ライフサイエンスコーパス: linear regression

1 ab initiation were compared with mixed-model linear regression.

2 tiset gene set testing to penalized multiple linear regression.

3 cal analyses comprised multiple logistic and linear regression.

4 ary function was assessed using multivariate linear regression.

5 ernal glucose and lipids, were estimated via linear regression.

6 O) were evaluated by Pearson correlation and linear regression.

7 aging features were tested by multivariable linear regression.

8 .5 exposure and skin aging manifestations by linear regression.

9 iology were identified by using multivariate linear regression.

10 ake and biomarker levels of the metals using linear regression.

11 es were tested for association with RA using linear regression.

12 ptiometry) were assessed using multivariable linear regression.

13 entration measured in bone was analyzed with linear regression.

14 ent change in rates was calculated using log-linear regression.

15 be tissue specific than eQTLs identified by linear regression.

16 trol for risk factors and copollutants using linear regression.

17 We studied the relation VA-QoL with linear regression.

18 t population for association with SNPs using linear regression.

19 sessed with analysis of variance followed by linear regression.

20 variability of SAP mean deviation (MD) using linear regressions.

21 Regularized multivariate linear regression accurately quantifies individual allel

22 Linear regression adjusted for age, ethnicity, infection

23 Linear regression adjusted for age, ethnicity, infection

24 ependent predictor of syndecan-1 by multiple linear regression adjusted for age, injury severity scor

25 ategories were evaluated using multivariable linear regression adjusting for age, race, traditional C

26 In multiple linear regression adjusting for ethnicity, BMI, LDL and

27 in 1529 females from TwinsUK using linear regressions adjusting for confounders and multipl

28 ssociation between each metric and LTL using linear regression, adjusting for demographics, blood cel

29 and HCV status with LFF using multivariable linear regression, adjusting for demographics, lifestyle

30 be used to predict (a) median dose by using linear regression after log transformation of doses and

31 ally varied allowed us to apply multivariate linear regression algorithms to establish correlations b

32 In linear regression analyses adjusted for maternal age, ra

33 composition were examined with multivariable linear regression analyses and cross-lagged modeling.

34 Linear regression analyses before and after multivariabl

35 esults from age- and race/ethnicity-adjusted linear regression analyses indicated modest, but statist

36 Results from multivariable linear regression analyses indicated that serum concentr

37 Linear regression analyses of 6-month recipient renal fu

38 Linear regression analyses were conducted between the fo

39 Multivariable linear regression analyses were conducted to explore the

40 Multiple linear regression analyses were performed to assess inte

41 Linear regression analyses were performed to determine a

42 Univariate and multivariate linear regression analyses were performed to determine t

43 On multiple linear regression analyses, ECV independently predicted

44 hin-session repeatability were assessed with linear regression analyses, intraclass correlation coeff

45 d the Kendall tau correlation, multivariable linear regression analyses, Kruskal-Wallis rank sum test

46 everity of MR imaging abnormalities by using linear regression analyses.

47 for IPH volume change were investigated with linear regression analyses.

48 atistical analysis, we used Bland-Altman and linear regression analyses.

49 A linear regression analysis adjusting for sex, age and bo

50 -media thickness (cCIMT) using multivariable linear regression analysis among 1554 African Americans

51 p and IOP and medications at one year with a linear regression analysis and survival with log-rank te

52 Linear regression analysis demonstrated a continuous rel

53 Linear regression analysis demonstrated that skin sodium

54 Linear regression analysis demonstrated that the drug de

55 Pearson's correlation, linear regression analysis for clinical outcome paramete

56 Linear regression analysis of all cases indicated that s

57 Using linear regression analysis of spectral integral values,

58 Correlation and linear regression analysis reveal a strong association b

59 Multiple linear regression analysis showed that apparent amylose

60 The linear regression analysis showed that stroke-related er

61 cted a region of interest-based multivariate linear regression analysis that was adjusted for potenti

62 We used multiple linear regression analysis to compare SMC with GES, adju

63 f the low concentration hCG protein assay in linear regression analysis was GO-peptide (1mM): GO-pept

