ライフサイエンスコーパス: regression

1 not low-volume aSAH (multivariable logistic regression).

2 ings was examined using multivariable linear regression.

3 detectable or undetectable with log-binomial regression.

4 ciated with PIR were assessed using logistic regression.

5 ictors of cPR were identified using logistic regression.

6 e using bivariate and multivariable logistic regression.

7 nalysis and by univariable and multivariable regression.

8 Risks of death were analyzed using Cox regression.

9 e carried out with ANOVA and multiple linear regression.

10 ching and multilevel, multivariable logistic regression.

11 (>=2 falls) before evaluation using adjusted regression.

12 etween phenotypes was assessed with logistic regression.

13 tervals (CIs) were determined using logistic regression.

14 ere pooled using 3-level random-effects meta-regression.

15 mortality using backwards stepwise logistic regression.

16 mortality between HEU and HUU using Poisson regression.

17 67, 95%CI: 0.45-0.98) only by univariate Cox regression.

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

19 cellular adenomas had long-term stability or regression.

20 %) was compared using unconditional logistic regression.

21 regression and with glaucoma using logistic regression.

22 esults generated from multivariable logistic regression.

23 TR was evaluated with multivariable logistic regression.

24 were investigated using logistic and linear regression.

25 as modeled by using Cox proportional hazards regression.

26 Models were tested with use of Cox regression.

27 , and [Formula: see text] penalized logistic regression (0.780).

28 Multivariate Cox regression adjusting for demographics and clinical measu

29 ere estimated using robust logistic quantile regression, adjusting for age, sex, ethnicity, education

30 try was assessed using multivariate logistic regression, adjusting for parity, and maternal age.

31 inverse probability treatment weighting and regression adjustment (IPTW-RA).

32 rk in murine eyes, which naturally undergoes regression after birth, to gain mechanistic insights tha

33 We propose a regression algorithm that utilizes a learned dictionary

34 Applying PLS regression allowed for the detection of ~20 percent blon

35 ardiometabolic markers using multiple linear regression among 15,612 adults aged 40-78 y at baseline

36 Logistic regression analyses adjusting for age, all the sociodemo

37 o treat by means of multilevel random effect regression analyses adjusting for clustering in health c

38 LnCeVar-Survival performs COX regression analyses and produces survival curves for var

39 Regression analyses demonstrated a trend for more leakag

40 r-regions, with adjusted linear and logistic regression analyses examining associations with immune p

41 Post hoc Cox regression analyses of outcomes by baseline HF history w

42 e analyses and multivariable binary logistic regression analyses were conducted on weighted data.

43 LD score regression analyses were first used to estimate the gene

44 Multiple linear regression analyses were performed for 12 cortical and s

45 Multivariable linear regression analyses were performed to evaluate the assoc

46 Univariable and multivariable logistic regression analyses were performed to identify parameter

47 Competing risk and Cox regression analyses were used to investigate the associa

48 d for the entire lung, and multiple logistic regression analyses with areas under the curve (AUCs) as

49 ive, sequence pattern analyses, and logistic regression analyses) aimed to detect any combinations of

50 In adjusted regression analyses, we examined associations of brain i

51 ventricle atrophy using nested multivariate regression analyses.

52 nd QLQ-OG25 were identified by multivariable regression analysis and combined to form a tool.

53 using time-dependent Cox proportional hazard regression analysis and landmark analysis.

54 Multiple logistic regression analysis demonstrated that increased inferior

55 A cox-regression analysis for post-liver transplant HCC recurr

56 Multiple linear regression analysis incorporating WLenh and series 1 DAe

57 A multivariate Cox regression analysis of the miR-21 expression in the TCGA

58 Regression analysis revealed that judgments about risks

59 Multivariate Cox regression analysis showed that age and pneumonia were i

60 Multivariate logistic regression analysis showed women with hydrosalpinx were

61 transcriptomic datasets through elastic net regression analysis to identify a gene signature that ca

62 Multivariable linear regression analysis was applied to study the difference

63 Multiple regression analysis was performed to determine whether a

64 A Cox regression analysis was performed to evaluate the risk f

65 Regression analysis was performed to test the independen

66 Logistic regression analysis was undertaken to identify independe

67 Multivariate Cox regression analysis was used to estimate risk-adjusted p

68 t hoc Bayesian analysis and a mixed logistic regression analysis were performed.

