ライフサイエンスコーパス: linear regression

1 t) and the cross-sectional study (q = 0.033, linear regression).

2 qAnti-HBc) were estimated using mixed-effect linear regression.

3 Carriage decay rate is analysed using non-linear regression.

4 the C3d-to-C3 ratio) were investigated using linear regression.

5 fficients (ICC), and Bland-Altman plots with linear regression.

6 en siblings was examined using multivariable linear regression.

7 edical facility were tested for trends using linear regression.

8 reinforcement-learning problem to a simpler linear regression.

9 ied DQIS by demographics were assessed using linear regression.

10 metry index were assessed using multivariate linear regression.

11 elates of avidity were examined in donors by linear regression.

12 sease activity were investigated by multiple linear regression.

13 dentified metabolites was investigated using linear regression.

14 ropy (FA), and global mean diffusivity using linear regression.

15 ficial neural networks performed better than linear regression.

16 The associations were examined by linear regression.

17 ses were carried out with ANOVA and multiple linear regression.

18 OOP expenditures with multivariate weighted linear regression.

19 using censored Kendall's tau correlation and linear regression.

20 ion between AEs and gender and country using linear regression.

21 iance, Pearson's chi-square test, and simple linear regression.

22 (LOD) for the isobaric internal standard in linear regression.

23 variants and metabolites were assessed using linear regression.

24 Xtreme gradient boosting as well as stepwise linear regression.

25 DMPA users and nonusers using multivariable linear regression.

26 opathology using mixed-effects multivariable linear regression.

27 trends were investigated using logistic and linear regression.

28 n worldwide was modelled using mixed-effects linear regression.

29 g data into 2-year cycles and using weighted linear regressions.

30 Log-linear regression, accounting for multiple pregnancies p

31 expenditure from total PA with multivariable linear regression adjusted for confounding factors.

32 etry outcomes were analyzed using multilevel linear regression, adjusted for age, sex, height, axial

33 composition were investigated using multiple linear regression adjusting for within-child correlation

34 We performed linear regressions adjusting for socioeconomic and lifes

35 We performed segmented linear regression, adjusting for secular trend and case

36 by the GRSs in adolescents were estimated by linear regression after adjustment for covariates.

37 es in cardiometabolic markers using multiple linear regression among 15,612 adults aged 40-78 y at ba

38 Linear regression analyses demonstrated lower FGF23 leve

39 Multivariable linear regression analyses demonstrated that non-White p

40 ified variants as determinants, we performed linear regression analyses on the residuals of the postp

41 artial least squares regression and multiple linear regression analyses prioritized three water quali

42 Linear regression analyses revealed that baseline buccal

43 r each gene mutation; (ii) weighted ordinary linear regression analyses to compare BFMMS and BFMDS ou

44 Linear regression analyses were conducted to determine w

45 Multiple linear regression analyses were performed for 12 cortica

46 Multivariable linear regression analyses were performed to evaluate th

47 Linear mixed model for repeated measures and linear regression analyses were performed.

48 m were also selected in the discriminant and linear regression analyses, and could be used as potenti

49 th CSF neurofilament light chain (NfL) using linear regression analyses.

50 correlation, area under the curve (AUC), and linear regression analyses.

51 Measurements were assessed by linear regression analyses: a between-group comparison o

52 h Initiative Observational Study, a weighted linear regression analysis and a novel penalized spline-

53 Measurements were compared by linear regression analysis and Bland-Altmann Plots, usin

54 Linear regression analysis demonstrated significant corr

55 Multiple linear regression analysis incorporating WLenh and serie

56 We ran linear regression analysis of the bronchial brushings tr

57 A multivariate linear regression analysis of the longitudinal data for

58 A multiple linear regression analysis showed that only oPMN (beta =

59 age and disease duration and (iii) weighted linear regression analysis to estimate the effect of age

60 Multivariable linear regression analysis was applied to study the diff

61 A linear regression analysis was performed and linear mult

62 Linear regression analysis was used to determine factors

63 Linear regression analysis was used to study association

64 A linear regression analysis with generalized estimating e

65 Multivariate linear regression analysis, adjusted for covariates, ind

66 Correlation statistics, linear regression analysis, and tests of means were appl

67 In linear regression analysis, BMI was positively associate

68 In multiple linear regression analysis, diabetes (coefficient, 10.1;

69 In the multiple linear regression analysis, for each increase in the con

70 Using multivariable linear regression analysis, we determined that multiple

71 Using weighted linear regression analysis, we found a strong relationsh

72 matic side chains using accurate competitive linear regression analysis.