64 or LBMADP, LBMMR-AC, and LBMFormula Further, linear regression analysis was performed on LBMMR-AC and

65 A hierarchical multiple linear regression analysis was performed to assess the r

66 Linear regression analysis was performed to identify tre

67 A hospital-level multivariate linear regression analysis was performed while controlli

68 General linear regression analysis was used to examine the assoc

69 Linear regression analysis was used to test the associat

70 lid markers was calculated through bivariate linear regression analysis, and the association between

71 lity and gait difficulty motor PD subtype in linear regression analysis, but staging of alpha-synucle

72 Using linear regression analysis, we were able to quantify the

73 estimated using survey weighted logistic and linear regression analysis.

74 empirical scaling parameters obtained from a linear regression analysis.

75 oss groups were performed using multivariate linear regression analysis.

76 e with Spearman correlation coefficients and linear regression analysis.

77 HDI] and Gross Domestic Product [GDP]) using linear regression analysis.

78 in the phantom experiment by using multiple linear regression analysis.

79 ears as dependent variables, using backwards linear regression analysis.

80 Using linear regression and adjusting for socioeconomic variab

81 ore with BP levels and incident CVD by using linear regression and Cox regression models, respectivel

82 Effects modeled using random-effects linear regression and interactions between delirium and

83 Multiple linear regression and meta-analysis were used to obtain

84 ions between SEP and BMI were examined using linear regression and multilevel models.

85 me (TKV) by magnetic resonance imaging using linear regression and multinomial logistic regression mo

86 Linear regression and proportional odds models were used

87 Multivariable mixed effects linear regressions and Pearson correlations were perform

88 s was assessed by using Pearson correlation, linear regression, and Bland-Altman analysis.

89 -phenotypes were analyzed using logistic and linear regression, and Cox proportional hazards models.

90 r levels were identified using multivariable linear regression, and Cox regression defined the associ

91 were analyzed by using Pearson correlation, linear regression, and nonlinear regression.

92 receiver-operating-characteristic analysis, linear regression, and quadratic-weighted kappa.

93 BSL distribution using a two-dimensional non-linear regression approach and correlated NBSL with sphe

94 the eQTLs identified by QRank but missed by linear regression are associated with greater enrichment

95 Effect estimates from multivariable linear regressions are presented as the percentage diffe

96 Linear regression assessed the association between imagi

97 Linear regression associated each physical performance t

98 After adjustment with linear regression, baseline miniAQLQ scores were worse i

99 care costs were compared using multivariable linear regression between patients who did and patients

100 Linear regression, Bland-Altman analysis, and paired t t

101 In a multiple linear regression, BMP-9 was independently associated wi

102 revalence and the prevalence of HIV viremia (linear regression coefficient per 1-percentage-point inc

103 ty in the range of 5-200microgL(-1), and the linear regression coefficients were higher than 0.99.

104 Multivariate linear regression, controlling for surgeon clustering, w

105 can be estimated from SPECT HMR via a simple linear regression equation, allowing use of the new card

106 ary standards on Altona had nearly identical linear regression equations (primary standard, Y = 1.05X

107 ifying mean effects on gene expression using linear regression, evidence suggests that genetic variat

108 Linear regression for internal validation of BFV and Pea

109 astfed and formula-fed infants, adjusting in linear regression for sex, gestational age, race/ethnici

110 e study of single fatty acids, showed a best linear regression for the first derivative approach in r

111 In multivariable linear regression, for participants without airflow obst

112 seasonal climate trends can be quantified by linear regression, (ii) the different seasonal records c

113 We used multivariable linear regression in a population-representative Hong Ko

114 oxin-leukocyte relationship was evaluated by linear regression in the National Health and Nutrition E

115 Using these predictors in a linear regression in the replication sample again result

116 Linear regression indicates agreement between the concen

117 regular QTL mapping approaches, i.e., simple linear regression (LR), linear mixed model (LMM), Bayesi

118 were examined with the use of multivariable linear regression.Mean absolute maternal macronutrient i

119 ire range) compared to area- or height-based linear regression methods, rivaling weighted linear regr

120 variations over time were investigated using linear regression mixed models.