69 (r = 0.562; P-Value = 0.01, forward stepwise regression analysis).

70 In multivariable logistic regression analysis, higher baseline IOP predicted highe

71 In multivariable regression analysis, predictors of incident CKD included

72 In multivariable logistic regression analysis, risk factors for severe infection i

73 In a multivariable logistic regression analysis, we investigated the risk of IE acco

74 Multivariable logistic regression analysis, with synthetic minority oversamplin

75 tients and control individuals with logistic regression analysis.

76 l survival (OS) evaluated using adjusted Cox regression analysis.

77 V or macular atrophy were investigated using regression analysis.

78 th/transplantation) were assessed, using Cox regression analysis.

79 ORs) were calculated as part of the logistic regression analysis.

80 hosis and HCC were determined using logistic regression analysis.

81 We used ordinal logistic regression and applied generalized estimating equations

82 Ordinal logistic regression and bootstrapped backwards selection were use

83 ally-weighted scatterplot smoothing (LOWESS) regression and change-point analyses and Spearman correl

84 lied stratified linkage disequilibrium score regression and evaluated heritability enrichment in 64 g

85 lopment and validation cohorts: the logistic regression and gradient boosting machine models were tra

86 e tradeoff of quantile g-computation and WQS regression and how these quantities are impacted by the

87 vival (PFS; by RECIST) were evaluated by Cox regression and Kaplan-Meier statistics.

88 tive propensity score-matched, survival (Cox regression and Kaplan-Meier), and center effects analyse

89 Binary logistic regression and multivariate analysis were conducted with

90 port vector machine, random forest, logistic regression and Naive Bayes.

91 Logistic regression and random forests using diagnostic and proce

92 ce of the ANN was evaluated against logistic regression and the standard grading system by analysing

93 -adjusted associations with IOP using linear regression and with glaucoma using logistic regression.

94 Doubling time estimates, dose response curve regression, and comparison analyses were performed.

95 s, Spearman correlation coefficients, linear regression, and generalized estimating equation models.

96 re constructed using a partial least-squares regression approach.

97 around 90 minutes; however, local quadratic regression around the 90-minute cutoff did not reveal ev

98 D) score at waitlist removal for "too sick." Regression assessed the association between social deter

99 Meta-regression at 1.5 T and 3.0 T identified vendor (beta at

100 Bisque implements a regression-based approach that utilizes single-cell RNA-

101 Overall, regression-based machine learning models are efficient t

102 We further developed a regression-based model to estimate the correlation betwe

103 The Wilcoxon signed rank test, orthogonal regression, Bland-Altman analysis, and coefficients of v

104 The logistic regression coefficients were identical between the metho

105 Based on the regression coefficients, a score for each PIRO component

106 Multiple linear regressions, conducted separately for CNS and non-CNS su

107 95% confidence intervals (CIs) using Poisson regression, controlling for potential confounding factor

108 imensional classification task with a larger regression dataset, allowing for the creation of deeper

109 units, calculated using proportional hazards regression, declined steadily with age at BMI assessment

110 In multiple variable regression, discharge to a subacute care facility was as

111 that incumbent tests (e.g. t-test and linear regression) do not consider, which can lead to false pos

112 Akaike information criteria (AIC) and slope-regression ERPs [rERPs; N.

113 Modified Poisson regression estimated perinatal mental illness risk betwe

114 Random-effects meta regression estimated whether sex differences in not enro

115 ion of Gaussian distributed errors in linear regression for eQTL detection, which results in increase

116 arameters show strong correlations for which regression formulae are given.

117 ch 38 loci would be missed within a logistic regression framework with a binary phenotype defined as

118 For ML, ridge penalized regression has been applied to 38 features extracted fro

119 On meta-regression, ICU admission was predicted by increased leu

120 herapeutically adapted for driving neovessel regression in ocular diseases.

121 mmed cell death 1 (PD1) can result in tumour regression in preclinical models and can improve antican

122 mated using mixed-effects linear or logistic regression, including a random effect to adjust for with

123 ter early left ventricular mass index (LVMi) regression is associated with fewer hospitalizations 1 y

124 d whether they contribute to atherosclerosis regression is not known.

125 atform differences by a linear mixed effects regression (LMER) model, and estimate them from matched

126 0% and specificity~90-93% through the sparse regression machine learning of patterns.

