ライフサイエンスコーパス: survival analysis

1 s for PCs and mortality were evaluated using survival analysis.

2 aluated for up to 9 weeks using Kaplan-Meier survival analysis.

3 ically informative picture than Kaplan-Meier survival analysis.

4 x proportional hazard models were fitted for survival analysis.

5 g both standard regression and time-to-event survival analysis.

6 r a visual field of <10 degrees) eyes, using survival analysis.

7 ides a consistent mathematical framework for survival analysis.

8 Results were assessed using Kaplan-Meier survival analysis.

9 6% and 15%, respectively, using Kaplan-Meier survival analysis.

10 blindness were estimated using Kaplan-Meier survival analysis.

11 ween June 2010 and June 2015 included in the survival analysis.

12 iate Cox proportional hazards regression for survival analysis.

13 rator characteristic curves and Kaplan-Meier survival analysis.

14 rom 29 international sites were included for survival analysis.

15 sessed with clinical predictors of CDI using survival analysis.

16 ion model, including quantile regression and survival analysis.

17 Seizure recurrence was evaluated using survival analysis.

18 red in hours, assessed using repeated events survival analysis.

19 arisons and Kaplan-Meier plots were used for survival analysis.

20 and Cox proportional hazards regression for survival analysis.

21 individual CM phenotypes were explored using survival analysis.

22 onsidered indeterminate in a competing risks survival analysis.

23 probabilities were estimated by Kaplan-Meier survival analysis.

24 l PFS analysis and the first interim overall survival analysis.

25 by Western blot, ELISA, flow cytometry, and survival analysis.

26 ence rates were evaluated using Kaplan-Meier survival analysis.

27 (more than two antibodies) were estimated by survival analysis.

28 ssification, discovery of image markers, and survival analysis.

29 s direct comparison of clinical outcomes via survival analysis.

30 survival traits have been proposed based on survival analysis.

31 Graft success was assessed by Kaplan-Meier survival analysis.

32 arly (<1 year) and late (>1 year) PTLD using survival analysis.

33 rison with conventional clonogenic radiation survival analysis.

34 aplan-Meier and Cox regression were used for survival analysis.

35 ality and length of stay were modelled using survival analysis.

36 edding rates were determined by Kaplan-Meier survival analysis.

37 l hazards regression models and Kaplan-Meier survival analysis.

38 t from randomisation until the final overall survival analysis.

39 We present the final protocol-specified survival analysis.

40 cluster analysis, functional annotation and survival analysis.

41 e stage at a specific time with Kaplan-Meier survival analysis.

42 rtality using time-dependent competing risks survival analysis.

43 diate uveitis remission were evaluated using survival analysis.

44 was calculated using parametric conditional survival analysis.

45 d men, so we pooled data from both sexes for survival analysis.

46 Using a Kaplan-Meier survival analysis, 1-, 5-, and 10-year disease-free surv

47 CC was reported among variables entered into survival analysis, (2) survival information was availabl

48 CC was reported among variables entered into survival analysis, (2) survival information was availabl

49 On multivariable Cox proportional hazard survival analysis, a higher Society of Thoracic Surgeons

50 everal large-scale breast cancer datasets in survival analysis, a subset of these biological processe

51 using logistic regression and competing risk survival analysis, accounting for time from illness onse

52 et-Cox and applied Net-Cox for a large-scale survival analysis across multiple ovarian cancer dataset

53 In survival analysis adjusted for age, sex, and comorbidity

54 ed lifetime risk using a modified version of survival analysis adjusted for the competing risk of dea

55 in the two groups with proportional hazards survival analysis, adjusting for key prognostic variable

56 In survival analysis, adults aged 25-34 years and >/=55 yea

57 In risk-adjusted survival analysis, all 3 groups had similar 5-year morta

58 We used multistate survival analysis, allowing for delayed entry, to assess

59 V QIs and mortality rates using Kaplan-Meier survival analysis and adjusted Cox proportional hazards

60 Data were analyzed using survival analysis and adjusted for calendar year, age, a

61 Data were analyzed by survival analysis and adjusted for gender, age, calendar

62 The combination of survival analysis and algorithms linking phylogenies to

63 We performed a survival analysis and calculated an adjusted daily hazar

64 were unblinded after final progression-free survival analysis and could transition to open-label eve

65 mboembolism were examined using Kaplan-Meier survival analysis and Cox proportional hazards regressio

66 We used survival analysis and Cox regression to estimate the haz

67 d laboratory assessments were compared using survival analysis and logistic regression models.

