ライフサイエンスコーパス: linear model

1 ion in the coefficients from the generalized linear model).

2 ric cancer (P = .018, in comparison with the linear model).

3 measured as E/A peak flow (P < 0.050 for all linear models).

4 er of cardiac remodelling (P < 0.050 for all linear models).

5 l field worsening (P = .006 by multivariable linear modeling).

6 c parameters of movement using a generalized linear model.

7 ctivation of study centers using generalized linear model.

8 ed a better fit to the data than the simpler linear model.

9 ) were predictive of growth in a generalized linear model.

10 UA or revascularization using a hierarchical linear model.

11 , that provide theoretical advances over the linear model.

12 iastinal signal was measured and fitted to a linear model.

13 atial statistics with a multivariate general linear model.

14 ), often in the framework of the generalized linear model.

15 ng support vector regression but not general linear model.

16 nd the phenotypes as predictors in a general linear model.

17 ive fitness ratios and fitting a generalized linear model.

18 predict their dynamics in terms of a forced linear model.

19 d using a mixed-effects, multilevel, general linear model.

20 nd elastic net (EBEN) priors for generalized linear models.

21 Treatment effects were evaluated with mixed linear models.

22 magnitude larger than predicted by canonical linear models.

23 ident outcomes by using adjusted generalized linear models.

24 er time were quantified by using generalized linear models.

25 ne expression differences were assessed with linear models.

26 Risk ratios were calculated with generalized linear models.

27 re analyzed with the use of adjusted general linear models.

28 archical logistic regression and generalized linear models.

29 nsitization, were examined using generalized linear models.

30 elationship were evaluated with hierarchical linear models.

31 otential outcome framework using generalized linear models.

32 ino acid substitutions in the linear and non-linear models.

33 nal connectivity were analyzed using general linear models.

34 h percentile were estimated using multilevel linear models.

35 glucose metabolism and amyloid plaques using linear models.

36 luated using two separate multivariate mixed linear models.

37 phylogenetic structure using distance-based linear models.

38 M), multiple linear regressions, and general linear models.

39 iated with sVCAM-1 were examined using mixed linear models.

40 asma FA were assessed using adjusted general linear models.

41 of GBCA, age, and sex by using multivariable linear models.

42 nical parameters were analyzed using general linear models.

43 identified by using boosting for generalized linear models.

44 s were examined using empiric Bayes-mediated linear models.

45 plaque presence were evaluated using general linear models.

46 adjusted for study, age, and BMI using mixed linear modeling.

47 images were assessed by using a generalized linear model accounting for case and reader variability.

48 ions (GEEs), an extension of the generalized linear model accounting for the within-subject correlati

49 pared between groups by using a hierarchical linear model, accounting for the repeated measurement de

50 [corrected].In a generalized linear model adjusted for cardiovascular risk factors, a

51 risks (RRs) were calculated by using general linear models adjusted for potential confounders.

52 hree visits was examined using mixed effects linear models, adjusted for race and site.

53 c steatosis (LPR </= 0.33) using generalized linear models, adjusting for demographics, individual an

54 itals using 2-level hierarchical generalized linear models, adjusting for patient demographics and cl

55 The biases may be accounted for by a linear model, although the spatial variation cannot be e

56 CT examination were assessed by using mixed linear model analyses.

57 Applying a standard generalized linear model analysis approach, our results indicate tha

58 A linear model analysis confirmed these findings while adj

59 Our procedure is based on the weighted linear model analysis facilitated by the voom method whi

60 notype data in human populations using mixed-linear model analysis.

61 pLARmEB, multilocus random-SNP-effect mixed linear model and fast multilocus random-SNP-effect EMMA

62 trol of RPPA experiments using a generalized linear model and logistic function.

63 ory, and executive function) using a general linear model and longitudinally using mixed-effects regr

64 between experimental groups by using a mixed linear model and t tests.