121 f of concept, we developed stepwise multiple linear regression (MLR) models for species that have bee

122 bruary (DJF) mean NAO index using a multiple linear regression (MLR) technique with autumn conditions

123 In a multiple linear regression model adjusting for age, ethnicity and

124 Multiple linear regression model and distance-based redundancy an

125 A multivariate linear regression model demonstrated that MMI score sign

126 Furthermore, a linear regression model derived solely from dynamical mo

127 A weighted linear regression model evaluated associations between m

128 A linear regression model incorporating indices for the PD

129 delta T cells was demonstrated by a multiple linear regression model integrating whole blood TCR gamm

130 fact that our previous work using RSA based linear regression model resulted out higher prediction q

131 The multiple linear regression model revealed that the number of rela

132 In addition, a two-step hierarchical linear regression model showed that significant predicto

133 We used a multivariate linear regression model to examine which contextual and

134 We then develop a linear regression model to predict JJA MDA8 ozone from 1

135 A linear regression model was built to identify variables

136 For each marker, a mixed-effects linear regression model was fitted for length-for-age z

137 The Mann-Whitney U test and linear regression model were used for statistical analys

138 We constructed a mixed-effects linear regression model with the individual physician as

139 Under the linear regression model, rs139438618 at the semaphorin 3

140 sociation with QOL was then assessed using a linear regression model, with binocular 10-2 VF sensitiv

141 ut age structure to directly improve the log-linear regression model.

142 of features based on their contribution to a linear regression model.

143 t enrichment analysis (GSEA), and a multiple linear regression model.

144 is and residual analysis based on a multiple linear regression model.

145 as explored in a similarly adjusted multiple linear regression model.

146 ciations were investigated with the use of a linear regression model.For high (1.22 g/d) compared wit

147 recorded, transcribed, and analyzed by using linear regression modeling for group differences.

148 Linear regression modeling predicted that CSP-specific I

149 Linear regression modeling showed a 3.2-point improvemen

150 Single and multiple linear regression modeling were performed using a broad

151 In adjusted generalized linear regression modeling, the egg intervention increas

152 in each year were quantified by multivariate linear regression modeling.

153 nk correlation coefficients and hierarchical linear regression modeling.

154 Linear regression modelling was used to investigate the

155 tween medication and IOP were assessed using linear regression models adjusted for age, sex, body mas

156 Multiple linear regression models adjusted for potential confound

157 We used multilevel linear regression models adjusted for potential confound

158 g multinomial logistic regression and simple linear regression models adjusted for potential confound

159 folate and insulin resistance using multiple linear regression models adjusted for potential confound

160 We fit covariate-adjusted linear regression models and conducted stratified analys

161 stimated differences in continuous LPR using linear regression models and prevalence ratios for prese

162 (n = 3,109) were investigated using multiple linear regression models and random intercept and random

163 Linear regression models and the t-test were employed to

164 Multiple linear regression models consistently led to better pred

165 Multivariable linear regression models controlled for gestational age,

166 Poisson log-linear regression models controlling for temporal confou

167 Linear regression models defined the association between

168 Multivariable mixed-effects linear regression models evaluated the association betwe

169 Separate multiple linear regression models examined the association betwee

170 Linear regression models focused on main and interactive

171 and rapid tool for screening huge numbers of linear regression models for significant interaction ter

172 We built three linear regression models for slaughter age by weight and

173 g meta-analysis of group-level estimates and linear regression models of pooled data, adjusted for bo

174 Multivariable linear regression models tested the association of conce

175 ational diabetes mellitus (GDM), and we used linear regression models to estimate associations with f

176 We fitted multivariable linear regression models to estimate exposure-outcome as

177 , cell types, and covariates, we used robust linear regression models to examine associations of pren

