ライフサイエンスコーパス: regression model

1 asured compared with predicted RMR (from own regression model).

2 was analyzed using a multivariable logistic regression model.

3 identify relevant parameters for a logistic regression model.

4 rintSO2 remained significant in the multiple regression model.

5 on techniques were combined using the hybrid regression model.

6 d case fatality rates through a time-varying regression model.

7 of asthma or exacerbation after LAIV using a regression model.

8 the occurrence of BSTD using a multivariate regression model.

9 ost per bed day was projected using a linear regression model.

10 .22) using a generalized estimating equation regression model.

11 e were obtained using multivariable logistic regression model.

12 tients was examined using a modified Poisson regression model.

13 surgical revascularization using a logistic regression model.

14 alysis was carried out using multiple linear regression models.

15 assessed using multivariable competing risk regression models.

16 estimation equations and multilevel logistic regression models.

17 using multivariable Cox proportional hazards regression models.

18 imated using integrated empirical geographic regression models.

19 -effects models and Cox proportional hazards regression models.

20 on and symptomatic infection we use logistic regression models.

21 were calculated using multivariate logistic regression models.

22 with baseline characteristics of loci using regression models.

23 uring pregnancy and childhood using land-use regression models.

24 s with each dietary score (WD, PD) in linear regression models.

25 ors with incident PD using adjusted logistic regression models.

26 evaluated using univariate and multivariate regression models.

27 he BGI was tested with Cox and mixed-effects regression models.

28 were estimated using multivariable logistic regression models.

29 actors were identified and validated via Cox regression models.

30 1 were compared between groups via logistic regression models.

31 ividual outcomes were examined using Poisson regression models.

32 ariatric surgery episode using multivariable regression models.

33 dized mortality/morbidity ratio weighted cox-regression models.

34 -to-severe diarrhoea in conditional logistic regression models.

35 d risk factors for DSA development using Cox regression models.

36 linical keratoconus based in binary logistic regression models.

37 rticipant was estimated by means of land-use regression models.

38 ariable Poisson, Fine-Gray, and log-binomial regression models.

39 ), and others were estimated through Poisson regression models.

40 ng study intake variation explained by these regression models.

41 Communities study using multivariable linear regression models.

42 he two study periods were assessed using Cox regression models.

43 s were compared using multivariable logistic regression models.

44 spital mortality was analyzed using logistic regression models.

45 ment analysis using Cox proportional hazards regression modeling.

46 (0.78 [95% CI 0.77-0.78]) than the logistic regression model (0.73 [0.72-0.74]) (p < 0.001).

47 94.2%, 96.9%, 97%, and 94% for the logistic regression model; 92.7%, 100%, 100%, and 92.9% for the I

48 In a stepwise multivariate regression model, a VDP greater than 4.28% was associate

49 ted using multivariable conditional logistic regression models, according to center, sex, age, and pe

50 Adjusted Poisson regression models accounted for 14 resident covariates.

51 There was no difference in logistic regression model accuracy comparing the data by either f

52 from 146 IOP-associated variants in a linear regression model adjusted for central corneal thickness

53 es were compared between groups using linear regression models adjusted for age and sex with family m

54 We used Cox proportional regression models adjusted for age, race, smoking, diet,

55 We computed logistic regression models adjusted for age, sex, BMI, smoking st

56 were assessed using Cox proportional-hazard regression models adjusted for age, sex, education, card

57 ameters for urban air pollution using linear regression models adjusted for age, sex, smoke, time liv

58 ardiovascular risk were determined using Cox regression models adjusted for cardiovascular risk facto

59 d PROs were investigated using mixed-effects regression models adjusted for clinically-relevant confo

60 level analysis with Cox proportional hazards regression models adjusted for clustering by facility an

61 the Swiss HIV Cohort Study.We performed Cox regression models adjusted for demographic factors, base

62 We used conditional logistic regression models adjusted for HDL cholesterol levels an

63 REE were identified, and prespecified linear regression models adjusted for nusinersen treatment (dis

64 5,276) and 15 (n = 3,446) years using linear regression models adjusted for potential confounders.

