ライフサイエンスコーパス: generalized linear model

1 n 29% reduction in the coefficients from the generalized linear model).

2 an either ANOVA or a Negative Binomial (in a generalized linear model).

3 fic kinematic parameters of movement using a generalized linear model.

4 type (P< .01) were predictive of growth in a generalized linear model.

5 field (STRF), often in the framework of the generalized linear model.

6 hemical parameters using a repeated measures generalized linear model.

7 mating relative fitness ratios and fitting a generalized linear model.

8 time since activation of study centers using generalized linear model.

9 (EBlasso) and elastic net (EBEN) priors for generalized linear models.

10 atus and incident outcomes by using adjusted generalized linear models.

11 imensions over time were quantified by using generalized linear models.

12 Risk ratios were calculated with generalized linear models.

13 d using hierarchical logistic regression and generalized linear models.

14 dices and sensitization, were examined using generalized linear models.

15 es under a potential outcome framework using generalized linear models.

16 trains using a statistical approach based on generalized linear models.

17 s and reperfusion therapy was examined using generalized linear models.

18 tive season adult survival rates in binomial generalized linear models.

19 rons through a statistical approach based on generalized linear models.

20 rror components and density dependence using generalized linear models.

21 s and non-heat-wave days using city-specific generalized linear models.

22 and malaria incidence, was determined using generalized linear models.

23 (CIs) were evaluated using negative binomial generalized linear models.

24 between groups with mixed effects-ANOVA and generalized linear models.

25 en infants of smokers and non-smokers, using generalized linear models.

26 association with transcript abundance using generalized linear models.

27 outflow defect were modeled by using Poisson generalized linear models.

28 dontal disease progression was measured with generalized linear models.

29 Annual trends were modeled using generalized linear models.

30 proportional-hazards models and multivariate generalized linear models.

31 cific taxon abundances, by negative binomial generalized linear models.

32 ations were identified by using boosting for generalized linear models.

33 correlation coefficients of the hierarchical generalized linear models (0.113 for any inotrope) indic

34 In Poisson generalized linear models, 3-day moving average concentr

35 FFDM and DBT images were assessed by using a generalized linear model accounting for case and reader

36 mating equations (GEEs), an extension of the generalized linear model accounting for the within-subje

37 We used the generalized linear model adjusted by sex, age, and body

38 [corrected].In a generalized linear model adjusted for cardiovascular ris

39 Using 3-level hierarchical generalized linear modeling adjusted for patient sociode

40 We applied an overdispersed Poisson generalized linear model, adjusting for time, day of wee

41 resection (proximal, distal, or total) using generalized linear models, adjusting for age, stage of d

42 ce of hepatic steatosis (LPR </= 0.33) using generalized linear models, adjusting for demographics, i

43 non-PCI hospitals using 2-level hierarchical generalized linear models, adjusting for patient demogra

44 Applying a standard generalized linear model analysis approach, our results

45 enterococci were significant (p < 0.01), and generalized linear model analysis identified fines as th

46 A generalized linear model analysis was also performed to

47 We used a generalized linear model and haplotype score tests for t

48 quality control of RPPA experiments using a generalized linear model and logistic function.

49 Bivariate analysis by using generalized linear modeling and one-way analysis of vari

50 We develop hierarchical generalized linear models and computationally efficient

51 We used hierarchical generalized linear models and data on patients from the

52 Using generalized linear models and model selection techniques

53 Statistical analyses consisted of generalized linear models and multivariate regressions.

54 Least-squares means from generalized linear models and odds ratios (ORs) and 95%

55 ,752 angiosperm species and use phylogenetic generalized linear models and path analyses to test rela

56 g HUMAnN2 and MetaPhlAn2, and analyzed using generalized linear models and random effects meta-analys

57 dized uptake value ratios was assessed using generalized linear models and sex-stratified analysis.