178 We ran linear regression models to examine discontinuity points

179 We fit linear regression models to examine the association betw

180 We used multivariate linear regression models to identify the combined and in

181 Statistical analyses used linear regression models to predict post-treatment score

182 2 definitions of an episode of care and fit linear regression models to understand whether payment d

183 Single linear regression models were built with data compiled f

184 Four linear regression models were computed to examine the as

185 Linear regression models were fitted to population-based

186 ble (age, sex, and body mass index-adjusted) linear regression models were fitted to study the associ

187 Repeated measures linear regression models were used to assess differences

188 Multiple linear regression models were used to determine the asso

189 Linear regression models were used to determine the effe

190 Multivariable linear regression models were used to evaluate the assoc

191 Multivariable linear regression models were used to examine associatio

192 Mixed-effect linear regression models were used to identify system fe

193 Linear regression models were used to investigate the as

194 Linear regression models were used to investigate the as

195 c trait prediction is usually represented as linear regression models which require quantitative enco

196 lls/mul threshold were estimated using local linear regression models with a data-driven bandwidth an

197 (EMR) for 33 countries using time series log-linear regression models with vital death records and in

198 Multiple linear regression models, adjusted for age, gender, ethn

199 over time since diagnosis using generalized linear regression models, adjusting for confounders.

200 disease severity surrogate) in multivariable linear regression models, and was associated with outcom

201 We analysed data using linear regression models, before and after adjustment fo

202 In multivariable linear regression models, concentric hypertrophy was ass

203 In adjusted linear regression models, higher plasma eicosapentaenoic

204 In multivariable linear regression models, PBB and PCB congener concentra

205 icted (FEV1%) were estimated by survival and linear regression models, respectively.

206 using univariable and multivariable stepwise linear regression models, taking family structure into a

207 Using adjusted linear regression models, we analyzed associations betwe

208 elation analyses were conducted using simple linear regression models, with unadjusted r(2) values re

209 ry time and LTL was evaluated using multiple linear regression models.

210 l fibrosis, were assessed with multivariable linear regression models.

211 52 SNPs to all phenotypes using logistic and linear regression models.

212 count were examined using mixed-effects and linear regression models.

213 prostane (15-F2t-IsoP), were evaluated using linear regression models.

214 e interaction term in a very large number of linear regression models.

215 rrelation, nonparametric test of trends, and linear regression models.

216 and VSF changes were assessed using multiple linear regression models.

217 metry and function by multivariable-adjusted linear regression models.

218 dictors of higher AE rates using generalized linear regression models.

219 analyzed using univariate and multivariable linear regression models.

220 within triacylglycerols through multivariate linear regression models.

221 rrelated with the quercetin concentration by linear regression (molar extinction coefficient 23.2 (+/

222 Linear regression of (ln-transformed) consumed nutrients

223 Hierarchical multilevel linear regression of 216,350 assessments made in 71,850

224 nd 0.83 (95% CI, 0.71 to 0.88) from weighted linear regression of 8-year OS rates versus 5-year DFS a

225 Linear regression of acute individual measures with cont

226 wth in study and fellow eyes was analyzed by linear regression of square-root transformed areas.

227 es (percentages of variation explained) from linear regressions of (ln-transformed) consumed fatty ac

228 Calibration coefficients for linear regressions of observed versus predicted mortalit

229 D of the residuals of ordinary least squares linear regressions of SAP mean deviation (MD) values ove

230 y in the total number of compounds detected (linear regression; p-values: < 0.001-0.012), providing a

231 ncorporation of the covariates through a log-linear regression parametrization of the parameters of t

232 ve binominal regression (plaque number), and linear regression (plaque size), and compared between ra

233 ngle-closure glaucoma (PACG) using pointwise linear regression (PLR) trend analysis.

234 A robust linear regression, post extraction addition method was u

235 Multiple linear regressions provided insights into the relative i

236 linear regression methods, rivaling weighted linear regression, provided that response is uniform nea

237 h the PFS hazard ratio was evaluated by both linear regression (R(2)WLS) and bivariate copula (R(2)Co

238 A semilog linear regression relationship between Raman spectral al

239 esults were compared using Cohen's kappa and linear regression, respectively.