68 morphism in disease progression in IPF using survival analysis and longitudinal decline in FVC.

69 OS) was examined using Kaplan-Meier log-rank survival analysis and multivariate Cox regression analys

70 ers), which was evaluated using Kaplan-Meier survival analysis and proportional hazards modeling.

71 Kaplan-Meier survival analysis and risk factors associated with GDD f

72 of their relation to well-known methods for survival analysis and the availability of software.

73 rkers were analyzed in Kaplan-Meier log-rank survival analysis and then multivariate Cox regression m

74 We conducted Kaplan-Meier survival analysis and used Cox proportional hazards mode

75 atients with relapsing-onset MS (ROMS) using survival analysis, and Cox regression employed to explor

76 Clinical response was entered into a survival analysis, and Cox regression was applied to the

77 tate of the art machine learning methods for survival analysis, and describe a framework for interpre

78 d SVR were tested using regression modeling, survival analysis, and locally weighted scatterplot smoo

79 We used descriptive statistics, survival analysis, and pooled logistic regression to com

80 ction in intraocular pressure from baseline, survival analysis, and reduction in the number of antigl

81 Survival analysis, and subgroup and unmatched analyses s

82 t survival was calculated using Kaplan-Meier survival analysis, and survival distributions were compa

83 By survival analysis, antifungal prophylaxis was associated

84 Survival analysis, applying cortical thickness of the id

85 Mas-o-menos is implemented for survival analysis as an option in the survHD package, av

86 The median follow-up for the updated survival analysis, as of Oct 19, 2016, was 91 months (IQ

87 On multivariable Cox survival analysis, ascending aortic area/height ratio (h

88 Methods We performed a landmark survival analysis at 7 months using the E3805 Chemohormo

89 A survival analysis based on the expression profiles of 32

90 and the risk of second eye involvement using survival analysis based on the presence of OSAS, indicat

91 rvived for 5 years (P = .014 for comparative survival analysis between patients with and without a CD

92 We used survival analysis, C statistics, and non-parametric regr

93 ncluding baseline CAC was evaluated by using survival analysis, C-statistics, net reclassification im

94 imary neuronal model in which a longitudinal survival analysis can be performed by following the over

95 A survival analysis, combining the mortality and speed of

96 On multivariate Cox survival analysis, compared to the group of iLVESD <2.5

97 Based on survival analysis, conventional RECIST might underestima

98 Cox regression survival analysis, corrected for potential modifiers, in

99 h logistic regression (disease severity) and survival analysis (cough duration).

100 Survival analysis (Cox proportional hazards modeling) wa

101 b emtansine after the second interim overall survival analysis crossed the prespecified overall survi

102 Survival analysis demonstrated a median survival time of

103 ivariable Cox proportional hazards modeling, survival analysis demonstrated a trend toward higher mor

104 Survival analysis demonstrated improved limb salvage dur

105 onsumer sensory acceptance determined by the survival analysis demonstrated that the rosemary extract

106 The survival analysis demonstrates strong prediction power o

107 We did the updated overall survival analysis, described in this Article at 77% data

108 Kaplan-Meier survival analysis did not demonstrate a difference in pa

109 In overall survival analysis, elderly recipients gained no relative

110 Survival analysis employed Kaplan-Meier curves and adjus

111 outperforms the conventional Cox regression survival analysis, especially for data sets with modest

112 A Kaplan-Meier survival analysis evaluated survival experience between

113 Survival analysis examined the effect of baseline pulse

114 itions to existing graphical and statistical survival analysis features, SurvCurv now includes extend

115 order to perform differential expression and survival analysis for a gene of interest.

116 Kaplan-Maier (KM) survival analysis for graft clarity showed cumulative su

117 tinopathy were assessed through a parametric survival analysis for interval-censored data.