58 Generalized linear models and Spearman's partial correla

59 ifies balanced spiking networks with Poisson generalized linear models and suggests several promising

60 Both single-marker (generalized linear model) and multi-marker (Bayesian app

61 roprotection was modeled with a log binomial generalized linear model, and data were pooled in a meta

62 recursive partitioning and regression tree, generalized linear model, and generalized additive model

63 i-continuous modeling framework based on the generalized linear model, and use it to characterize gen

64 method, Approximate Posterior Estimation for generalized linear model, apeglm, has lower bias than pr

65 Both a generalized linear model approach and the linear discrim

66 A Bayesian phylogeographic generalized linear model approach was used to reconstruc

67 Compared with the Bayesian hierarchical generalized linear model approach, the state-of-the-art

68 atively weighted least squares for classical generalized linear models as implemented in the package

69 We used generalized linear models, assuming a Poisson distributi

70 egative binomial distribution and assuming a generalized linear model (at the gene level) that consid

71 rk (MMLPNN), Ridge regression (RR), Boosting generalized linear model (BGLM), Negative binomial gener

72 glm, hapassoc, HapReg, Bayesian hierarchical generalized linear model (BhGLM) and logistic Bayesian L

73 Risk ratios (RRs) were calculated with generalized linear models by using a Poisson link functi

74 cteristic curve (Az) was calculated based on generalized linear models by using biopsy as the referen

75 The parameter estimates from our generalized linear model can be transformed to yield pop

76 A quasi-Poisson generalized linear model combined with a distributed lag

77 r very small sample sizes, the beta-binomial generalized linear model, combined with simple outlier d

78 A logistic generalized linear model confirmed the significance of t

79 Then, the SNPs were analyzed with a generalized linear model controlling for genotyping plat

80 atient-reported outcomes were analyzed using generalized linear models, controlling for confounding v

81 tests, logistic regression (predictive), and generalized linear models (cost).

82 A generalized linear model determined when the postoperati

83 We find that BTH and a double generalized linear model (dglm) outperform classical tes

84 We adapt the double generalized linear model (dglm) to simultaneously fit th

85 ss computing time than Bayesian hierarchical generalized linear model, efficient mixed model associat

86 Generalized linear models estimated correlations with po

87 Multivariate repeated-measures generalized linear models estimated mean number of teeth

88 Repeated-measures generalized linear models estimated the mean cumulative

89 screte phylogeographic approach coupled to a generalized linear model extension to characterize the d

90 We compared a range of negative binomial generalized linear models fitted to the meningitis data.

91 Generalized linear model fitting showed decreasing perce

92 Using a generalized linear model for a spiking recurrent neural

93 Result: we proposed a network module-based generalized linear model for differential expression ana

94 LINSIGHT combines a generalized linear model for functional genomic data wit

95 tive protein levels were examined by using a generalized linear model for gamma distribution.

96 A generalized linear model for log-transformed 3MSE scores

97 We propose a two-part, generalized linear model for such bimodal data that para

98 using mixed-models analysis dof variance and generalized linear models for multiple repeated measurem

99 ch interval (PDC <80%) were identified using generalized linear models for repeated measures.

100 paradigm along with a novel extension of the generalized linear model framework (GLM), termed the spa

101 We further integrate the model into the generalized linear model framework in order to perform d

102 A generalized linear model framework was used to predict t

103 Statistical models were implemented using a generalized linear model framework, including the experi

104 that the algorithm fits into the statistical generalized linear models framework, describe a practica

105 Generalized linear models gave very unrealistic projecti

106 A generalized linear model generated log relative risks fo

107 si-biophysical interpretation of the Poisson generalized linear model (GLM) as a special case of the

108 Moreover, a generalized linear model (GLM) constructed on responses

109 abilistic clustering methods of Tzeng to the generalized linear model (GLM) framework established by

110 ar mixed model (GLMM) is an extension of the generalized linear model (GLM) in which the linear predi

111 In contrast, a generalized linear model (GLM) is very interpretable esp

112 A Generalized Linear Model (GLM) model analysis was perfor

113 ion rate matrix between discrete states as a generalized linear model (GLM) of genetic, geographic, d

114 A Generalized Linear Model (GLM) revealed a significant di

115 A generalized linear model (GLM) revealed that sound and m

116 We extend a generalized linear model (GLM) that predicts postsynapti

117 onstructing neuronal circuitry by applying a generalized linear model (GLM) to spike cross-correlatio

118 The proposed generalized linear model (GLM) used geographic and demog

119 A generalized linear model (GLM) was used to test the asso

120 Using the generalized linear model (GLM) with adjustment for poten

121 ostics was confirmed by statistical methods: generalized linear model (GLM), linear discriminant anal

122 logit for probability of nonzero costs and a generalized linear model (GLM).