73 rvention period, and analyzed with segmented linear regression analysis.

74 essed by multinomial regression and multiple linear regression analysis.

75 ions (DMPs and DMRs) were identified through linear regression analysis.

76 igns within the general linear model such as linear regression and analysis of covariance.

77 Multivariable linear regression and analysis of variance were used to

78 Multivariable linear regression and Bayesian kernel machine regression

79 to analyze differences in growth rates using linear regression and comparative statistics.

80 We performed multivariable linear regression and Cox proportional hazards analysis

81 score with LDL-C levels and ASCVD risk using linear regression and Cox-proportional hazard models, re

82 ementations of multivariable MR use standard linear regression and hence perform poorly with many ris

83 Linear regression and logistic regression were employed

84 Multivariable linear regression and mediation analyses were used to in

85 rship patterns over time were evaluated with linear regression and nonparametric testing.

86 We used multiple linear regression and predictive models to assess the co

87 We used multivariable linear regression and statistical mediation analyses to

88 z-score (HAZ) using difference-in-difference linear regression and the Oaxaca-Blinder decomposition m

89 ariable-adjusted associations with IOP using linear regression and with glaucoma using logistic regre

90 Using linear regressions and analyses of covariance with post

91 ssessed for allometry in all analogues using linear regressions and geometric morphometric analyses.

92 an structural equation modeling coupled with linear regressions and log normal accelerated failure-ti

93 of different structural descriptors via both linear regressions and neural networks.

94 Multivariable logistic and linear regressions and survival analysis were performed

95 analyzed demographic changes over time using linear regression, and changes in characteristics, diagn

96 um tests, Spearman correlation coefficients, linear regression, and generalized estimating equation m

97 atality was modelled for each location using linear regression, and sepsis incidence was estimated by

98 Multivariable linear regression assessed the association of cardiac st

99 After quality control and imputation, a linear regression-based association analysis was conduct

100 Multiple linear regression compared clinical parameters based on

101 Multiple linear regressions, conducted separately for CNS and non

102 ty and scar size were analyzed with multiple linear regression controlling for baseline measures.

103 ssessed by permutation analysis of pointwise linear regression criteria on HVF testing.

104 ived measures were examined voxel-wise using linear regression (cross-sectional) and linear mixed eff

105 e requirement was determined using a 2-phase linear regression crossover model to identify a breakpoi

106 Linear regression demonstrated no significant correlatio

107 atures that incumbent tests (e.g. t-test and linear regression) do not consider, which can lead to fa

108 uper-sensitive colorimetric process produced linear regression equation for H(2)O(2) as A = 0.00105C

109 Adjusting for correlated variables in linear regression explained 46.3% of the variability in

110 fects and associations were determined using linear regression, exploring maternal status as a mediat

111 Multivariate linear regression for BCVA had an R(2) value of 0.42 and

112 Multivariate linear regression for DR severity resulted in an R(2) va

113 assumption of Gaussian distributed errors in linear regression for eQTL detection, which results in i

114 absolute abundance data are modeled using a linear regression framework.

115 vitro identity relations were determined by linear regression (ideally, slope = 1 and intercept = 0)

116 Using multivariable linear regression in Hong Kong's "Children of 1997" birt

117 Covariates adjusted using multivariable linear regression included age, sex, race, AHRQ socioeco

118 sider when a perfect fit to training data in linear regression is compatible with accurate prediction

119 ed better performance compared to a constant linear regression (mean squared error = 1.10 vs. 1.59, p

120 ve developed a randomized Haseman-Elston non-linear regression method applicable when many environmen

121 incorporates outlier detection using robust linear regression methodology using a manually curated s

122 oth user-selected reference compounds and/or linear regression methods.

