ライフサイエンスコーパス: regression model

1 conducted using the Cox proportional hazards regression model.

2 ed using a multivariate proportional hazards regression model.

3 were assessed using a hierarchical, logistic regression model.

4 ored in a similarly adjusted multiple linear regression model.

5 we refer to as the latent Dirichlet process regression model.

6 score was calculated and then entered into a regression model.

7 ed by the ensemble predictions obtained in a regression model.

8 having early or late tears using a segmented regression model.

9 rate was assessed using a negative binomial regression model.

10 incident HF was analyzed by using a Poisson regression model.

11 ing regimen, were included in a multivariate regression model.

12 structure to directly improve the log-linear regression model.

13 compared by using a Cox proportional hazards regression model.

14 mpared with reference patients in a logistic regression model.

15 implementation, analyzed using the segmented regression model.

16 residual analysis based on a multiple linear regression model.

17 independent predictors of gait speed in the regression model.

18 Results were analyzed using a logistic regression model.

19 lanomas was analyzed using an exact logistic regression model.

20 risks of outcomes were estimated by Poisson regression models.

21 ined predictors of outcomes with appropriate regression models.

22 ay on treatment effectiveness using logistic regression models.

23 of higher AE rates using generalized linear regression models.

24 nivariate analysis and multivariate logistic regression models.

25 d-use data into seasonally adjusted land-use regression models.

26 risk factors for ESRD using multivariate Cox regression models.

27 HD in offspring were analyzed using logistic regression models.

28 ed using univariate and multivariable linear regression models.

29 triacylglycerols through multivariate linear regression models.

30 he output of DHR models to static sinusoidal regression models.

31 al group were calculated with the use of Cox regression models.

32 nd asthma (n = 1405) were investigated using regression models.

33 linear and generalized linear mixed-effects regression models.

34 ases using propensity score matching and Cox regression models.

35 r of diagnosis, were estimated using Poisson regression models.

36 sex, were examined using linear and Poisson regression models.

37 dence rates were assessed by fitting Poisson regression models.

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

39 in the TD/CTD cohort were studied using Cox regression models.

40 and LTL was evaluated using multiple linear regression models.

41 lasma scores directly from food intake using regression models.

42 nalyzed in 381 participants by using Poisson regression models.

43 to all phenotypes using logistic and linear regression models.

44 d nonstatin LLT use in hierarchical logistic regression models.

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

46 tested using univariate and multivariate Cox regression models.

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

48 ion method based on latent Dirichlet Process regression models.

49 nd function by multivariable-adjusted linear regression models.

50 lities for four common variants of threshold regression models.

51 from multivariable Cox proportional hazards regression models.

52 motional outcome were calculated in multiple regression models.

53 followed by generalized estimating equation regression modeling.

54 s and controls with Cox proportional hazards regression modeling.

55 year were quantified by multivariate linear regression modeling.

56 assessed by generalized linear mixed method regression modeling.

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

58 In a hierarchical logistic regression model, a routine of early discharge (defined

59 otic melanomas were evaluated using logistic regression models adjusted for age, sex, study center, a

60 Multiple linear regression models adjusted for potential confounders wer

61 d with the use of proportional probabilities regression models adjusted for potential confounders.

62 ere assessed using linear fixed-effect panel regression models adjusted for smoke-free policies, gros

63 Cox proportional hazards regression models adjusted for socioeconomic status and

64 Multivariate Cox regression models (adjusted for age, diabetes, sex, and

65 Multiple linear regression models, adjusted for age, gender, ethnicity,

66 h outcome, we estimated conditional logistic regression models adjusting for race/ethnicity, history

67 Conditional logistic regression models adjusting for risk factors evaluated a

68 Conditional logistic regression models adjusting for serum cotinine concentra

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

70 ly (per 5% of energy) were obtained from Cox regression models, adjusting for demographic factors, me

71 ed using multivariable hierarchical logistic regression models, adjusting for important prognostic fa

72 Univariate and regression model analysis demonstrate that plasma levels

73 brid chemical transport (Geos-Chem)/land-use regression model and natural log transformed.

