ライフサイエンスコーパス: regression

1 moderators of efficacy were analyzed by meta-regression.

2 adjusted for baseline characteristics by Cox regression.

3 of LVAD were assessed with stepwise logistic regression.

4 ventilation in multivariable competing risk regression.

5 erosive tooth wear were assessed by logistic regression.

6 associated with PNF on multivariate logistic regression.

7 by a functional extension of the elastic-net regression.

8 cancer of two consecutive scores by logistic regression.

9 th or until December 31, 2010, with logistic regression.

10 ristic of atherosclerotic plaques undergoing regression.

11 ally matched controls by univariate logistic regression.

12 al and achieve a high rate of complete tumor regression.

13 , and multivariable Cox proportional hazards regression.

14 sue specific than eQTLs identified by linear regression.

15 mice with an IKK inhibitor resulted in tumor regression.

16 nd opinion use were evaluated using logistic regression.

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

18 hi(2) test, the Student t test, and logistic regression.

19 with disease progression using multivariate regression.

20 were calculated using multivariable logistic regression.

21 cross all variants was sought using MR-Egger regression.

22 g multiple-stressor data using dose-response regression.

23 and 95% confidence intervals computed in Cox regressions.

24 Our framework is based on Gaussian Process regression, a Bayesian learning technique, providing unc

25 hazard of CRC using Cox proportional hazards regression, accounting for within-cluster correlation by

26 We used logistic regression adjusted by age, sex, and study design featur

27 malaria does not appear to materially alter regression-adjusted prevalence estimates.

28 s was assessed using interrupted time series regression adjusting for arrest factors and temporal tre

29 in the non-incentivised group using logistic regression, adjusting for community and number of childr

30 line covariates only, and 2) made additional regression adjustment for concurrent height, weight, or

31 a significant moderator in subgroup and meta-regression analyses (slope beta = -0.16; 95% CI, -0.29 t

32 We conducted conditional logistic regression analyses adjusted for body mass index, smokin

33 Logistic regression analyses examined the association between pre

34 Hierarchical regression analyses further show that variations in spat

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

36 Results from multivariable linear regression analyses indicated that serum concentrations

37 Regression analyses revealed that this combination of fa

38 We performed multivariable Cox regression analyses to identify factors associated with

39 Cox proportional hazards regression analyses were conducted between imaging metri

40 Multivariable regression analyses were performed to assess the relatio

41 an-Meier curves and Cox proportional hazards regression analyses were used to compare OS of patients

42 d between groups using multivariate logistic regression analyses, adjusting for maternal age, ethnici

43 In regression analyses, models comprising significant varia

44 ncer were assessed in multivariable logistic regression analyses.

45 than CC-genotype patients, according to Cox regression analysis (univariate P = .040 and multivariab

46 Results were confirmed in regression analysis adjusted for team composition.

47 thickness (cCIMT) using multivariable linear regression analysis among 1554 African Americans from ME

48 Univariate and mixed-effects logistic regression analysis controlling for center effect were u

49 The use of multiple regression analysis demonstrates that FAEE content can b

50 Cox regression analysis explored risk factors for interim de

51 emission and zero-inflated negative binomial regression analysis for alcohol consumption.

52 We used logistic regression analysis for remission and zero-inflated nega

53 Multivariable Cox regression analysis identified that Model A or Model B h

54 Regression analysis indicated that the pesticide concent

55 t SCNA, we describe a method termed "Genomic Regression Analysis of Coordinated Expression" (GRACE) t

56 Correlation and linear regression analysis reveal a strong association between

57 Cox regression analysis revealed that elevated PDW was an in

58 A Cox-regression analysis revealed that mortality was much hig

59 Regression analysis revealed the two strongest independe

60 Introducing these variables to a logistic regression analysis showed areas under the receiver-oper

61 Logistic regression analysis showed that a decrease in ONH diamet

62 Regression analysis showed that cerebellar metrics accou

63 Cox regression analysis showed that MPV was an independent p

64 Univariate Cox proportional-hazards regression analysis showed that the CTC count in PPB or

65 Furthermore, regression analysis shows a positive association between

66 f resection margin status on survival, and a regression analysis to analyze positive resection margin