123 ne (SVM), boosted regression tree (BRT), and generalized linear model (GLM).

124 new minorize-maximization (MM) algorithm for generalized linear models (GLM) combined with heuristic

125 We apply generalized linear models (GLM) to estimate presence pro

126 rtality in Beijing, China (2009-2012), using generalized linear models (GLM).

127 Generalized-linear models (GLM) and multi-level Cox-regr

128 ious Bayesian hierarchical models, including generalized linear models (GLMs) and Cox survival models

129 Regularized generalized linear models (GLMs) are popular regression

130 Generalized linear models (GLMs) are used in high-dimens

131 of the negative binomial to provide flexible generalized linear models (GLMs) on both the mean and di

132 We also used generalized linear models (GLMs) to examine female repro

133 ecouples kinematics from mechanics, and used Generalized Linear Models (GLMs) to show that Vg neurons

134 Generalized linear models (GLMs) tolerate without bias o

135 developed previously for this purpose, using generalized linear models (GLMs).

136 In a multivariate generalized linear model, HCV RNA concentrations decreas

137 In a generalized linear model, higher TTV levels were associa

138 A generalized linear model identified combinations of cyto

139 A generalized linear model identified the following factor

140 A generalized linear model identified the presence of Stow

141 In adjusted generalized linear models, in addition to MELD (P < .001

142 In a generalized linear model including the covariates testos

143 glomerular filtration rate was estimated by generalized linear models, including tests of interactio

144 f accuracy) with those of a forward selected generalized linear model (interpretability).

145 This behavior was captured by a generalized linear model involving not only the visual r

146 ase status, the logistic-regression model or generalized linear model is typically employed.

147 We used a binomial generalized linear model (log-binomial model) to examine

148 The generalized linear model methodology implemented via the

149 inuous, or time-to-event end points in which generalized linear models, models for longitudinal data

150 lized linear model (BGLM), Negative binomial generalized linear model (NBGLM), Boosting generalized a

151 TI using generalized estimating equation and generalized linear models (non-ART group pVL and hemoglo

152 riori estimates of song spectrograms using a generalized linear model of neuronal responses and a ser

153 A generalized linear model of repeated measures with gener

154 he method, named NanoStringDiff, considers a generalized linear model of the negative binomial family

155 Using generalized linear modeling of UMRV infection overlaid o

156 hese models are mathematically equivalent to generalized linear models of binomial responses that inc

157 Generalized linear models of the association between FEV

158 Generalized linear models on brush samples demonstrated

159 In multivariable hierarchical generalized linear models, only differences in LOS by su

160 ood glucose decreased from 142 to 115 mg/dL (generalized linear model p < .001).

161 ood glucose decreased from 134 to 116 mg/dL (generalized linear model p = .001).

162 ibutions were similar in the four hospitals (generalized linear model p = .18).

163 Three-level hierarchical generalized linear models (patients clustered within sur

164 Point process generalized linear models (PP-GLMs) provide an important

165 simulated dataset to illustrate how weighted generalized linear model regression can be used to estim

166 for the presence of AMD on the basis of the generalized linear model regression framework.