123 Multivariate linear regression (MLR) analysis controlled for addition

124 Multiple Linear Regression (MLR) analysis of preprocessed spectra

125 fferent modeling methods, including multiple linear regression (MLR), partial least squares regressio

126 the 95% confidence interval derived from the linear regression model (2182 versus 3110; P < 0.05).

127 erived from 146 IOP-associated variants in a linear regression model adjusted for central corneal thi

128 Our multiple linear regression model explained 61.1% of the variance

129 We derived an estimated CNS from a linear regression model in which we regressed the observ

130 A linear regression model including breast volume at the s

131 A novel evaluation tool with a linear regression model of cSMS and grass pollen counts

132 Linear regression model showed serum PCT to be a signifi

133 Linear regression model showed that SSPiM in the inner n

134 timate epigenetic age for each patient and a linear regression model tested whether chronologic age a

135 We consider a multivariate linear regression model that relates multiple predictors

136 We create a Bayesian linear regression model that uses the morphome to robust

137 The method is based on a piecewise linear regression model that was developed to predict th

138 d environmental allergies in a multivariable linear regression model to determine the effect of these

139 A linear regression model trained on historical data was u

140 time and progressive number of procedures, a linear regression model was applied.

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

142 netic effects on the quantitative trait by a linear regression model with random effects and develop

143 ployed a multivariable logistic as well as a linear regression model, adjusting for a considerable nu

144 with each of the response using a univariate linear regression model, and to select predictors that m

145 In a multiple linear regression model, c.

146 emained the strongest factor in the multiple linear regression model, independently from cord atrophy

147 d least squares method is shown to be a safe linear regression model, providing greater reliability o

148 t machine learning algorithm and traditional linear regression model, respectively, with soil tempera

149 Using a multiple linear regression model, we derived the time interval to

150 re compared with those of a commonly adopted linear regression model, which we refer to here as linea

151 unit cost per bed day was projected using a linear regression model.

152 for age, sex, and principal components in a linear regression model.

153 re projected using an ordinary least squares linear regression model.

154 arge' (PTD)-using a Hierarchical Generalized Linear Regression Model.

155 ociated with burnout in a partially adjusted linear regression model.

156 ing and IOP was assessed with a multivariate linear regression model.

157 Univariate analyses and generalized linear regression modeling were applied to test the asso

158 Multivariable linear regression models (adjusted for mid-childhood bod

159 measures were compared between groups using linear regression models adjusted for age and sex with f

160 sted within each ancestry group using robust linear regression models adjusted for age, sex, cell-typ

161 ure parameters for urban air pollution using linear regression models adjusted for age, sex, smoke, t

162 and AGE-RAGE biomarkers were examined using linear regression models adjusted for demographics, heig

163 icting REE were identified, and prespecified linear regression models adjusted for nusinersen treatme

164 8 (n = 5,276) and 15 (n = 3,446) years using linear regression models adjusted for potential confound

165 erived metabolites in plasma and urine using linear regression models adjusting for major confounders

166 ssociation of dAGEs with SAF was analyzed in linear regression models and stratified for diabetes and

167 The following operators for linear regression models are available in seagull: lasso

168 Linear regression models assessed associations of the AH

169 Adjusted linear regression models below these thresholds revealed

170 Linear regression models compared changes over time by S

171 Linear regression models comparing cognitive scores betw

172 Unadjusted and adjusted linear regression models determined associations between

173 Multiple linear regression models determined the age-stratified a

174 Mixed effects linear regression models estimated associations between

175 Linear regression models estimated the effect of sex on

176 We constructed linear regression models evaluating the association betw

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

178 Multivariable linear regression models examined the association betwee

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

180 In multivariate linear regression models for mean arterial pressure or S

181 cross-sectional analyses utilizing multiple linear regression models for SIClamp (P < 0.05); higher