118 CANARY risk groups were compared using survival analysis for progression-free survival.

119 Here, we present the final overall survival analysis for this trial.

120 hild with ASD vs an unaffected child using a survival analysis framework for time to next birth and a

121 vital signs were utilized in a discrete-time survival analysis framework to predict the combined outc

122 In a survival analysis framework, estimation of transmission

123 Survival analysis has been applied to The Cancer Genome

124 On multivariable Cox survival analysis, higher Society of Thoracic Surgeons s

125 On multivariable Cox survival analysis, higher STS score (hazard ratio [HR]:

126 Disease-free survival analysis, however, demonstrated that only 57% o

127 In univariable survival analysis, HPV positivity was significantly corr

128 Multivariate survival analysis identified four variables that were si

129 Cox regression and Kaplan-Meier survival analysis identified that amplification of the C

130 To explore this hypothesis, we also perform survival analysis in 2315 patients aged </= 40 years at

131 Survival analysis in an independent replication TMA of 3

132 The long-term 5-year actual survival analysis in case-control and case-matched popul

133 Propensity-matched survival analysis in patients with atrial fibrillation w

134 n of single genes is not straightforward and survival analysis in specific GEO datasets is not possib

135 Survival analysis included Kaplan-Meier curves and Cox r

136 In a multivariate survival analysis, increased age (P<0.0001), diabetes (P

137 Survival analysis indicated a shorter duration of presei

138 Survival analysis indicated that 50% of the patients had

139 Importantly, Kaplan-Meier survival analysis indicated that elevated KSRP expressio

140 lts of the first pre-planned interim overall survival analysis indicated that everolimus might be ass

141 Cox-regression survival analysis indicates that OTUB1 overexpression is

142 Here, we use a variant of survival analysis known as cure rate modelling to differ

143 In the survival analysis, late initiators (> 5 d) had higher mo

144 an unknown follow-up were excluded from the survival analysis, leaving 231684 patients in this cohor

145 Survival analysis, logistic regression, and interim moni

146 ology showed anaplasia (n = 8; excluded from survival analysis); low risk/completely necrotic (n = 7;

147 On multivariable Cox survival analysis, LV-GLS was independently associated w

148 b emtansine after the second interim overall survival analysis (median follow-up duration 24.1 months

149 The prespecified survival analysis method was competing risk regression.

150 ds (reactive doses) during BL DPT, using the survival analysis method, in order to suggest optimal st

151 We used non-parametric survival analysis methods to estimate gains in the popul

152 , is an improvement that complements current survival analysis methods.

153 he longitudinal cohort were compared using a survival analysis model.

154 We then did a survival analysis of 133 patients to determine whether c

155 A genome-wide survival analysis of 14,406 Alzheimer's disease (AD) cas

156 h time from ALS diagnosis to death through a survival analysis of 145 ALS patients enrolled during 20

157 Survival analysis of 2007 to 2011 California State Inpat

158 Survival analysis of a contemporaneous population of PAD

159 , race, and smoking status) progression-free survival analysis of all stage I cases.

160 Furthermore, survival analysis of CRC patients demonstrated that high

161 Here we report a statistical method SURVIV (Survival analysis of mRNA Isoform Variation), designed f

162 d a cross-sectional analysis and prospective survival analysis of patients who had undergone a Fontan

163 Survival analysis of persons carrying either the 22q11.2

164 ase, data from 18 centers were collected for survival analysis of prospectively enrolled cirrhosis pa

165 We perform survival analysis of several TCGA subtypes and find that

166 We now report a long-term survival analysis of that trial.

167 e the performance of the model, we conducted survival analysis of the dichotomized groups, and compar

168 We report results from the final overall survival analysis of the TH3RESA trial.

169 We used a survival analysis of the time to diagnosis in the offspr

170 A longitudinal survival analysis of the Veterans Aging Cohort Study inc

171 observed without a change in cell growth or survival; analysis of such pairs identifies drug equival

172 and clinical data were then used to preform survival analysis on a gene by gene basis on sub-populat

173 We performed survival analysis on a subcohort of individuals who were

174 We did implant survival analysis on all patients within the Clinical Pr

175 g exposure data, which enabled us to perform survival analysis on drug-stratified subpopulations of c

176 nsplant population was censored from further survival analysis on receipt of a transplant.