65 As CPM focuses on linear modeling and a purely data-driven approach, neuro

66 We use marginal linear models and lag-lead analysis to measure ecologica

67 Using generalized linear models and model selection techniques, we used 12

68 erm species and use phylogenetic generalized linear models and path analyses to test relationships be

69 Both single-marker (generalized linear model) and multi-marker (Bayesian approach) analy

70 erm children using both univariable (general linear model) and multivariable models (support vector r

71 e individual level with use of a generalised linear model, and microsimulation of unobservable diseas

72 modeling framework based on the generalized linear model, and use it to characterize genes with cons

73 Data were analysed using hierarchical linear modelling, and moderation effects of personality

74 characteristics were assessed using a mixed linear model approach and subsequent post hoc t tests.

75 A Bayesian phylogeographic generalized linear model approach was used to reconstruct the spatio

76 here the structure and parameters of the non-linear model are optimized using an evolutionary algorit

77 udies, e.g., in ecology, where flexible, non-linear models are fitted to high-dimensional data.

78 Mixed linear models assessed the ability of standardized basel

79 n these cells in response to depolarization (linear models at false discovery rate </=0.05).

80 mial distribution and assuming a generalized linear model (at the gene level) that considers the depe

81 A linear model based on this approach was able to account

82 We developed linear models based on age, climate and previous growth

83 Comparison of teeth and implants via general linear models based on orthogonal polynomials showed sim

84 eversal task, assessed with standard general linear-model-based analysis.

85 er systolic BP in Hispanics/Latinos (general linear model; beta, .23; 95% CI, .04-.43) and Asians (be

86 ant perinatal clinical factors using general linear model but not support vector regression.

87 atios (RRs) were calculated with generalized linear models by using a Poisson link function with robu

88 rve (Az) was calculated based on generalized linear models by using biopsy as the reference standard

89 tive model for oxi-mC-seq data and a general linear model component to account for confounding effect

90 In general linear models controlling for age, gender, education and

91 ssociations were estimated using Poisson log-linear models controlling for continuous air temperature

92 tic regression (predictive), and generalized linear models (cost).

93 A multivariate general linear model described the influence of sex, body height

94 taxa and functions, based on distance-based linear models (DistLM).

95 In adjusted linear models, each doubling of tAs was associated with

96 time than Bayesian hierarchical generalized linear model, efficient mixed model association (EMMA) a

97 A linear model estimated at the age-group level implies th

98 Linear models estimated from observed fluctuations, toge

99 General linear models evaluated the effect of role, specialty, a

100 General linear models examined intervention effect on conditiona

101 A first-order linear model fit best at the riparian site, indicating c

102 Linear models fit with generalized estimating equations

103 In a phantom study, we estimated a linear model fitting the CZT camera data to the planar d

104 Using a generalized linear model for a spiking recurrent neural network, we

105 proposed a network module-based generalized linear model for differential expression analysis of the

106 LINSIGHT combines a generalized linear model for functional genomic data with a probabil

107 We developed a new multivariable linear model for GFR using statistical regression analys

108 We propose a two-part, generalized linear model for such bimodal data that parameterizes bo

109 Based on our experiences we have built a linear model for the length of time that contributors ar

110 ignificant differences were identified using linear models for microarray assays.

111 deled change through time using hierarchical linear models for total nitrogen (TN), total phosphorus

112 her integrate the model into the generalized linear model framework in order to perform differential

113 models were implemented using a generalized linear model framework, including the experimental condi

114 hrough the hypothesis testing procedure in a linear model framework.

115 Generalized linear models gave very unrealistic projections far away

116 ted from ROIs determined through our General Linear Model (GLM) analysis and prior publications were

117 e with previous studies, a classical general linear model (GLM) analysis based on cued attention cond

118 Moreover, a generalized linear model (GLM) constructed on responses in the dorsa

119 rix between discrete states as a generalized linear model (GLM) of genetic, geographic, demographic,

120 We extend a generalized linear model (GLM) that predicts postsynaptic spiking as

121 General linear models (GLMs) incorporating land cover, precipita

122 ive binomial to provide flexible generalized linear models (GLMs) on both the mean and dispersion.

123 ematics from mechanics, and used Generalized Linear Models (GLMs) to show that Vg neurons more direct

124 eviously for this purpose, using generalized linear models (GLMs).