182 proportional hazards models and hierarchical linear regression models for the primary outcomes of all

183 iation with medication status, we calculated linear regression models including an interaction effect

184 In multivariable linear regression models incorporating age, sex, body ma

185 The multivariable linear regression models reported here can inform crolib

186 We fit multivariable linear regression models to adjust for county-level cova

187 ility of receiving blood culture by age, and linear regression models to analyze changes by month to

188 Using linear regression models to quantify associations at 720

189 We used adjusted linear regression models to study the relation between a

190 Multiple linear regression models were constructed to determine t

191 Unadjusted estimates from linear regression models were expressed as percentage di

192 Linear regression models were fit for each of the three

193 Linear regression models were fitted to test for a linea

194 Multiple linear regression models were performed to estimate perc

195 Multiple linear regression models were performed to identify sali

196 Multinomial logistic and multiple linear regression models were trained and optimized to p

197 Multiple linear regression models were used to assess the associa

198 Multivariate logistic and linear regression models were used to estimate associati

199 Linear regression models were used to examine associatio

200 and fat," "fat," and "salt, umami and fat." Linear regression models were used to examine associatio

201 Multivariable linear regression models were used to relate cumulative

202 Linear regression models were used to relate measures of

203 Multivariate linear regression models were used to study the associat

204 By using linear regression models with field data, we estimate th

205 We fitted linear regression models with generalized estimating equ

206 ts between benefit levels using hierarchical linear regression models, and calculated Spearman's corr

207 alidation approach by applying multivariable linear regression models, machine learning techniques, a

208 tion and HOMA-IR change, we performed robust linear regression models.

209 isk in Communities study using multivariable linear regression models.

210 ical analysis was carried out using multiple linear regression models.

211 abolites with each dietary score (WD, PD) in linear regression models.

212 ing field measurements coupled with Multiple Linear Regressions Models (MLR) to predict future change

213 ective quantitative data analyses, including linear regression multivariable hierarchical modeling, d

214 as performed by ordinary least-squares (OLS) linear regression of global RNFL thickness over time.

215 .35 (95% CI, 0.01 to 0.60) from the weighted linear regression of log(HR)-OS on log(HR)-EFS.

216 opes (0.77 and 0.69, respectively) fitted by linear regression of measured and estimated chemical con

217 Ordinary least squares linear regression of SAP mean deviation (MD) and SD-OCT

218 he longitudinal outcomes by fitting a simple linear regression of the response on a time-varying cova

219 ermined visual field progression rates using linear regression of the summary index mean deviation (M

220 he registry-specific measures, a significant linear regression of total mortality rate (as well as PC

221 f enantioselectivity, including multivariate linear regression of TS energy, were carried out and the

222 We use OSM features as predictors for linear regressions of counts of traffic disruptions and

223 rly, moderate, and severe disease (ANOVA and linear regressions of thickness on VFMD).

224 ediction accuracy than ordinary least square linear regression (OLSLR) for short series of visits.

225 bal VF progression rate was calculated using linear regression on mean deviation.

226 ctoral progression rate was calculated using linear regression on the sensitivity at each VF location

227 We give a characterization of linear regression problems for which the minimum norm in

228 All linear regressions (R (2) >= 0.82, P < 0.0001) provided

229 ia and trained four machine learning models, linear regression, random forest, extreme gradient boost

230 Multivariate linear regression results show that TGC was associated w

231 tabolites were evaluated using multivariable linear regression; results were pooled by random-effects

232 Multivariate linear regression revealed that sex and MBV were associa

233 Multivariable linear regression showed significant association between

234 Linear regression showed that increased D-dimer ordering

235 9 study areas across Europe, with supervised linear regression (SLR) and random forest (RF) algorithm

236 were measured then associated using multiple linear regression stepwise analysis.