167 lates the importance of each predictor using generalized linear model regression of distances between

168 However, generalized linear model regression suggests that four o

169 Multivariable generalized linear model regressions with propensity sco

170 A generalized linear model revealed enhanced conjunctive c

171 A generalized linear model revealed that the nature (inhib

172 This random generalized linear model (RGLM) predictor provides varia

173 Generalized linear model showed that enamel lesions were

174 Generalized linear models showed that only one of the tw

175 ysis of the monitoring cohort data set using generalized linear models showed the following: (1) an o

176 ed using item response models and subsequent generalized linear models, showing that the most importa

177 encing depth is utilized as a covariate in a generalized linear model, successfully remove the influe

178 Multivariable generalized linear modeling suggests an independent asso

179 We calculated risk ratios (RRs) using generalized linear models, taking into account sampling

180 Here, we describe the development of a generalized linear model (termed a pathotyping model) to

181 stical method, a well-validated hierarchical generalized linear model that included both patient-leve

182 model performance was with the hierarchical generalized linear models that adjusted for patient case

183 pitalizations by using the negative binomial generalized linear model, the rate ratio (eplerenone ver

184 In multivariable linear regression and generalized linear models, there was an independent, inv

185 plan-Meier curves and a proportional hazards generalized linear model to assess whether the time to s

186 The algorithm uses a generalized linear model to deconvolute different effect

187 In addition, we used a Poisson generalized linear model to estimate excess perforations

188 e probability-weighted two-part, probit, and generalized linear model to estimate incremental per pat

189 Missing payment data were imputed using a generalized linear model to estimate overall PrEP medica

190 We used a generalized linear model to examine the relation between

191 DeconvSeq utilizes a generalized linear model to model effects of tissue type

192 We used a generalized linear model to predict single neuron respon

193 site by iteratively fitting a feature-based generalized linear model to SELEX probe counts.

194 with Dunn's test for multiple comparison and generalized linear models to adjust for confounding fact

195 a and arm symptoms and multivariate-adjusted generalized linear models to compare HRQOL (physical fun

196 at least one year after randomization using generalized linear models to compute risk ratios and 95

197 rformed by using univariate and multivariate generalized linear models to determine significant risk

198 We used generalized linear models to determine the relationship

199 We used generalized linear models to estimate adjusted mean tria

200 ed distributed lag models and over-dispersed generalized linear models to estimate the cumulative eff

201 RiboDiff uses generalized linear models to estimate the over-dispersio

202 We used multivariate generalized linear models to evaluate both access to tra

203 We developed hierarchical generalized linear models to examine associations betwee

204 We constructed generalized linear models to examine the determinants of

205 ting Sobol's sensitivity indices (SSI) under generalized linear models to existing liver RNA expressi

206 We used multivariate generalized linear models to identify factors associated

207 the Child Behavior Checklist (CBCL) and used generalized linear models to test the association betwee

208 We used generalized linear models to test the null hypothesis th

209 develop a method based on the framework of a generalized linear model using four-way cross population

210 en treatments were made using a mixed effect generalized linear model using least squares estimation.

211 diabetes-care characteristics by means of a generalized linear model using the complementary log-log

212 A generalized linear model was applied to test this relati

213 ariate statistical tests, and a hierarchical generalized linear model was created to test for indepen

214 A hierarchical generalized linear model was used to assess risk factors

215 A generalized linear model was used to control for confoun

216 A generalized linear model was used to estimate adjusted m

217 A generalized linear model was used to estimate incrementa

218 A generalized linear model was used to estimate the effect

219 A mixed effects multivariable generalized linear model was used to estimate the mean r

220 A generalized linear model was used to model the mean func

221 A mixed-effects generalized linear model was used to test for difference

222 or within-subject correlations of knee data, generalized linear modeling was used in the correlation

223 A hidden Markov model combined with generalized linear models was able to decode social comp

224 Binomial regression (generalized linear model) was used to examine the risk r

225 Using a generalized linear model, we explain how peripheral enco

226 Using a generalized linear model, we explored the effects of tim

227 Using a generalized linear model, we identified subtle variation

228 Using binaurally uncorrelated noise and a generalized linear model, we were able to estimate the s

229 Using Poisson generalized linear models, we assessed short-term associ

230 Using multivariable generalized linear models, we estimated adjusted risk di

231 Transplant Recipients data and multivariable generalized linear models, we examined factors associate

232 Using generalized linear models, we identified significant ass

233 Propensity score matching and generalized linear modeling were used.

234 Generalized linear models were adjusted for age, age squ

235 Generalized linear models were applied to assess de novo

236 Sex- and menopause-specific multiple generalized linear models were applied.

237 Generalized linear models were developed to identify pre

238 Bivariate analyses and hierarchical generalized linear models were employed to measure assoc

239 Multivariable generalized linear models were performed to analyze the

240 tics as well as unadjusted and risk-adjusted generalized linear models were performed to assess adver

241 multivariable, and propensity score-adjusted generalized linear models were performed.