65 statin C) and ACR with cancer risk using Cox regression models adjusted for potential confounders.

66 Bivariate analyses followed by logistic regression models adjusted for relevant confounders were

67 ed to sleep characteristics were assessed in regression models adjusted for sociodemographic and card

68 Multivariable linear regression models (adjusted for mid-childhood body mass

69 tbreak over the same period, using a Poisson regression model adjusting for correlation within hospit

70 ) over time was assessed with a linear mixed regression model adjusting for the effects of baseline M

71 Multivariable modified Poisson regression models adjusting for confounding by age, race

72 We fitted mixed-effects Cox regression models adjusting for multiple pregnancies per

73 was estimated using a multivariable logistic regression model, adjusting for age, sex, indigenous sta

74 A logistic regression model after the intention-to-treat principle

75 From a statistical analysis of the regression model and by using the Cramer-Rao lower bound

76 In particular, linear log-contrast regression model and Dirichlet regression model are prop

77 First, we fitted a Cox regression model and estimated the 10-year predicted ris

78 The accuracy of the logistic regression model and generalizability of the ISOLD score

79 Adjusted regression models and a meta-analysis were performed.

80 Logistic regression models and accuracy performance tests were es

81 We specify logistic regression models and adjust for a range of covariates a

82 erate-severe or severe food insecurity using regression models and algorithmic weighting procedures.

83 We used interrupted time series logistic regression models and estimated marginal effects to exam

84 series of multilevel multivariable logistic regression models and geospatially visualising unexplain

85 ctomy type (partial/radical)-to fit logistic regression models and grouped patients according to degr

86 Adjusted logistic regression models and meta-analyses were performed.

87 Logistic regression models and random forest models classified T

88 2) and persistence from 12 to 24 months into regression models and tested for the mediating effect of

89 sociations were estimated with quasi-Poisson regression models and then pooled by random-effects meta

90 ariate analysis were fed into a multivariate regression model, and models were built by combining ind

91 ch of the response using a univariate linear regression model, and to select predictors that meet a p

92 ence rates, hazard ratios using adjusted cox-regression models, and standardized mortality/morbidity

93 log-contrast regression model and Dirichlet regression model are proposed to estimate the causal dir

94 Logistic regression models assessed correlates of non-RTW, adjust

95 Random effect regression models assessed group differences in brain vo

96 Cox proportional hazards regression models assessed the association between plasm

97 We created multivariable regression models at the year, day, and visit level afte

98 ment and validation of a new signature-based regression model, augmented with a particular choice of

99 Regularized regression models built using 5hmC densities in genes pe

100 n discrimination in a multivariable logistic regression model (C-statistic 0.82 vs 0.78; p = 0.0009).

101 In a multiple linear regression model, c.

102 multivariable-adjusted conditional logistic regression models, caffeic acid (ORlog2: 0.55; 95% CI: 0

103 A multivariable logistic regression model calculated the odds ratio (OR) for SCAD

104 We fit mixed-effects Cox regression models (center as random effect) to evaluate

105 The logistic regression model combining T2-weighted SI ratio with T2-

106 Logistic regression models combining T2-weighted SI and T2-weight

107 Multivariable conditional logistic regression modeling compared the odds of undergoing scre

108 Linear regression models compared changes over time by SWAD/STA

109 Linear regression models comparing cognitive scores between par

110 ared risks of SPM using a cause-specific Cox regression model considering death as a competing risk f

111 ivariate ordinary least squares and logistic regression models controlling for a wide range of contro

112 In both multivariable logistic regression models correcting for propensity score 1 (aOR

113 Using the discovery cohort, multivariate Cox regression modeling defined a minimal model including wh

114 The optimal regression model demonstrated R(2) values of 0.92 and 0.

115 ciated with results reporting using logistic regression models, described sponsor-level reporting, ex

116 We tested logistic regression model discrimination using the C-index and ca

117 est; Spearman's correlation and log-binomial regression model estimated the association between MMPs

