted linear models.

58 We used generalized estimating equation-adjusted regression to c

59 Analyses were by generalized estimating equations adjusting for childhood

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

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

62 Generalized estimating equations allowed for clustered d

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

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

65 Linearized generalized estimating equation analyses related materna

66 Multivariable generalized estimating equation analyses were conducted

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

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

69 Generalized estimating equations analyses were used to t

70 Generalized estimating equation analysis was done to ass

71 ment measures, ART group, age, and RTI using generalized estimating equation and generalized linear m

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

73 Multivariable generalized estimating equation and mediation regression

74 17) cohorts were analyzed using multivariate generalized estimating equations and a modified Poisson

75 valuated with linear regression models using generalized estimating equations and controlling for dem

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

77 Generalized estimating equations and Cox regression were

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

79 nd spoke characteristics were analyzed using generalized estimating equations and Kendall taubeta non

80 Generalized estimating equations and logistic regression

81 Generalized estimating equations and marginal modeling w

82 Logistic regression with generalized estimating equations and mediation analysis

83 Generalized estimating equations and nonparametric boots

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

85 Generalized estimating equations and Spearman's rank cor

86 Logistic regression with generalized estimating equations and stabilized inverse-

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

88 The generalized estimating equation approach was used to dea

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

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

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

92 Generalized estimating equations assuming a negative bin

93 Poisson regression with generalized estimating equations calculated the relative

94 Generalized estimating equation clustering by hospital s

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

96 patterns were assessed longitudinally using generalized estimating equations controlling for age and

97 Generalized estimating equations estimated relative risk

98 Logistic regression with generalized estimating equations estimated the odds rati

99 Generalized estimating equations examined associations o

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

101 Generalized estimating equations for logistic regression

102 d MacNemar chi(2) test for paired sample and generalized estimating equations for modeling the Vessel

103 Generalized estimating equations for Poisson regression

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

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

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

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

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

109 Generalized estimating equation (GEE) models informed th

110 We used generalized estimating equation (GEE) models to analyze

111 Generalized estimating equation (GEE) models were used t

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

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

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

115 We applied the generalized estimating equations (GEE) to examine the as

116 Generalized Estimating Equations (GEE) were used to comp

117 Generalized estimating equations (GEE) were used to esti

118 and within group analysis was done by using generalized estimating equations (GEE).

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

120 erences (aRDs) with cluster adjustment using generalized estimating equations (GEEs) using difference

121 Negative binomial generalized estimating equations (GEEs) were used to cal

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

123 Generalized estimating equations (GEEs) with a log link

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

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

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

127 ogies: fixed-effects logistic regression and generalized estimating equations (GEEs).

128 distensibility coefficient, and when we used generalized estimating equations instead of logistic reg

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

130 Generalized estimating equation linear regression models

131 Generalized estimating equation log-linear models were u

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

133 retention and adherence was evaluated using generalized estimating equation logistic models with rob

134 Generalized estimating equation logistic regression mode

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

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

137 robiota and persistent hrHPV infection using generalized estimating equation logistic regression mode

138 Generalized estimating equations logistic regression ana

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

140 Generalized estimating equations logistic regression was

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

142 sessed the trend of NWCO prevalence with the generalized estimating equation method.

143 Generalized estimating equation methods were used for lo

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

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

146 A generalized estimating equation model was used to perfor

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

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

149 The adjusted generalized estimating equations model that accounted fo

150 A generalized estimating equations model was used to analy

151 The generalized estimating equations model was used to analy

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

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

154 Using generalized estimating equation models adjusted for pote

155 Analyses were performed using multivariable generalized estimating equation models adjusting for pat

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

157 Generalized estimating equation models assessed lung fun

158 Generalized estimating equation models assessed the asso

159 Generalized estimating equation models assessed the asso

160 Using multivariate generalized estimating equation models the association o

161 d using multivariable linear regression with generalized estimating equation models to account for co

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

163 ed age-specific linear regression models and generalized estimating equation models to assess longitu

164 We used multivariable generalized estimating equation models to assess the rel

165 We used generalized estimating equation models to examine the lo

166 on, the coefficient of determination for the generalized estimating equation models was 0.25, with an

167 Generalized estimating equation models were applied to e

168 Generalized estimating equation models were clustered by

169 Eye-specific data and generalized estimating equation models were used to acco

170 Generalized estimating equation models were used to acco

171 Piecewise generalized estimating equation models were used to comp

172 Multivariate generalized estimating equation models were used to dete

173 Generalized estimating equation models were used to dete

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

175 Multivariate generalized estimating equation models were used to esti

176 Generalized estimating equation models were used to gene

177 Linear mixed models and generalized estimating equation models with a binomial d

178 Results of our generalized estimating equation models with robust stand

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

180 Generalized estimating equation models, which were adjus

181 nd T1-weighted MRI scans were analyzed using generalized estimating equation models, with consensus r

182 elation coefficients, linear regression, and generalized estimating equation models.