74 new pipeline was built based on a meta-Lasso regression model and the proof-of-concept study was perf

75 ed and analyzed to fit multivariate logistic regression models and build a risk calculator.

76 trends from 2009-2014 were calculated using regression models and compared with claims-based estimat

77 We fit covariate-adjusted linear regression models and conducted stratified analyses by c

78 nsity score-matched Cox proportional hazards regression models and Kaplan-Meier survival analysis.

79 d a series of linear continuous and logistic regression models and non-linear restricted cubic spline

80 Bivariate and multivariate logistic regression models and Odds ratio with 95% interval were

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

82 ed hazard ratios (HRs) were estimated by Cox regression models and presented with 95% CIs.

83 109) were investigated using multiple linear regression models and random intercept and random slope

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

85 binary indicator (exposed/nonexposed) to the regression model, and a method based on fractional polyn

86 sed Cox proportional hazard models, logistic regression models, and Fine-Gray competing risk regressi

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

88 operating characteristic curve for the full regression model applied to the NLST database demonstrat

89 Multivariable logistic regression modeling assessed the independent effects of

90 Multivariable logistic regression models assessed independent associations betw

91 Finally, the diagnostic accuracy of a regression model based on dentate gyrus mean diffusivity

92 lative and fish quality control aspects, PLS regression model based on spectral range 1139.9-1643.7cm

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

94 We analysed data using linear regression models, before and after adjustment for confo

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

96 The meta-regression models can be updated when additional data be

97 In logistic regression models, cannabis use at wave 1 was associated

98 In regression models, clinical variables explained approxim

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

100 iosensitive variable improved lasso logistic regression models compared to model performance without

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

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

103 Next, we created a logistic regression model, controlling for comorbidity and acuity

104 Linear regression models defined the association between early

105 The regression model determined that time to first physical

106 Separate multiple linear regression models examined the association between AMD a

107 In a Cox regression model, exposure to voriconazole alone (adjust

108 Multivariable logistic regression models fitted the association of age, sex, sm

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

110 Linear regression models focused on main and interactive effect

111 dows-PLS, were applied to develop an optimal regression model for rice amylose determination.

112 d, transcribed, and analyzed by using linear regression modeling for group differences.

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

114 as the independent variable in multivariable regression models for association with primary intracere

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

116 id tool for screening huge numbers of linear regression models for significant interaction terms in a

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

118 A univariable regression model found SP-A levels were significantly ne

119 However, random regression models found variation between individuals in

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

121 timate PM2.5 concentrations, many parametric regression models have been developed, while nonparametr

122 In regression modeling, having a CD4 count </=700 cells/mm(

123 In a multivariate time-varying Cox regression model, HCV-infected patients had a 27% increa

124 In adjusted linear regression models, higher plasma eicosapentaenoic acid (

125 Multivariable Cox proportional hazard regression modeling identified factors associated with t

126 Logistic regression models identified characteristics associated

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

128 t day 28 were retrospectively analyzed using regression models in different subgroups of patients.

129 A regression model including the baseline normal-appearing

130 Multiple regression models including all confounders indicated th

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

132 A multiple logistic regression model incorporating oxygenation index, interl

133 The analysis of deviance for the APC regression models indicated that the drift variable is r

134 The evolutionary growth/regression model introduced in this article agrees well

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

136 t test, the Mann-Whitney test, and logistic regression modeling of sample adequacy were performed.

137 e contributions of these pathways using meta-regression models of published data on dust pesticide co

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

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

140 A multiple logistic regression model predicting odds of successful oral food

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

142 s used to identify covariates for a logistic regression model predictive of severe ADAMTS13 deficienc

143 Competing risk regression models produced consistent results.

144 A censored normal regression model provided the best fit model, which pred

145 Multivariable Cox proportional-hazards regression models provided hazard ratios (HRs) with 95%

146 Stratified Cox regression models provided propensity-adjusted hazard ra

147 hat our previous work using RSA based linear regression model resulted out higher prediction quality