118 Logistic regression models estimated ORs of scoring low on 1 of 4

119 Linear and quantile regression models estimated the association between PPYE

120 Regression model estimates indicated different spatial r

121 We compared OLS and quantile regression models estimating SACE to calculate the effec

122 ss index (BMI) <25 or >=25 kg/m(2); logistic regression models evaluated preconception lipid concentr

123 Logistic regression models evaluated the relation of baseline wei

124 We constructed linear regression models evaluating the association between bas

125 Ordinary least squares regression models examined associations between burn siz

126 Multilevel mixed-effects linear regression models examined effects of age and sex.

127 ransition status, and multivariable logistic regression models examined factors associated with satis

128 Multivariable linear regression models examined the association between CBH s

129 hed NK cell subpopulations implicated in the regression model exhibited enhanced effector functions a

130 The multi-linear regression models explain about 89% to 96% of the variat

131 Our multiple linear regression model explained 61.1% of the variance in 2017

132 In Cox regression models, factors associated with a trend for r

133 Visualization of regression models fitted to the data imply that youth wi

134 A regression model for frataxin which included HAX-1, grou

135 assessed using a linear and binary logistic regression modeling for the continuous and categorical o

136 Newly derived multivariable logistic regression models for 5-year survival in new cohorts had

137 nalyses were best fit with quartic and cubic regression models for CT and FSSC/BSSC, respectively.

138 used Poisson generalized estimating equation regression models for longitudinal binary outcomes.

139 net, RF, XGBoost, LightGBM) to commonly used regression models for prediction of undiagnosed T2DM.

140 ional hazards models and hierarchical linear regression models for the primary outcomes of all-cause

141 , we assessed 33 982 HCTs using multivariate regression models for the role of HLA mismatching on out

142 We fit Weibull regression models for time to viral load >1000 copies/mL

143 A Cox proportional-hazards regression model found that the adjusted hazard rate for

144 In risk-adjusted multivariable regression models, history of previous abdominal surgery

145 ge of the fact that the conditional logistic regression model (i.e. the SSF) is likelihood-equivalent

146 Regression models identified 17 variables that were sign

147 Multivariate Cox regression models identified other predictors of disease

148 Regression models identified variables associated with d

149 type-associated SNPs in a joint multiple-SNP regression model in GWAS.

150 We derived an estimated CNS from a linear regression model in which we regressed the observed CNS

151 The binary logistic regression model included the minimal corneal thickness,

152 A linear regression model including breast volume at the start of

153 In a multivariate Cox regression model including each of the clinical and gene

154 the strongest factor in the multiple linear regression model, independently from cord atrophy.

155 Measurements and predictions of a land-use regression model indicate moderate spatial correlation b

156 Multinomial logistic regression modeling indicated that Drymarchon couperi ha

157 Regression models indicated that %ViableSperm of bulls w

158 A ridge regression model is constructed to identify the critical

159 In a multivariable regression model, K. aerogenes BSI, relative to Ecc BSI,

160 Furthermore, in a multivariate regression model, KEi(EDV) E/A ratio and 4D flow derived

161 n the multivariable Cox proportional hazards regression model, major vascular complications (P=0.044)

162 d predicted response to ICS through logistic regression models.Measurements and Main Results: We iden

163 he unmeasured CpG sites using the mixture of regression model (MRM) of radial basis functions, integr

164 an be achieved using a multivariate logistic regression model of MRI parameters after thresholding th

165 Regression modeling of epigenetic states at cCREs and ge

166 By fitting three multiple logistic regression models (one for each delivery mode), we calcu

167 In adjusted regression models, patients with large burns tended to s

168 Of the five partial least square regression models (PLSR) developed, protein, fiber and p

169 than the previous state-of-the-art logistic regression model (PPV of 17% [SD: 0.06]) and the baselin

170 We evaluated a multivariable logistic regression model predicting 5-year survival derived from

171 uated the utility of the DHIs using multiple regression models predicting moose abundance by administ

172 In adjusted regression models, prior APT was not associated with hae

173 The multivariable linear regression models reported here can inform crolibulin do

174 ne learning algorithm and traditional linear regression model, respectively, with soil temperature an