127 the number of markers, a penalized multiple regression method can be adopted by fitting all bins to

128 r-selected reference compounds and/or linear regression methods.

129 Multivariate linear regression (MLR) analysis controlled for additional fact

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

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

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

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

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

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

136 Regression model estimates indicated different spatial r

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

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

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

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

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

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

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

144 Linear regression model showed that SSPiM in the inner nuclear

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

146 A logistic regression model trained using samples collected during

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

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

149 A Cox regression model was used for statistical analyses.

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

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

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

153 tients was examined using a modified Poisson regression model.

154 surgical revascularization using a logistic regression model.

155 was analyzed using a multivariable logistic regression model.

156 94.2%, 96.9%, 97%, and 94% for the logistic regression model; 92.7%, 100%, 100%, and 92.9% for the I

157 Multinomial logistic regression modeling indicated that Drymarchon couperi ha

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

159 Regression modelling was used for a statistical analysis

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

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

162 5,276) and 15 (n = 3,446) years using linear regression models adjusted for potential confounders.

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

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

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

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

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

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

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

170 Linear and quantile regression models estimated the association between PPYE

171 Logistic regression models evaluated the relation of baseline wei

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

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

174 Regression models identified 17 variables that were sign

175 Regression models identified variables associated with d

176 Regression models indicated that %ViableSperm of bulls w

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

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

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

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

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

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

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

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

185 Cox regression models were applied to analyze the associatio

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

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

188 Regression models were fitted to assess association betw

189 Logistic regression models were fitted to determine the associati

190 Three complementary regression models were generated for number of patients

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

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

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

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

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

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

197 Random regression models were used to jointly analyse live body

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

199 Linear regression models were used to relate measures of neonat

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

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

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

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

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

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

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

207 ng study intake variation explained by these regression models.

208 Communities study using multivariable linear regression models.

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

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

211 Developmental regression occurred in all Rett syndrome participants (m

212 Conditional logistic regression odds ratios (ORs) accounting for individual m

213 On multivariate regression (odds ratio or hazard ratio, 95% confidence i

214 ting lymphocytes (TILs) can mediate complete regression of certain human cancers.

215 ment due to high response rates and profound regression of malignant melanomas carrying BRAF(V600E) m

216 with various neuromuscular diseases, such as regression of motor neuron axons, motor neuron death, an

217 Meta-regression of the effect of dose on mortality did not re

218 ndent mouse and primary human cells, causing regression of the malignant clones in vivo, and inducing

219 Multivariate logistic regression of the retrospective cohort demonstrated pred

220 stry-specific measures, a significant linear regression of total mortality rate (as well as PCa speci

221 ioselectivity, including multivariate linear regression of TS energy, were carried out and the obtain

222 , (ROC analysis, followed by binary logistic regression) only Ultrasound depth is a significant predi

223 fter adjustment using multivariable logistic regression, patients in the high-risk group were more li

224 Finally, partial least squares regression (PLSR) was used to estimate the amount of adu

225 We compared, by Cox proportional hazards regression, progression-free survival (PFS) after relaps

226 lness and should be dosed "enough," logistic regression, propensity score matching, and inverse proba

227 ses using traditional multivariable logistic regression, propensity score matching, propensity score

228 algorithms were developed based on logistic regression, random forests, gradient boosted trees and a

229 Regression remained significant when performed on 200 bo

230 Meta-regression revealed an increase in Anisakis spp. abundan

231 problem: sparse label-noise-robust logistic regression (Rlogreg), robust elastic net based on the le

232 f-the-art methods in both classification and regression settings under various data settings.

233 ceptor reflectivity and intact RPE after SDD regression should be seen in the larger context of outer

234 Logistic regression showed increasing odds of respiratory failure

235 mically scaled tumor volume are estimated as regression splines in a generalized additive mixed model

236 150 minutes by the residuals of a nonlinear regression that predicted TG at 150 minutes as a functio

237 and Basenji, by applying stratified LD score regression to 41 diseases and traits (average N = 320K),

238 Negative binomial regression to account for multiple primary events gave a

239 We used chi2 statistics and ordinal regression to assess the significance of associations an

240 We used multivariable conditional logistic regression to calculate odds ratios (ORs).

241 xacerbation groups and then used statistical regression to compare this VDP threshold against convent

242 using random effects multivariable logistic regression to control for confounding.