67 We performed multinomial logistic regression analysis to assess the weighting of histologi

68 We used multiple linear regression analysis to compare SMC with GES, adjusting f

69 We performed a multivariate regression analysis to estimate the burden of RSV in chi

70 llus PCR results, subjected to multilogistic regression analysis to identify a best-fit model for pre

71 Competing risk regression analysis was performed to calculate the risks

72 Multivariable logistic regression analysis was performed to determine variables

73 Regression analysis was used in the development of table

74 Multiple logistic regression analysis was used to estimate adjusted odds r

75 Log-binomial regression analysis was used to estimate relative risks

76 Regression analysis was used to explore the characterist

77 Multivariable ordinal logistic regression analysis with an interaction term was used to

78 In IPTW-adjusted Cox proportional hazards regression analysis, AC was associated with a significan

79 d gait difficulty motor PD subtype in linear regression analysis, but staging of alpha-synuclein path

80 Regression analysis, correlation coefficient analysis, a

81 In multivariable Cox regression analysis, treatment with either regimen (haza

82 In meta-regression analysis, variables significantly associated

83 associated with 30-day mortality in logistic regression analysis.

84 was developed using a stepwise multivariable regression analysis.

85 iable linear model for GFR using statistical regression analysis.

86 This study was a systematic review and meta-regression analysis.

87 ndependent t test, Wald chi(2), and binomial regression analysis.

88 twork for Organ Sharing, competing risk, Cox regression and Kaplan-Meier analyses were performed on f

89 We used stepwise Cox regression and the Kaplan-Meier method to assess variabl

90 aracteristic curve, Kaplan-Meier method, Cox regression, and classification and regression tree (CART

91 ypes were analyzed using logistic and linear regression, and Cox proportional hazards models.

92 ct MDD, using area under the curve, logistic regression, and linear mixed model analyses, was tested.

93 This analysis demonstrates a simple logistic regression approach for testing a priori hypotheses abou

94 The regression approach resulted in a greater median increas

95 Using a regression approach, we identified key transcription fac

96 through implementation of a powerful reverse regression approach.

97 sing interval-censored survival and binomial regression approaches a multi-model framework was implem

98 Although standard logistic regression approaches were predictive, they were minimal

99 TLs identified by QRank but missed by linear regression are associated with greater enrichment in gen

100 Linear regression assessed the association between imaging feat

101 Spherical regression at last follow-up was an average of +0.59 D.

102 were used to develop a partial least-squares regression-based model (r(2) = 0.53; Nash-Sutcliffe effi

103 ress this deficiency, we present RolyPoly, a regression-based polygenic model that can prioritize tra

104 Regression-based techniques were used to create a risk a

105 In the present work, we developed regression between ovary development and three ribosome

106 Multiple regression models (standardized regression coefficients [SRCs] and semipartial correlati

107 Logistic regressions controlled for sociodemographic, clinical, a

108 On multivariate regression controlling for injury severity and demograph

109 Eliminating PEPD causes cell death and tumor regression due to p53 activation.

110 mean effects on gene expression using linear regression, evidence suggests that genetic variation can

111 of the five immunised Tasmanian devils, and regression followed therapy of experimentally induced DF

112 Concordance between 4 antibodies revealed regression for tumor tissue cores (R2 = 0.42-0.91) and c

113 tal variable analysis, although conventional regression gave a small positive association (0.02 highe

114 In multivariable logistic regression, high safe patient handling behaviors were si

115 Logistic regression identified that dwell time was the only risk

116 produced a compound that shows durable tumor regression in a lymphoma xenograft model.

117 on of compound 28 and ibrutinib led to tumor regression in an ABC-DLBCL mouse model.

118 paclitaxel is effective in inducing disease regression in treatment-refractory breast cancer chest w

119 A meta-regression including 5 variables explained 99.6% of betw

120 Linear regression indicates agreement between the concentration

121 ertainty test, interval Partial Least Square Regression (iPLS) and Genetic Algorithm (GA).