175 A logistic regression model revealed that CCC was associated with F

176 The regression models revealed a small subgroup of diagnosed

177 troscopy with 1D-CNN as a classification and regression model show a good performance, and such a met

178 The regression model showed a 37% (P < .001) reduction in cS

179 Linear regression model showed serum PCT to be a significant pr

180 Baseline stroke severity adjusted regression model showed that changes within 96-hour post

181 A logistic regression model showed that non-obese patients (BMI < 3

182 Linear regression model showed that SSPiM in the inner nuclear

183 Results from our spatial regression models showed biome stability, rainfall seaso

184 using three-level random-intercept logistic regression models, showing no differences in neonatal or

185 In multivariate regression models, strength was associated with FA (b =

186 Multivariable logistic regression models tested associations of geography and/o

187 inus the adjusted odds ratio from a logistic regression model that compared vaccination history for w

188 For AAAs smaller than 50 mm, a regression model that included both baseline WSS and lum

189 The method is based on a piecewise linear regression model that was developed to predict the bound

190 In a multiple logistic regression model the factor wound irrigation with polyhe

191 In logistic regression models, the likelihood that RGS was perceived

192 d a multivariable, multilevel random-effects regression model to analyze current factors associated w

193 We used logistic regression model to develop Model 1 by retaining the pre

194 ctivity, and trained a linear support vector regression model to estimate verbal memory performance b

195 ultivariate response random effects logistic regression model to simultaneously examine variation and

196 We used multilevel multivariable regression modeling to assess the association between da

197 We used Poisson regression modeling to calculate the prevalence ratios (

198 We used negative binomial regression modeling to determine whether daily maximum t

199 tive outcomes, we used multivariate logistic regression models to adjust for clinical and demographic

200 vidual descriptors and clusters, we used Cox regression models to assess associations with time from

201 encounter, and we used multivariate logistic regression models to assess demographic and clinical fac

202 nd, in a post-hoc analysis, we used logistic regression models to assess the association between demo

203 We used Cox regression models to calculate overall hazard ratios and

204 Logistic regression models to determine covariate risk contributi

205 We used propensity score-adjusted regression models to determine the impact of high bundle

206 We used proportional probabilities regression models to estimate fecundability ratios (FR)

207 We used time-varying Cox regression models to examine the association between 1-

208 We employed Cox proportional hazards regression models to investigate associations of PA leve

209 We used multivariable regression models to quantify the proportion of the vari

210 We used adjusted linear regression models to study the relation between aMED and

211 Here, we use beta regression models to study the socioeconomic and geograp

212 We built logistic regression models to test for associations between house

213 A logistic regression model trained using samples collected during

214 We compare the method with a random regression model using MTG2 and BLUPF90 software and qua

215 or glaucoma versus nonglaucoma from logistic regression models using MRW-BMO values from all sectors

216 permutational multivariate ANOVA and hurdle regression models using the negative binomial distributi

217 omic disadvantage with hierarchical logistic regression models, using practices serving the fewest so

218 A Sparse Partial Least Squares regression model was able to explain the combination of

219 tistical analysis, the cause-specific hazard regression model was applied, with clinically relevant C

220 d progressive number of procedures, a linear regression model was applied.

221 In this study, a logistic regression model was developed to quantify the risk of r

222 A Cox regression model was fitted to ascertain the all-cause m

223 A Cox regression model was used for statistical analyses.

224 A profile regression model was used to identify sensitive time per

225 A propensity score-weighted logistic regression model was used to mitigate potential bias ass

226 A multilevel logistic regression model was used to test the association.

227 Cox proportional hazards regression modeling was used to determine hazard ratios

228 Regression modelling was used for a statistical analysis

229 Using a multiple regression model, we show that the combination of both t

230 Using Cox proportional-hazards regression modeling, we estimated the risk of hepatocell

231 Using regression models, we investigated group difference in l

232 ated a significant difference), and binomial regression model were used to analyze differences across

233 Descriptive statistics and a binary logistic regression model were used to analyze the data.

234 Regression model were used to evaluate the relationship

235 Cox regression models were applied to analyze the associatio

236 idual univariable and multivariable logistic regression models were assessed for each time-weighted-a