177 In a relative survival analysis, our cohort of LA positives showed a p

178 ve for a lower survival using a Kaplan-Meier survival analysis (P < 0.001).

179 On Kaplan-Meier log-rank survival analysis, patients with low CHD5 expression had

180 On Cox proportional hazards multivariable survival analysis, previous XRT remained an independent

181 Multivariate survival analysis (primary end point of all-cause mortal

182 In the multivariate survival analysis, pT3/4 stage (Hazard Ratio [HR]: 2.03,

183 In uncensored graft survival analysis, recipients older than 69 years had de

184 Survival analysis represents an important outcome measur

185 The survival analysis revealed a higher 90-day mortality ris

186 Overall, allograft survival analysis revealed a survival advantage for pati

187 Survival analysis revealed lower post-HTx survival in hi

188 Moreover, retrospective survival analysis revealed survival benefits for patient

189 A survival analysis revealed that a stronger alcohol-speci

190 Survival analysis revealed that acuity </= 0.22 logMAR w

191 Survival analysis revealed that increased expression of

192 r overall survival was crossed, this overall survival analysis serves as the final and confirmatory a

193 Kaplan-Meier's survival analysis showed a significantly higher cumulati

194 Finally, the relapse-free survival analysis showed a statistically significant dif

195 Interim overall survival analysis showed a trend favouring trastuzumab e

196 Kaplan-Meier survival analysis showed increased risk of incident synu

197 Propensity-matched Kaplan-Meier survival analysis showed no difference in graft or patie

198 Conditional survival analysis showed rates of appropriate therapy an

199 Kaplan Meier survival analysis showed significantly shorter relapse-f

200 Uni- und multivariate survival analysis showed that Klatskin tumor patients wi

201 Survival analysis showed that the median overall surviva

202 Moreover, survival analysis showed that the overall survival of pa

203 Survival analysis showed that the signature was associat

204 Survival analysis showed that the time to PTS score >/=1

205 Survival analysis showed, after adjustment for age and s

206 er, we provide a use case where we performed survival analysis showing that a loss of phosphorylation

207 ling plotting, correlation analysis, patient survival analysis, similar gene detection and dimensiona

208 Risk and survival analysis studies controlling for several potent

209 Notably, a systematic survival analysis suggested the strength of ceRNA-ceRNA

210 ble Cox regression analysis and Kaplan-Meier survival analysis, taking into account age, metastatic s

211 Data were analyzed with survival analysis techniques and adjusted for calendar y

212 chronic plaque psoriasis were compared using survival analysis techniques and predictors of discontin

213 Data were analyzed with survival analysis techniques and were adjusted for sex,

214 n follow-up could lead to bias when standard survival analysis techniques are applied.

215 cts of European ancestry) performed by using survival analysis techniques.

216 spital admission to death or discharge using survival analysis techniques.

217 factors using odds ratios from discrete time survival analysis, the area under the curve, and cross v

218 With Cox survival analysis, the diagnostic effectiveness of the S

219 By Kaplan-Meier survival analysis, the first versus the first or second

220 In the survival analysis, the hazard ratio for death in the fre

221 xploratory molecular pathologic epidemiology survival analysis, there was no significant interaction

222 We used Kaplan-Meier survival analysis to adjust for censorship due to the en

223 effect on contraceptive selection, and used survival analysis to assess pregnancy rates.