183 s were made using bivariate and multivariate generalized estimating equation models.

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

185 and cognitive impairment were assessed using generalized estimating equation models.

186 Generalized estimating equations models for repeated mea

187 Generalized estimating equations models were used to est

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

189 ng invasive mechanical ventilation (adjusted generalized estimating equation odds ratio, 0.36; 95% CI

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

191 We used a generalized estimating equation Poisson regression model

192 e injury and athlete-exposure data, and used generalized estimating equations Poisson regression mode

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

194 Generalized estimating equations, propensity-matched mod

195 e axial growth and developed a multivariable generalized estimating equation regression model to pred

196 ssion (aRR = 1.11, 95% CI:1.01-1.22) using a generalized estimating equation regression model.

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

198 We used Poisson generalized estimating equation regression models for lo

199 Generalized estimating equation regression models were u

200 We used linear generalized estimating equation regression models, adjus

201 asensitive PCR, and MDA was characterized by generalized estimating equation regression.

202 Generalized estimating equation regressions of polyp cha

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

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

205 Modeling using generalized estimating equations showed that methylation

206 Generalized estimating equations suggested that PERG amp

207 Generalized estimating equations tested the pre- and pos

208 We used generalized estimating equations that accounted for conf

209 Generalized estimating equations (that adjusted for diag

210 We used a generalized estimating equation to evaluate associations

211 Logistic regression with generalized estimating equations to account for clusteri

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

213 We conducted the analysis using generalized estimating equations to account for clusteri

214 umulus), using linear regression models with generalized estimating equations to account for correlat

215 , a marginal logistic model was fitted using generalized estimating equations to account for househol

216 ltivariable logistic regression models, with generalized estimating equations to account for physicia

217 We used generalized estimating equations to account for potentia

218 and GHTN with log-binomial regression using generalized estimating equations to account for repeat p

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

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

221 We used generalized estimating equations to calculate the preval

222 We used generalized estimating equations to compare outcomes bet

223 We used generalized estimating equations to compare the groups o

224 We used generalized estimating equations to determine associatio

225 We used generalized estimating equations to determine census tra

226 We used multivariable generalized estimating equations to determine the associ

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

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

229 We used log binomial models with generalized estimating equations to estimate association

230 used ordinal logistic regression and applied generalized estimating equations to estimate change in t

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

232 We used ordinal logistic regression with generalized estimating equations to estimate the associa

233 Multivariate logistic regression with generalized estimating equations to estimate the effect

234 We used generalized estimating equations to estimate the odds ra

235 We used generalized estimating equations to examine associations

236 We used generalized estimating equations to examine associations

237 We fitted linear regression models with generalized estimating equations to examine change in ln

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

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

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 identify the factors

243 We used generalized estimating equations to test associations of

244 Analyses were conducted using Generalized Estimating Equations, to account for cluster

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

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

247 A linear regression analysis with generalized estimating equations was employed to examine

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

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

250 Logistic regression with generalized estimating equations was used to evaluate as

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

252 multivariable logistic regression model with generalized estimating equations was used to explore ris

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 on individ

260 Generalized estimating equations were applied to ascerta

261 Linear regressions with generalized estimating equations were applied to estimat

262 Marginal models using generalized estimating equations were applied.

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

264 Generalized estimating equations were performed to ident

265 Descriptive statistics and generalized estimating equations were performed.

266 polygenic score (GPS) for BMI and BMI, while generalized estimating equations were used for obesity (

267 f polyps and confidence (three-point scale.) Generalized estimating equations were used for sensitivi

268 We used a family-centered approach, and generalized estimating equations were used to account fo

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

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

271 Generalized estimating equations were used to compare ad

272 Generalized estimating equations were used to compare me

273 Generalized estimating equations were used to compare th

274 Adjusted logistic regression analyses and generalized estimating equations were used to determine

275 Generalized estimating equations were used to estimate o

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

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

278 pared using t-tests or analyses of variance; generalized estimating equations were used to estimate t

279 Generalized estimating equations were used to evaluate a

280 Multivariable generalized estimating equations were used to evaluate t

281 Generalized estimating equations were used to evaluate t

282 Generalized estimating equations were used to examine th

283 Generalized estimating equations were used to examine th

284 Generalized estimating equations were used to examine tr

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

286 Generalized estimating equations were used to identify f

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

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

289 Generalized estimating equations were used to test the a

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

291 Logistic regression and generalized estimating equations were used.

292 arying levels of specialization by using the generalized estimating equation with robust variance est

293 Generalized estimating equations with a Poisson loglinea

294 Generalized estimating equations with adjustment for age

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

296 Linear mixed model regression analysis and generalized estimating equations with Bonferroni adjustm

297 Generalized estimating equations with Gaussian family, i

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

299 for each lesion was then determined by using generalized estimating equations, with observations nest

300 3 or more) were analyzed using multivariate generalized estimating equations, with results expressed