148 The multiple linear regression model revealed that the number of relapses an

149 Multiple logistic regression models revealed that combining the features T

150 In adjusted mixed effects regression models, shorter sleep duration (per hour less

151 Statistical regression models show that a significant part of northe

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

153 Cox proportional regression model showed the hazard ratio (HR) of acute k

154 whole, our final analysis based on logistic regression models showed that higher NDVI was a predicto

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

156 parasite prevalence, and multilevel Poisson regression models showed that such differences were infl

157 In multiple regression models, socioenvironmental determinants had c

158 Multiple regression models (standardized regression coefficients

159 In logistic regression models stratified by race, the median(range)

160 In the analysis of IPD, 2 regression models, stratified and unstratified by study,

161 We extended the classical tumor regression models such as Skipper's laws and the Norton-

162 nivariable and multivariable stepwise linear regression models, taking family structure into account.

163 Logistic regression models tested any independent relationship be

164 Multivariable linear regression models tested the association of concentric h

165 bined flow-cytometry variables in a multiple regression model that identified individuals with celiac

166 relative risk was observed for the land-use regression model that included traffic information (RR =

167 in early childhood, and we estimate flexible regression models that allow for nonlinearities in the r

168 sed risk metrics have been based on logistic regression models that incorporate aspects of the medica

169 analyzed these data using mixed-effects meta-regression models that weighted each summary statistic b

170 In fully-adjusted logistic regression model, the odds ratio (OR) per 10 unit change

171 After adjustment with logistic regression modeling, the odds of death were 14.8-fold hi

172 Based on hierarchical logistic regression models, the likelihood of receiving ranibizum

173 In the multiple logistic regression models, the median glycemic level was an inde

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

175 We used a multivariable logistic regression model to compute the conditional probability

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

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

178 nscripts, we used a cross-validated logistic regression model to identify the presence of HCC-derived

179 We then developed a Bayesian Gaussian Regression model to measure the relationship among DNA m

180 We then develop a linear regression model to predict JJA MDA8 ozone from 1980 to

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

182 dataset and then used mixed-effects logistic regression modeling to determine the effect of NAI treat

183 by applying generalized linear least-squares regression modelling to components of the multiproxy dat

184 METHOD: The authors used logistic regression models to assess prospective associations bet

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

186 on kernel and were then fitted with logistic regression models to classify steatosis, that were then

187 meters over time, and used repeated-measures regression models to define their association with LVEF

188 were tested by using multivariable logistic regression models to determine which combination of metr

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

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

191 We fitted multivariable linear regression models to estimate exposure-outcome associati

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

193 ansport models, and satellite-based land use regression models to estimate neighborhood annual averag

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

195 We used parametric Weibull regression models to estimate the time until the loss of

196 We used conditional logistic regression models to evaluate exposure risk between P kn

197 We used multivariable time-dependent Cox regression models to evaluate vaccine effectiveness, acc

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

199 Statistical analyses used linear regression models to predict post-treatment scores for e

200 Multiple regression models to predict severity were generated fro

201 METHODS AND We fit mixed-effects logistic regression models to routine surveillance data recording

202 In a Cox regression model, transplantation at the weekend was not

203 We used logistic regression models under a generalized estimating equatio

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

205 A Bayesian hierarchical logistic regression model was applied to estimate the non-updated

206 ng the occurrence of LV thrombus, a multiple regression model was applied.