125 In a generalized linear model, higher TTV levels were associated with a d

126 Hierarchical linear modeling (HLM) was applied where level-1 data con

127 A generalized linear model identified combinations of cytokines, allow

128 A generalized linear model identified the presence of Stowaway element

129 Hierarchical linear modeling identified factors associated with the s

130 Inferential (linear) models identified a consistent negative associat

131 ARA progression data were best fitted with a linear model in all genotypes.

132 Hierarchical model within a frequency domain linear model in order to enforce sparsity and incorporat

133 theory to decompose chaotic dynamics into a linear model in the leading delay coordinates with forci

134 In adjusted generalized linear models, in addition to MELD (P < .001), factors i

135 Using a saturated mixed linear model including epistasis and environmental inter

136 We fitted mixed linear models including age, gender, and 45 myopia-assoc

137 a variety of stimuli more accurately than a linear model, including stimuli targeted to cones within

138 th groups were compared using a multivariate linear model, including variables that were significantl

139 Robust linear models indicated that DE plus saline and DE plus

140 Linear models indicated that dolphin abundance was signi

141 A consequence of linear models is that faster translation of a given mRNA

142 qual to 45% using a log binomial generalised linear model it was found that participants with a cathe

143 Linear models (LMs) were applied to identify factors ass

144 regression trees, BRT) and more traditional linear models (logistic regression, LR).

145 We introduce a liability-threshold mixed linear model (LTMLM) association statistic for case-cont

146 r can be approximated sufficiently well by a linear model, methods exist to identify the number and c

147 ds are all based on a fixed-SNP-effect mixed linear model (MLM) and single marker analysis, such as e

148 In encoding analysis, the linear model needs to be appropriately regularized, whic

149 First, in contrast to a linear model of cancer progression, metastases can origi

150 PCA and IBS were used in a mixed linear model of capsaicin and dihydrocapsaicin content a

151 primary and metastatic melanomas supports a linear model of clonal evolution in cancer.

152 Different from the general linear model of fMRI that predicts responses directly fr

153 the laser spectrum, generalizing the seminal linear model of Schawlow and Townes.

154 amed NanoStringDiff, considers a generalized linear model of the negative binomial family to characte

155 A conceptual non-linear model of virus adaptation that incorporates the t

156 Here we use non-linear modeling of neuronal activity and bifurcation the

157 d assessed differences between conditions by linear modeling of the data.

158 Using generalized linear modeling of UMRV infection overlaid on biotic and

159 are mathematically equivalent to generalized linear models of binomial responses that include a compl

160 d not be accurately predicted by traditional linear models of vestibular processing.

161 Generalized linear models on brush samples demonstrated oral cortico

162 her pig domestication followed a traditional linear model or a more complex, reticulate model.

163 Compared to estimates from the IPCC Tier 1 linear model, our updated N2 O emissions range from 20%

164 and the initial language impairment (general linear model overall significant at P < 0.0001; ExpB 1.0

165 oup and controls were assessed using general linear model (P < 0.05 corrected for multiple comparison

166 Status Scale scores in surface-based general linear modelling (P < 0.05).

167 Three-level hierarchical generalized linear models (patients clustered within surgeons within

168 Regularized linear models performed nearly as well as random forest-

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

170 llary light reflex) contributed heavily to a linear model predicting behavioral state, whereas brain

171 GWAS was accomplished with a mixed linear model procedure implementing the additive and dom

172 Mixed linear models provide important techniques for performin

173 rea and relating that to carbon lost using a linear model (r(2) = 0.41), we found 1.1% outlying PAs (

174 exponential model (R2 = 0.40; P < .001) and linear model (R2 = 0.25; P = .002).