240 es and costs was estimated using Poisson and linear regression, respectively.

241 Multiple linear regression revealed a 1.89-mm decreased infiltrat

242 Additionally, multiple linear regression revealed that the long-chain-to-interm

243 onships among genes by employing regularized linear regression (ridge regression), using temporal cha

244 Results from the linear regression showed larger intruding angles were st

245 Linear regression showed ratings differed more between P

246 Linear regression showed significant effect of adduction

247 Unadjusted linear regression showed that increases in highest-level

248 tants (k values), which were obtained by non-linear regression, showed that the degradation rate of d

249 ealthy older adults, sCA was quantified by a linear regression slope of proportionate (%) changes in

250 -1 risk allele with FEV1/FVC by multivariate linear regression, stratified by asthma status.

251 Logistic and linear regression techniques were used to examine the re

252 Through multivariable linear regression, ten cord serum metabolites were ident

253 Linear regression test 1 knowledge scores were used to t

254 Multivariate linear regression tested the association of ante mortem

255 Linear regression tested the associations between pre-pr

256 and retinol concentration (from HPLC) using linear regression that estimated the difference in metab

257 Based on linear regression, the risk of EOGBS in settings with 80

258 respectively) were estimated with the use of linear regression.The mean +/- SD maternal weight gain f

259 ts (age, 18-71 years), we used multivariable linear regression to assess the independent associations

260 We used linear regression to determine change in cognitive perfo

261 We used linear regression to estimate adjusted HRR-level 30-day

262 Viva prebirth cohort, and used multivariable linear regression to estimate associations with sociodem

263 We employed multivariable linear regression to evaluate the association between tr

264 nship of indoor tanning to melanoma risk and linear regression to examine age of indoor tanning initi

265 We used linear regression to examine associations and interactio

266 mpared with alteplase-treated patients using linear regression to generate odds ratios.

267 hodology of creatinine measurement, and used linear regression to model the effects of practice chang

268 We performed multivariable linear regression to study the independent association o

269 We used linear regression to test intervention effects on use of

270 We used multivariable-adjusted linear regressions to estimate mean differences in cogni

271 We used multiple linear regressions to measure the association between co

272 Lineweaver-Burke plots and modelled with non-linear regression using reciprocal terms.

273 Linear regression was performed for each phenotype, depe

274 Multiple linear regression was performed to examine the contribut

275 A stepwise linear regression was performed to identify factors expl

276 eature matrix based on internal vectors, and linear regression was used as a learning technique.

277 Multivariable linear regression was used for the analysis of DNA methy

278 Linear regression was used to analyze the association be

279 Simple and multivariable linear regression was used to compare body mass index gr

280 Linear regression was used to control for state, age, se

281 Stepwise multiple linear regression was used to derive a GFR equation.

282 Multivariable linear regression was used to determine the association

283 Generalized linear regression was used to determine the effect of pr

284 Multiple linear regression was used to determine the relation bet

285 Multivariable Poisson log-linear regression was used to estimate adjusted risk rat

286 Linear regression was used to evaluate how levels of cor

287 Linear regression was used to examine associations betwe

288 Linear regression was used to examine risk factors for d

289 Multivariate linear regression was used to identify socio-demographic

290 Linear regression was used to study the effect of the nu

291 Using multivariable linear regression we assessed the association between ex

292 Using multivariable linear regression, we assessed the association between e

293 Log-linear regressions were adjusted for a priori selected c

294 Linear regressions were used to determine the effect of

295 In the analyses, hierarchical multiple linear regressions were used to quantify each variable's

296 ect to a sum-to-zero constraint in penalized linear regression, where the correspondence between nonz

297 Weighted linear regression with 2 linear splines and a knot at Ja

298 A multilevel, mixed-effects linear regression with intention-to-treat analyses is pr

299 atic PDFF accuracy were assessed by means of linear regression with the respective reference standard

300 after TAVR were examined using multivariable linear regression, with adjustment for baseline health s