122 lies on summary level results data, LD score regression is computationally tractable even for very la

123 ework, Logistic Multiple Network-constrained Regression (LogMiNeR), and apply it to understand the me

124 On multivariate logistic regression, lower baseline GDF-15 was associated with im

125 ssed by using comprehensive state-of-the-art regression methods that can accommodate time-dependent e

126 used difference-in-differences multivariable regression methods to analyze changes in prescriptions a

127 tion study compares methods under parametric regression misspecification; our results highlight TMLE'

128 ed risk for final VA <20/200 in the multiple regression model (OR, 4.35; P = 0.011).

129 ng the relative magnitude of the exponential regression model coefficients of independent predictors

130 A regression model including the baseline normal-appearing

131 A linear regression model incorporating indices for the PDO and A

132 A multivariate logistic regression model predicting referral to PC was created.

133 In addition, a two-step hierarchical linear regression model showed that significant predictors of B

134 We applied a Poisson regression model to analyze the longitudinal change in r

135 In this study, we proposed a random regression model to estimate genome-wide imprinting effe

136 We then used a multivariable regression model to evaluate the association between mar

137 ly downscaled using an asynchronous regional regression model to provide finer resolution future clim

138 A multivariable logistic regression model was constructed to quantify the adjuste

139 A propensity score-weighted logistic regression model was used to adjust for confounders.

140 A hierarchical logistic regression model was used to identify predictors of dela

141 mong this cohort, a Cox proportional hazards regression model was used to identify predictors of surv

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

143 tality was obtained from multilevel logistic regression model, adjusting for demographics, mechanism,

144 In a logistic regression model, more catatonia signs were associated w

145 waitlisted patients using a multivariate Cox regression model, with a competing risk approach as a se

146 residual analysis based on a multiple linear regression model.

147 independent predictors of gait speed in the regression model.

148 Results were analyzed using a logistic regression model.

149 assessed by generalized linear mixed method regression modeling.

150 thods using 2 independently derived logistic regression models (a de novo model and an a priori model

151 Multiple regression models (standardized regression coefficients

152 Multiple linear regression models adjusted for potential confounders wer

153 Conditional logistic regression models adjusting for risk factors evaluated a

154 ages 5-9 years were calculated using Poisson regression models and pooled.

155 Linear regression models and the t-test were employed to compar

156 al severity with linear and ordinal logistic regression models before and after adjusting for covaria

157 Such method consists of fitting whole-genome regression models by subsampling observations in each ro

158 We used published data to create logistic regression models comparing annual trends in the represe

159 Partial least square (PLS) regression models confirmed reliability of detection and

160 atios (HR) and 95% CIs with multivariate Cox regression models fitting stromal TILs as a continuous v

161 ideline periods in the hierarchical logistic regression models for all of the risk groups.

162 We used mixed-effects regression models for ordered-categorical outcome variab

163 Regression models gave r>0.77 confirming that Se dose an

164 Logistic regression models identified characteristics associated

165 oxidative stress, and the utility of complex regression models in capturing mediated associations whe

166 Multivariable Cox proportional hazards regression models on the risk of a disease milestone and

167 Multiple logistic regression models revealed that combining the features T

168 Multivariable Cox regression models showed that PENK level was an independ

169 Logistic regression models tested any independent relationship be

170 We used Cox proportional hazards regression models to assess the association between late

171 We used logistic regression models to estimate associations of PFASs (log

172 diabetes mellitus (GDM), and we used linear regression models to estimate associations with first-tr

173 We used Bayesian mixed-effects regression models to estimate mortality overall and from

174 We fitted a series of regression models to estimate the proportion of moderate

175 types, and covariates, we used robust linear regression models to examine associations of prenatal le

176 zard ratios were estimated with weighted Cox regression models using Barlow weights to account for th

177 Single linear regression models were built with data compiled from pre

178 Pooled multivariate logistic regression models were constructed for each infection-bu

179 Longitudinal regression models were constructed to assess association

180 Regression models were developed to assess the relation

181 Multiple regression models were fitted to estimate genetic effect

182 Multivariable hierarchical (2-level) regression models were used to calculate calendar-year r

183 Cox regression models were used to calculate hazard ratios (

184 Time-dependent Cox regression models were used to calculate hazard ratios (

185 Mixed-effects regression models were used to compare PRO scores across

186 Conditional logistic regression models were used to estimate odds ratios (ORs

187 ntially confounding covariates, and logistic regression models were used to estimate the risk of pre-