237 squared (PLS)-discriminant analysis, and PLS-regression models were assessed to examine relations bet

238 Bayesian linear mixed effects regression models were constructed to evaluate associati

239 Hierarchical multivariable logistic regression models were constructed to evaluate the assoc

240 Univariate and multivariate logistic regression models were created.

241 Logistic regression models were developed using lncRNAs and/or el

242 Multivariate regression models were established using Akaike informat

243 Unadjusted estimates from linear regression models were expressed as percentage differenc

244 Regression models were fit comparing circulating miRNAs

245 Regression models were fitted to assess association betw

246 Logistic regression models were fitted to determine the associati

247 Bayesian regression models were fitted to survival outcomes and e

248 Three complementary regression models were generated for number of patients

249 Regression models were generated to predict carbonate su

250 Multiple linear regression models were performed to estimate percentage

251 comparison between groups, and multivariate regression models were used for association between MRI

252 Kaplan-Meier and landmark Cox Regression models were used for survival estimates.

253 Conditional logistic regression models were used to adjust for potential conf

254 ate and multivariate Cox proportional hazard regression models were used to analyze predictors of sur

255 Logistic regression models were used to analyze risk factors for

256 Multivariable Cox regression models were used to analyze the relationship

257 Cox regression models were used to assess associations betwe

258 Multivariable Cox regression models were used to assess associations of as

259 survival curves and Cox proportional hazard regression models were used to assess clinical character

260 Univariable and multivariable logistic regression models were used to assess predictors of mort

261 Regression models were used to assess the associations b

262 Multivariable Cox and logistic regression models were used to assess the independent re

263 Adjusted path analysis logistic regression models were used to assess the role of pre-pr

264 Unconditional logistic regression models were used to calculate lung cancer odd

265 Fixed-effects regression models were used to compare within-person eff

266 Hierarchical logistic regression models were used to determine associations be

267 Flexible semi-nonparametric regression models were used to estimate associations bet

268 Multivariate logistic and linear regression models were used to estimate associations bet

269 Unconditional logistic regression models were used to estimate odds ratios and

270 Polytomous logistic regression models were used to estimate ORs and 95% CIs

271 Logistic regression models were used to estimate progression by b

272 Multivariable logistic regression models were used to estimate the odds of in-h

273 Logistic regression models were used to estimate the odds ratio (

274 Logistic regression models were used to evaluate associations of

275 Linear regression models were used to examine associations betw

276 Generalized linear mixed regression models were used to examine the association b

277 Logistic regression models were used to examine the effect of cli

278 Regression models were used to investigate the associati

279 Random regression models were used to jointly analyse live body

280 Cox proportional hazards regression models were used to obtain age- and sex-adjus

281 Multivariable logistic regression models were used to provide adjusted rates of

282 Multivariable linear regression models were used to relate cumulative SSB con

283 Linear regression models were used to relate measures of neonat

284 Regression models were used to test associations in PWH.

285 Linear mixed regression models were used.

286 Logistic regression models were utilized to estimate the probabil

287 ymptom onset was examined in a multivariable regression model, which was reduced by stepwise backward

288 ared with those of a commonly adopted linear regression model, which we refer to here as linear trend

289 rion in a stepwise fashion to build logistic regression models, which were then translated into predi

290 Calibration data fitted a linear regression model with R(2) were values highest to 0.984.

291 ffects on the quantitative trait by a linear regression model with random effects and develop efficie

292 was calculated, and a multivariate logistic regression model with random intercepts was used to comp

293 It was based on the optimized regression model with standard-sample bracketing (ORM-SS

294 results among outpatients using mixed-effect regression models with a random effect for study site ho

295 s for mortality were calculated by using Cox regression models with emphysema as the main predictor.

296 We used Poisson regression models with log link functions to estimate ri

297 We used multivariable logistic regression models with medical school-specific fixed eff

298 adjusted cluster Chi-square and hierarchical regression models with program-level intercepts measured

299 Univariable and multivariable regression models with standardized regression coefficie

300 In a binary logistic regression model, young age (P = 0.033), history of PEP