242 ods were used to reconstruct HIV spread, and generalized linear models were tested for viral factors

243 Univariate and multivariate generalized linear models were used to analyze the relat

244 Generalized linear models were used to assess difference

245 Generalized linear models were used to assess the associ

246 Multiple generalized linear models were used to assess the influe

247 Generalized linear models were used to assess the per ge

248 Generalized linear models were used to assess the relati

249 s (Bangladesh 398, Malawi: 900, Nepal: 615), generalized linear models were used to assess the streng

250 Adjusted Generalized Linear Models were used to compare matched p

251 Linear and generalized linear models were used to determine whether

252 Multivariate, generalized linear models were used to estimate the asso

253 Generalized linear models were used to evaluate direct a

254 Linear mixed models and generalized linear models were used to evaluate the asso

255 Multilevel generalized linear models were used to evaluate trends i

256 Generalized linear models were used to examine if PTSD,

257 Generalized linear models were used to examine whether t

258 Generalized linear models were used to explore the assoc

259 Generalized linear models were used to identify hospital

260 Generalized linear models were used to predict the netwo

261 Generalized linear models were used to predict the spiki

262 ere used to evaluate detection accuracy, and generalized linear models were used to test ADC differen

263 Generalized linear models were used to test the associat

264 Logistic regression and generalized linear models were used to test the associat

265 Generalized linear models were used, adjusting for age a

266 ations, 4-parameter sinusoid regression, and generalized linear models were used.

267 s two approaches: (i) a simple beta-binomial generalized linear model, which has not been extensively

268 Generalized linear models, which included a mixed-random

269 A mixed effect generalized linear model with a logit link function was

270 A generalized linear model with a Poisson distribution and

271 logistic regression, Poisson regression, and generalized linear model with gamma distribution and log

272 onometric model (probit regression model and generalized linear model with gamma distribution) was us

273 The generalized linear model with intraclass correlation was

274 A generalized linear model with least absolute shrinkage s

275 Costs were modeled using a generalized linear model with log-link and gamma-distrib

276 lation in scRNA-seq counts, we recommend the generalized linear model with negative binomial count di

277 A generalized linear model with negative binomial regressi

278 A generalized linear model with robust estimation was used

279 We applied a generalized linear model with single nucleotide polymorp

280 We used a generalized linear model with splines to simultaneously

281 We used binary generalized linear modeling with a log link to estimate

282 We then performed logistic regression and generalized linear modeling with gamma distribution (log

283 Generalized linear models with a gamma distribution and

284 morbidity on surgery was determined by using generalized linear models with a logit link accounting f

285 ntially abundant features are detected using generalized linear models with a negative binomial distr

286 The discussion focuses on generalized linear models with an additional illustratio

287 dels in "intention-to-treat" analyses and in generalized linear models with binary outcomes and inver

288 Two generalized linear models with elastic net regularizatio

289 Relative risks (RRs) were estimated using generalized linear models with fine stratification on th

290 the incidence were based on marginal, exact generalized linear models with generalized estimating eq

291 Generalized linear models with log link, Poisson distrib

292 Using generalized linear models with propensity scores, cost d

293 tervention and compared between groups using generalized linear models with robust SEs.

294 We mined data from Instagram, and used generalized linear models with site- and country-level d

295 Generalized linear models with variable selection posses

296 For each of these time series, Poisson generalized linear models with varying lag structures we

297 We compared these intervals using a generalized linear model (with compound symmetry correla

298 er of bumps and UFOV score was assessed in a generalized linear model, with adjustment for vision and

299 proximal tubular function were evaluated by generalized linear models, with adjustment for renal- an

300 st associated with HAIs were estimated using generalized linear models, with adjustments for patient