175 Multivariable generalized linear model regressions with propensity score adjustmen

176 Together with linear modeling results, these findings suggest that mos

177 Hierarchical linear modeling revealed that improved treatment respons

178 Hierarchical linear modeling revealed that response to psychotherapy

179 Linear modelling reveals that sequence features at both

180 Principal components analysis and linear model selection showed evidence of ENSO-driven sy

181 Multivariate analysis using a general linear model showed plasma PK to be significantly associ

182 Intention-to-treat analysis using mixed linear models showed that PBT was noninferior to FBT on

183 Linear models showed the relevance of the addition of pe

184 m response models and subsequent generalized linear models, showing that the most important determina

185 For idealized linear models, structure is simplest across the neuron m

186 rray of pairwise t tests toward more general linear modeling structures, such as those provided by th

187 Kallisto TPM data gives the best fit to the linear model studied.

188 lculated risk ratios (RRs) using generalized linear models, taking into account sampling weights.

189 we describe the development of a generalized linear model (termed a pathotyping model) to predict the

190 For validation, we performed mixed linear model testing for the association between CFI rat

191 With a linear model that combines chromatin annotations and seq

192 tween each CpG site and PTSD diagnosis using linear models that adjusted for cell proportions and age

193 Hierarchical linear models that included a priori covariates and only

194 tinct tropical seasons and determined simple linear models that relate transcriptomic variation to cl

195 In a multivariate linear model, the main contributor to systolic, diastoli

196 We used a mixed-effect linear model to analyse the association of nutritional s

197 The algorithm uses a generalized linear model to deconvolute different effects, and emplo

198 y-weighted two-part, probit, and generalized linear model to estimate incremental per patient per mon

199 We used a mixed linear model to impute DNA methylation (DNAm) levels of

200 or children and AYAs, and used a generalised linear model to model survival time trends (1999-2007) a

201 ratively fitting a feature-based generalized linear model to SELEX probe counts.

202 strapping in conjunction with response error linear modeling to decouple biological variance from inf

203 We used generalised linear modelling to assess FeNO as a predictor of respon

204 ses were done using hierarchical generalised linear models to adjust for identified confounders and a

205 We used linear models to correlate the number of permitted swine

206 sing univariate and multivariate generalized linear models to determine significant risk factors for

207 ed lag models and over-dispersed generalized linear models to estimate the cumulative effects of ozon

208 RiboDiff uses generalized linear models to estimate the over-dispersion of RNA-Seq

209 We developed hierarchical generalized linear models to examine associations between admission

210 We constructed generalized linear models to examine the determinants of attributabl

211 We used multivariate generalized linear models to identify factors associated with each p

212 We then used generalised linear models to investigate the associations between ho

213 We use linear models to quantify this pattern.

214 We used generalized linear models to test the null hypothesis that condition

215 Multilevel mixed linear models (to account for the inclusion of 2 eyes of

216 cation with time-varying distributed lag non-linear models, using a bivariate spline to model the exp

217 ain Monte Carlo algorithm for Gaussian mixed linear model via Gibbs sampling.

218 t the validity of the model, the correlative linear model was applied to determine the enantiomeric e