207 A linear regression model was built to identify variables indepen

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

209 To adjust for selection bias, a logistic regression model was created to estimate odds ratios for

210 A multiple logistic regression model was estimated at implant and patient le

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

212 A Cox regression model was used for multivariate analysis to c

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

214 Cox proportional hazards regression model was used to compare overall survival (O

215 A proportional hazards regression model was used to estimate hazard ratios (HRs

216 A facility-level fixed-effects quasi-Poisson regression model was used to examine the incidence of ur

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

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

219 Furthermore, PLS regression model was used to quantify the levels of adul

220 Logistic regression modeling was used to examine associations bet

221 Cox proportional hazards regression modeling was used to examine the independent

222 Multivariable logistic regression modelling was used to identify predictors of

223 Linear regression modelling was used to investigate the overall

224 Using multivariable regression modeling, we assessed the hazard of developin

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

226 Using adjusted linear regression models, we analyzed associations between PRM(

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

228 Using generalised linear and logistic regression models, we examined the effect of 12 independ

229 Single and multiple linear regression modeling were performed using a broad range o

230 To do it, Partial Least Squares (PLS) regression models were applied to correlate NIR spectra

231 Regression models were built sequentially, with variable

232 Multiple-variable logistic regression models were built to compare the diagnostic y

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

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

235 Longitudinal regression models were constructed to assess association

236 Univariate then multivariable logistic regression models were constructed to assess the indepen

237 etween potato consumption and mortality, Cox regression models were constructed to estimate HRs with

238 Multivariate regression models were constructed to examine the associ

239 Regression models were developed to assess the relation

240 Logistic regression models were established for both the CVR and

241 Multiple regression models were fitted to estimate genetic effect

242 Linear regression models were fitted to population-based data o

243 e, sex, and body mass index-adjusted) linear regression models were fitted to study the association b

244 Multilevel multivariable logistic regression models were fitted, adjusting for patient var

245 Multivariate linear and logistic regression models were performed to assess factors impac

246 Bipollutant regression models were run to estimate the health associ

247 Conditional logistic regression models were used to assess associations with

248 Logistic regression models were used to assess risk associated wi

249 Cox regression models were used to assess the association be

250 Multivariable logistic regression models were used to assess the extent to whic

251 CIs calculated from Cox proportional hazards regression models were used to assess the relationship b

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

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

254 Cox regression models were used to calculate hazard ratios (

255 Cox proportional hazards regression models were used to calculate hazard ratios f

256 Meier estimates and Cox proportional hazards regression models were used to calculate hazard ratios.

257 Cox proportional hazards regression models were used to calculate relative mortal

258 Poisson regression models were used to compare outcomes after pr

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

260 ooled in case-control analyses, and logistic regression models were used to compute risks.

261 Regression models were used to determine relative risk o

262 >/=3 months, and population average logistic regression models were used to determine risk factors fo

263 Multivariable regression models were used to determine sociodemographi

264 Unadjusted and adjusted logistic regression models were used to determine the predictors

265 Separate logistic regression models were used to determine the relationshi

266 Cox proportional hazards regression models were used to estimate hazard ratios (H

267 Cox regression models were used to estimate hazard ratios fo

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

269 Multivariable conditional logistic regression models were used to estimate odds ratios adju

270 Multivariable unconditional logistic regression models were used to estimate odds ratios and

271 Poisson regression models were used to estimate the age-adjusted

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

273 Multivariable Cox proportional hazards regression models were used to evaluate relationships (h

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

275 Multivariable Poisson regression models were used to evaluate the simultaneous

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

277 Cox regression models were used to identify factors associat

278 Logistic regression models were used to obtain the odd ratios (OR

279 Regression models were used to study potential associati

280 5% confidence interval) and ordinal logistic regression models were used.

281 d sex-adjusted and multivariate-adjusted Cox regression models, whatever the significance threshold r

282 Hierarchical regression models, which accounted for similarities acro

283 t identifying relevant pathways; 2) Use of a regression model whose covariates embed all method-drive

284 Mortality risk was evaluated using Cox regression model with propensity score calibrated for ea

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

286 Mixed-effects regression models with a random intercept were used to a

287 ) were calculated using conditional logistic regression models with adjustment for important covariat

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

289 Data were analyzed using multiple logistic regression models with backward stepwise elimination (Pr

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

291 HOD: We estimate cross-sectional, ecological regression models with data from 27 European countries o

292 Logistic regression models with empirical Bayes factors were used

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

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

295 eline (time 0) biomarker profiles-both using regression models with interaction terms for treatment a

296 st episode of self-harm were analyzed in Cox regression models with time-varying treatment, adjusted

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

298 on with QOL was then assessed using a linear regression model, with binocular 10-2 VF sensitivity as

299 coabundance groups were used as outcomes in regression models, with prenatal/birth and demographic c

300 easures at 35 years (wave 10) using logistic regression models, with progressive adjustment: (1) adju