188 Multivariable binomial regression models were used to evaluate the effects of o

189 Multiple linear and logistic regression models were used to examine relations of plas

190 rcent effect changes in conditional logistic regression models with and without additional adjustment

191 ts on use of coping strategies and mediation regression models with bias-corrected bootstrapping to e

192 ned Gaussian process (GP) classification and regression models with expression and localization data

193 VL > 40 copies/mL) were estimated by Poisson regression models with generalized estimating equations

194 In regression models, APOE-e4 dose and age both consistentl

195 Using beta regression models, we analysed the outcome data released

196 Using Cox and binomial regression models, we compared the 2 randomization group

197 to all phenotypes using logistic and linear regression models.

198 d nonstatin LLT use in hierarchical logistic regression models.

199 midlife using quantile, linear, and logistic regression models.

200 tested using univariate and multivariate Cox regression models.

201 tion (PSD), were analyzed with multivariable regression models.

202 ion method based on latent Dirichlet Process regression models.

203 nd function by multivariable-adjusted linear regression models.

204 lities for four common variants of threshold regression models.

205 ay on treatment effectiveness using logistic regression models.

206 HD in offspring were analyzed using logistic regression models.

207 linear and generalized linear mixed-effects regression models.

208 mes between 1994 and 2012 using adjusted Cox regression models.

209 d with the quercetin concentration by linear regression (molar extinction coefficient 23.2 (+/-0.3)x1

210 After adjusted Cox survival regression, mortality differed significantly between the

211 nificant slope of the ordinary least squares regression of a simulated patient's mean deviation (MD)

212 Linear regression of acute individual measures with contractile

213 Histological regression of BE was seen in 41% (20/49).

214 and induces cytotoxic T-lymphocyte-mediated regression of established hepatocellular carcinoma.

215 ses against DFTD and trigger immune-mediated regression of established tumours.

216 available on whether treatment can achieve a regression of liver fibrosis in chronic HEV patients.

217 nd low-dose doxorubicin resulted in complete regression of pre-existing distant metastases in 65% of

218 onged pattern of FAdE expression and delayed regression of the postnatal fetal cortex (X-zone) were d

219 There was 100% regression of vitreous seeds after intravitreal injectio

220 emia (AML) that enabled chemotherapy-induced regressions of established disease followed by lethal re

221 resent context these variances come from the regressions of shape on some exogenous cause or effect o

222 On multivariate logistic regression, only age younger than 50 years, baseline ser

223 cantly greater in those with no pathological regression (P = 0.008).

224 RM) using two different Partial Least Square-Regression (PLS-R) multivariate quantification methods.

225 A stepwise regression procedure selected the following variables fo

226 Firth's logistic regression provides a concise statistical inference proc

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

228 Fixed effects regression showed that informal socializing and social p

229 Linear multivariate regression showed that successful agers (N = 789) report

230 Multivariable regression showed the following factors to be significan

231 k values), which were obtained by non-linear regression, showed that the degradation rate of delphini

232 ysis (muPIA) and were found to have a Deming-regression slope of 0.97 (R(2) = 0.960) when compared to

233 exhibited different limits of detection and regression slopes, indicating that the chemotaxis-relate

234 N model over HIST in both classification and regression tasks, yielding nodule classification and rat

235 g of temporal topic trends using time-series regression techniques can estimate disease case counts w

236 Multivariate linear regression tested the association of ante mortem CSF tau

237 The algorithm uses a penalized regression that balances a data fitting term with a pena

238 Using multivariate Cox proportional hazards regression, the hazard ratio of HD in the first 30 days

239 We performed logistic regression to assess correlations between exposure sourc

240 e first offered appointment; we used Poisson regression to compare the proportion of women who partic

241 pathological criteria, and used multivariate regression to control for age at death and sex.