237 We conducted multiple 3-level linear regressions that accounted for repeated measures

238 We used linear regression to assess the associations between pol

239 We used multivariable linear regression to assess the value of DWMA volume, in

240 st month since infection using mixed-effects linear regression to estimate decay and when titres fell

241 We used inverse variance-weighted linear regression to estimate magnitude of the populatio

242 We used linear regression to estimate outcomes adjusting for cli

243 We used linear regression to evaluate temporal trends in inpatie

244 We used linear regression to examine country-level associations

245 We used multivariable linear regression to examine the relation between matern

246 In addition, we performed linear regression to identify clinical factors associate

247 cross DH recurrent events, and multivariable linear regression to identify determinants of DeltaA and

248 We used multilevel log-linear regression to model resource use and estimated in

249 We used multivariable linear regression to predict total payments and OOP expe

250 tification of unmodified peptides and robust linear regression to quantify the modification extent of

251 We used robust linear regression to test the associations of FA concent

252 We conducted multivariable linear regressions to examine associations between food

253 We used linear regressions to examine associations of PRSs with

254 s (CFUs) per mL CSF were analyzed by general linear regression versus day of culture over the first 1

255 Stepwise linear regression was applied to select the model best p

256 d Student t test or Mann-Whitney U test, and linear regression was performed to examine for associati

257 Hospital-level, multivariate linear regression was performed to measure the effects o

258 Linear regression was used to assess factors associated

259 Linear regression was used to associate miRNAs with chan

260 Normal linear regression was used to compare the standardized a

261 Linear regression was used to compare trends in use of g

262 Hierarchical linear regression was used to determine the association

263 ctions on lung growth.Methods: Mixed-effects linear regression was used to estimate FEV(1) and FVC fr

264 Linear regression was used to estimate the trends in ann

265 Multivariable linear regression was used to evaluate associations betw

266 crobleeds in each brain region, and multiple linear regression was used to evaluate microbleeds on ne

267 Multivariable linear regression was used to examine associations with

268 Linear regression was used to examine sex differences fo

269 Multivariable linear regression was used to identify independent clini

270 Linear regression was used to model HIV serostatus and A

271 meaningful changes were defined a priori and linear regression was used to model PCI scores on baseli

272 Multivariable linear regression was used to model the association betw

273 Linear regression was used to predict newborn TL from ma

274 Linear regression was used to test for associations betw

275 Multivariate linear regression was used to test for cross-sectional a

276 Linear regression was used to test the association betwe

277 Multivariable linear regression was used with carbohydrate knowledge,

278 Multiple linear regression was used.

279 nces in the HIV set point viral load (SPVL), linear regression was used; the frequency of the most co

280 Multiple linear regression was utilized to identify the associati

281 Using linear regression, we assessed hospital-level associatio

282 Using multilevel modeling and linear regression, we found that early poverty predicted

283 Using linear regression, we predicted physical health summary

284 Using linear regression, we tested for association between cog

285 Multinomial logistic regression and linear regression were used to relate class membership t

286 Multiple linear regressions were applied to examine associations

287 Analysis of covariance and multivariate linear regressions were conducted with sleep-related var

288 Univariate linear regressions were performed within each eye to cor

289 Multiple linear regressions were performed, while adjusting for p

290 Logistic and linear regressions were used to determine the relationsh

291 Linear regressions were used to examine associations, re

292 cs, binary logistic regression, and multiple linear regressions were used.

293 x slope, mean deviation slope, and pointwise linear regression) were used to define eyes as stable or

294 sk biomarkers were assessed by multivariable linear regression, whereas associations between TMAO and

295 ity of life-EQ-5D-5L on a 0 to 1 scale-using linear regression with adjustment for patient, tumor, an

296 meters were investigated using multivariable linear regression with generalized estimating equation m

297 ive versus objective reports over time using linear regression with interaction terms.

298 ignificance was determined using categorical linear regression with P < .05.

299 Using linear regression, with covariates of age, sex and educa

300 Associations were assessed by multivariable linear regression, with p-values corrected using the Ben