219 A distributed lag non-linear model was developed to estimate the cumulative ef

220 In this study, a robust linear model was developed to predict As RBA in mice usi

221 A generalized functional linear model was implemented in order to regress the che

222 The linear model was optimal for 12 regions, whereas the spl

223 A general linear model was used to assess serial FSH by OFR.

224 First, the general linear model was used to fit regional time-activity curv

225 General linear modeling was used to 1) estimate the association

226 Using a mixed linear model we show that 4.1% of the variation in the m

227 Thanks to a penalized linear model, we also show that the number of features u

228 Using a generalized linear model, we explain how peripheral encoding of olfa

229 Using hierarchical linear modeling, we analyzed data from the 2004 Health a

230 Using Poisson generalized linear models, we assessed short-term associations betwe

231 Using general linear models, we computed site-specific and pooled esti

232 Using Generalised Linear Models, we found that primary neuron responses we

233 General linear model were used to assess outcome.

234 Propensity score matching and generalized linear modeling were used.

235 Linear models were adjusted for age, sex, years of educa

236 Multivariable mixed-effects log-linear models were constructed to determine the associat

237 Generalized linear models were developed to identify predictors, and

238 Linear mixed effects and Bayesian piece-wise linear models were employed to test hypothesized relatio

239 Multilevel mixed linear models were performed for analyses.

240 Mixed-effects linear models were tested with visual field mean deviati

241 Multivariate general linear models were used to adjust for gestational age; f

242 Hierarchical linear models were used to analyse health outcomes and h

243 Mixed-effects linear models were used to analyze the data.

244 Generalized linear models were used to assess differences among comp

245 General linear models were used to assess longitudinal associati

246 Generalized linear models were used to assess the association betwee

247 Multiple generalized linear models were used to assess the influence of compl

248 Repeated-measures analyses using mixed linear models were used to estimate and compare study en

249 Log-linear models were used to estimate prevalence ratios (P

250 Multivariate, generalized linear models were used to estimate the associations bet

251 Linear mixed models and generalized linear models were used to evaluate the associations bet

252 Multilevel generalized linear models were used to evaluate trends in the risk-a

253 Generalized linear models were used to examine if PTSD, other psychi

254 Complex samples general linear models were used to explore cross-sectional assoc

255 General linear models were used to explore diagnostic difference

256 Generalized linear models were used to identify hospital structural

257 Generalized estimating equation log-linear models were used to integrate time into the model

258 Linear models were used to partition the variation in pe

259 Generalized linear models were used to predict the spiking activity

260 In contrast to linear models where translation is largely limited by in

261 eptide charge are well described by a simple linear model, which should help improve current coiled-c

262 EA: performed better than the linear and non-linear models whose parameters are estimated using the l

263 lity associations with a distributed lag non-linear model with 21 days of lag, and then pooled them i

264 iffered between SZ and HCs, we implemented a linear model with DeltaBPND as dependent variable, time

265 cific means were compared by using a general linear model with false discovery rate control for multi

266 Although the mechanism is unknown, a non-linear model with perceptual feedback accurately simulat

267 species data were then combined to develop a linear model with pooled slopes for each independent par

268 P vs sham blocks) was assessed using general linear model with repeated measures.

269 We developed a linear model with six parameters that can predict 38% of

270 We used a generalized linear model with splines to simultaneously capture 2 ty

271 for final model development, resulting in a linear model with the equation RBA = 0.65 x IVBA + 7.8 a

272 ipping force, and that a first order dynamic linear model with these STN LFP features as inputs can b

273 erformed logistic regression and generalized linear modeling with gamma distribution (log link), resp

274 Generalised linear modelling with random effects to account for clus

275 two-level, population-weighted hierarchical linear models with 20 multiply imputed datasets.

276 Generalized linear models with a gamma distribution and a log link w

277 dant features are detected using generalized linear models with a negative binomial distribution.

278 Two generalized linear models with elastic net regularization (14VF and

279 risks (RRs) were estimated using generalized linear models with fine stratification on the propensity

280 We used log-linear models with generalized estimating equations to e

281 Linear models with generalized estimating equations were

282 Linear models with generalized estimating equations were

283 evaluated by using the Fisher exact test and linear models with generalized estimating equations.

284 mated in the IoW cohort (n = 1456) using log-linear models with generalized estimating equations.

285 ce were based on marginal, exact generalized linear models with generalized estimating equations.

286 Generalized linear models with log link, Poisson distributions, and

287 tworks were compared between groups by using linear models with permutation testing.

288 Using generalized linear models with propensity scores, cost differences f

289 Linear models with repeated measures indicated that the

290 Data were analyzed by using ANCOVA and mixed linear models with sex and baseline value as covariates.

291 ed data from Instagram, and used generalized linear models with site- and country-level deviations to

292 e age at onset were determined using general linear models with the age at onset as the dependent var

293 ch of these time series, Poisson generalized linear models with varying lag structures were used to e

294 ernative analysis method, such as the use of linear models (with various covariance structures), and

295 and with severity of disease by generalised linear modelling, with and without adjustment for age, s

296 bular function were evaluated by generalized linear models, with adjustment for renal- and HIV-specif

297 d with HAIs were estimated using generalized linear models, with adjustments for patient demographics

298 nal problems was estimated using generalised linear models, with appropriate distribution and link fu

299 ifferences, but using BV as a covariate in a linear model would.

300 ne regressor by beta-values from the general linear model yielded regionally specific time-activity c