242 tering of patients within facilities and Cox regression to determine the volume-outcome relationship,

243 We used logistic regression to estimate the association between epilepsy

244 ctors were analysed using time-dependent Cox regression to examine their potential influence on the t

245 imicrobial resistance, and negative binomial regression to examine trends in icidence of bloodstream

246 We used logistic regression to investigate factors associated with retent

247 We used linear mixed-effects regression to model the relation between each log-transf

248 ostic biomarkers would be beneficial for the regression to NGR and the early prevention of DM among p

249 We used linear regression to test intervention effects on use of coping

250 c controls and hierarchical Bayesian spatial regression) to test whether the decline in pneumonia hos

251 thod, Cox regression, and classification and regression tree (CART) analyses were performed for diagn

252 Boosted Regression Trees (BRT) analyses helped finding significa

253 ecological niches were modeled using boosted regression trees and subsequently fitted, along with add

254 r subsets that undergo cell death and tumour regression upon inhibition of CDK4 and CDK6.

255 used segmented ordinary least-squares (OLS) regression using Newey-West standard errors to accommoda

256 ically entails fitting a polytomous logistic regression via maximum likelihood estimation.

257 A weighted, multivariable, extended Cox regression was conducted, which suggested that in nutrit

258 ed with pembrolizumab, nearly complete tumor regression was observed after 4 cycles of therapy.

259 Cox proportional hazards regression was performed in propensity score-matched coh

260 of the 53 carotids with IPH at baseline, and regression was present in 16 (30%).

261 oderate, and high) were created and logistic regression was undertaken to evaluate the optimal predic

262 Multivariable Cox proportional hazards regression was used to adjust for potential confounding

263 Negative binomial regression was used to analyze changes from baseline, an

264 Poisson regression was used to analyze the relation between infl

265 Multivariable log-binomial regression was used to assess the associations of baseli

266 s were identified and multivariable logistic regression was used to determine sociodemographic factor

267 Multivariable linear regression was used to determine the association between

268 Stepwise regression was used to determine variables predicting wh

269 Multivariable Poisson log-linear regression was used to estimate adjusted risk ratios (aR

270 Cox proportional hazards regression was used to estimate hazard ratios (HRs) and

271 Cox proportional hazards regression was used to estimate HRs and 95% CIs of diabe

272 Linear regression was used to evaluate how levels of cortical F

273 Logistic regression was used to evaluate the association between

274 Weighted multivariable regression was used to examine trends in rates of sudden

275 Multivariable logistic regression was used to explore the association of diseas

276 Logistic regression was used to identify risk factors for new per

277 Logistic regression was used to investigate if patient factors, p

278 eously in a multilocus model and least angle regression was used to select the most potentially assoc

279 Using multiple logistic regression, we identified significant associations betwe

280 essel basement membrane, and the kinetics of regression were dose dependent.

281 Generalized estimating equations and Cox regression were used to assess associations of socioecon

282 Propensity score matching and stratified Cox regression were used to compare the 2 strategies.

283 Kaplan-Meier method and Cox regression were used to evaluate the prognostic impact o

284 Log-linear regressions were adjusted for a priori selected covariat

285 Durable tumor regressions were observed in seven (70%) of 10 patients

286 Logistic regressions were performed to assess if their influence

287 Logistic regressions were used to determine the association of mi

288 confidence intervals (CIs) for TBI in a Cox regression, while adjusting for age, sex, race/ethnicity

289 d 95% confidence intervals (CIs) by logistic regression with adjustment for age, gender, and smoking.

290 related to SGA risk with the use of Poisson regression with confounder adjustment; linear splines we

291 ricted splines and multivariable log-Poisson regression with empirical standard errors were used to e

292 Logistic regression with generalized estimating equations to acco

293 Multivariable logistic regression with generalized estimating equations was use

294 We used multivariate logistic regression with PCR-confirmed influenza infection as the

295 Multivariate logistic regression with restricted cubic splines was utilized to

296 Poisson regression with robust variance estimation provided prev

297 Associations were assessed using Poisson regression with robust variance estimation.

298 lan-Meier analysis, Cox proportional hazards regression with the Harrell C-index, and net reclassific

299 FF accuracy were assessed by means of linear regression with the respective reference standards.

300 AVR were examined using multivariable linear regression, with adjustment for baseline health status a

301 eater brain response 1) for prediction error regression within the caudate, ventral caudate/nucleus a