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 mating relative fitness ratios and fitting a generalized linear model.

3 hemical parameters using a repeated measures generalized linear model.

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

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

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

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

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

9 rror components and density dependence using generalized linear models.

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

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

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

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

14 association with transcript abundance using generalized linear models.

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

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

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

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

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

20 Risk ratios were calculated with generalized linear models.

21 d using hierarchical logistic regression and generalized linear models.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

39 A generalized linear model analysis was also performed to

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

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

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

43 We develop hierarchical generalized linear models and computationally efficient

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

45 Using generalized linear models and model selection techniques

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

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

48 ,752 angiosperm species and use phylogenetic generalized linear models and path analyses to test rela

49 Generalized linear models and Spearman's partial correla

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

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

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

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

54 Both a generalized linear model approach and the linear discrim

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

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

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

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

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

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

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

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

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

64 A logistic generalized linear model confirmed the significance of t

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

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

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

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

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

70 Repeated-measures generalized linear models estimated the mean cumulative

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

72 Using a generalized linear model for a spiking recurrent neural

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

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

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

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

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

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

79 A generalized linear model framework was used to predict t

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

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

82 Generalized linear models gave very unrealistic projecti

83 A generalized linear model generated log relative risks fo

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

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

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

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

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

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

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

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

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

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

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

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

96 Generalized linear models (GLMs) tolerate without bias o

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

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

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

100 A generalized linear model identified combinations of cyto

101 A generalized linear model identified the presence of Stow

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

103 In a generalized linear model including the covariates testos

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

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

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

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

108 The generalized linear model methodology implemented via the

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

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

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

112 Using generalized linear modeling of UMRV infection overlaid o

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

114 Generalized linear models of the association between FEV

115 Generalized linear models on brush samples demonstrated

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

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

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

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

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

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

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

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

124 However, generalized linear model regression suggests that four o

125 Multivariable generalized linear model regressions with propensity sco

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

144 We used generalized linear models to determine the relationship

145 We used generalized linear models to estimate adjusted mean tria

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

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

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

149 We developed hierarchical generalized linear models to examine associations betwee

150 We constructed generalized linear models to examine the determinants of

151 We used multivariate generalized linear models to identify factors associated

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

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

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

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

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

157 A generalized linear model was applied to test this relati

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

159 A generalized linear model was used to estimate the effect

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

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

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

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

164 Using a generalized linear model, we identified subtle variation

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

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

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

168 Propensity score matching and generalized linear modeling were used.

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

170 Generalized linear models were developed to identify pre

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

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

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

174 Generalized linear models were used to assess difference

175 Generalized linear models were used to assess the associ

176 Multiple generalized linear models were used to assess the influe

177 Generalized linear models were used to assess the per ge

178 Generalized linear models were used to assess the relati

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

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

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

182 Multilevel generalized linear models were used to evaluate trends i

183 Generalized linear models were used to examine if PTSD,

184 Generalized linear models were used to explore the assoc

185 Generalized linear models were used to identify hospital

186 Generalized linear models were used to predict the spiki

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

188 Generalized linear models were used to test the associat

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

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

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

192 Generalized linear models, which included a mixed-random

193 A generalized linear model with a Poisson distribution and

194 The generalized linear model with intraclass correlation was

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

196 We applied a generalized linear model with single nucleotide polymorp

197 We used a generalized linear model with splines to simultaneously

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

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

200 Generalized linear models with a gamma distribution and

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

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

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

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

205 Two generalized linear models with elastic net regularizatio

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

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

208 Generalized linear models with log link, Poisson distrib

209 Using generalized linear models with propensity scores, cost d

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

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

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

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

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