243 odels were built using Partial Least Squares regression to determine dry matter (DM), soluble solids

244 dels to estimate tree densities and logistic regression to estimate mortality by size class.

245 We used log-linear binomial regression to estimate risk ratios (RRs) and 95% CIs.

246 crossover analysis with conditional logistic regression to estimate the association between hourly pa

247 DVS sales data and difference-in-differences regression to evaluate how WIC authorization affected sa

248 We used linear regression to examine country-level associations between

249 ds during the study period and used logistic regression to examine sociodemographic and clinical fact

250 In addition, we performed linear regression to identify clinical factors associated with

251 We used multivariable ordinal logistic regression to identify factors associated with indolent

252 AD/PD followingly was determined by the Cox regression to identify potential confounding factors.

253 ks and computational paradigms, ranging from regression to image classification and reinforcement lea

254 imputation for missing covariates, logistic regression to model the association between PFAS exposur

255 clones and with Partial Least Squares (PLS) regressions to predict its contents in soluble solids (S

256 We used a Poisson regression tree model to estimate an optimal VDP thresho

257 nent was developed, and a classification and regression tree was used to stratify patients into diffe

258 t-offs estimated from the classification and regression tree, patients were stratified into different

259 work (DBN) inference algorithm, genist, or a regression tree-based pipeline, rtp-star.

260 s associated with a significant pathological regression (TRG1-2 = 44% vs 8%, P < 0.001) and a trend t

261 of pre-eclampsia and GHTN with log-binomial regression using generalized estimating equations to acc

262 eased sensitivity vs cytology when comparing regression vs persistence/progression.

263 was the strongest biomarker associated with regression vs progression.

264 The average myopic regression was - 0.51 +/- 0.38 D.

265 Stepwise linear regression was applied to select the model best predicti

266 le case of CLL relapse following spontaneous regression was associated with increased BCR signaling,

267 Robust regression was carried out, adjusting for maternal age,

268 re performed for each comparison, and a meta-regression was conducted to adjust for use and duration

269 Multiple logistic regression was performed on demographic and anatomic fac

270 Cox regression was performed to determine the prognostic rel

271 Stepwise logistic regression was performed to select the optimal combinati

272 Survey-adjusted logistic regression was used to compare the odds for in-hospital

273 Conditional logistic regression was used to create models of associations bet

274 Cox regression was used to estimate hazard ratios (HRs) for

275 Cox regression was used to estimate hazard ratios of dementi

276 Poisson regression was used to evaluate the association between

277 Multivariable linear regression was used to identify independent clinical pre

278 Multivariable logistic regression was used to identify risk factors for the pri

279 ful changes were defined a priori and linear regression was used to model PCI scores on baseline PCI,

280 Multivariable linear regression was used to model the association between apn

281 Multivariate linear regression was used to test for cross-sectional associat

282 Multiple linear regression was utilized to identify the associations amo

283 riptive statistics and multivariate logistic regression, we examined the association (P < .05) betwee

284 e-crossover design, and conditional logistic regression, we examined the association between source-s

285 riable logistic and Cox proportional hazards regression were applied.

286 Backward selection and multivariate logistic regression were conducted to assess risk of GI adverse e

287 Single factor analysis and logistic regression were performed, and a composite risk score wa

288 alysis of covariance and multivariate linear regressions were conducted with sleep-related variables

289 Univariable and multivariable Cox regressions were performed to determine the prognostic v

290 Univariate and multivariate logistic regressions were performed, and population attributable

291 and risk factors for fatal outcome (logistic regression) were evaluated.

292 arkers were assessed by multivariable linear regression, whereas associations between TMAO and the fe

293 thod is based on Bayesian variable selection regression, which not only accounts for cis- and trans-e

294 tions to predict GGG 1 vs >1, using logistic regression with a nested leave-pair out cross validation

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

296 e who did not using Cox proportional hazards regression with inverse probability weighting.

297 e applied multiexposure linear mixed-effects regressions with participant-level random intercept to i

298 Cox regression, with adjustment for age (as the underlying m

299 s (VE) was estimated by conditional logistic regression, with adjustment for reported contact with ch

300 ves were estimated by weighted least squares regression (WLS), confirming heteroscedasticity for all