224 Within severity stratum, we used parametric survival analysis to compare length of stay by timing of

225 lepsy surgery between 1990 and 2010 and used survival analysis to detect preoperatively identifiable

226 We performed Lasso-based survival analysis to determine parameters associated wit

227 We used survival analysis to determine whether elevated FGF-23 i

228 For each state, we used discrete time survival analysis to develop age trend models for RSV ho

229 We used non-parametric survival analysis to estimate a longitudinal HIV care ca

230 were compared using unadjusted Kaplan-Meier survival analysis to estimate risk of and time to compli

231 We used survival analysis to estimate the 5-year risk of symptom

232 atios were calculated using a time-dependent survival analysis to estimate the effect of (131)I thera

233 aplan-Meier estimates and applied parametric survival analysis to examine proportions of patients wit

234 We used multivariable recurrent-event survival analysis to identify characteristics of physici

235 We use survival analysis to model this effect, and estimate tha

236 Applying survival analysis to the most thoroughly ascertained sub

237 Using survival analysis, uncorrected patient cure rates at day

238 x regression followed by genotype-stratified survival analysis using a composite endpoint of death, t

239 Out of the 35 miRNAs, survival analysis using Cox regression model identified

240 Survival analysis using Cox regression was used to estim

241 A survival analysis using demographic and echocardiographi

242 Actuarial survival analysis using Kaplan-Meier curves, Cox regress

243 ework for NSCLC computer-aided diagnosis and survival analysis using novel image markers.

244 Median follow-up time for survival analysis was 20.0 months (1.0 to 25.4 months).

245 Alzheimer's disease/senile dementia-free survival analysis was assessed using a Kaplan-Meier meth

246 Survival analysis was closed in December 2015, and no fu

247 A survival analysis was conducted comparing attrition from

248 A milestone survival analysis was conducted to capture the 5-year su

249 Survival analysis was conducted using Kaplan-Meier analy

250 Survival analysis was conducted using Kaplan-Meier curve

251 A preplanned interim overall survival analysis was conducted.

252 Kaplan Meier survival analysis was done looking at transplant-free ov

253 Survival analysis was performed for patients in the post

254 Overall survival analysis was performed on an intention-to-treat

255 Survival analysis was performed to evaluate intensificat

256 Kaplan-Meier survival analysis was performed to examine time to gener

257 Survival analysis was performed using adjusted Cox regre

258 Survival analysis was performed using Cox regression.

259 Survival analysis was performed using Kaplan-Meier curve

260 Survival analysis was performed using the Cox proportion

261 Survival analysis was performed using the Kaplan-Meier m

262 Survival analysis was performed with Cox regression with

263 Survival analysis was performed with Kaplan-Meier analys

264 id not develop melanoma were examined, and a survival analysis was performed.

265 Survival analysis was used to assess associations betwee

266 Survival analysis was used to assess individual eyes for

267 Survival analysis was used to calculate reactivation rat

268 Survival analysis was used to compare risk of disease in

269 A survival analysis was used to determine the effectivenes

270 Survival analysis was used to estimate adjusted hazard r

271 Discrete time survival analysis was used to estimate children's probab

272 Multivariable survival analysis was used to estimate influences of age

273 Survival analysis was used to examine the predictors of

274 Multivariable Cox regression survival analysis was used to identify independent progn

275 Weibull parametric survival analysis was used to model the prediction of th

276 Parametric survival analysis was used to model the time to hospital

277 Discrete-time survival analysis was used to predict the combined outco

278 Survival analysis was used to relate fatty acid composit

279 The primary endpoint was overall survival; analysis was by intention to treat.

280 Using survival analysis, we compared intervals from start of t

281 Using Kaplan-Meier survival analysis, we found 30-day mortality was 5.2% an

282 Using survival analysis, we found that people in the overweigh

283 gene expression data and combining this with survival analysis, we show that the expression of putati

284 Multivariate logistic regression and survival analysis were performed.

285 In the survival analysis, which included all 326 patients, prog

286 At the preplanned interim overall survival analysis, which was performed after 77% of the

287 ons were modeled by using interval-censoring survival analysis with 3 parametric approaches.

288 ased study.DESIGN, SETTING, AND PARTICIPANTS Survival analysis with a median follow-up of 7.6 years.T

289 Competing risk and survival analysis with adjustment for confounders were u

290 Survival analysis with an average follow-up of 13.8 year

291 We then use its output for survival analysis with clinicopathological multivariable

292 Kaplan-Meier estimation was used for survival analysis with log-rank test and Cox proportiona

293 Kaplan-Meier survival analysis with log-rank testing was performed to

294 DESIGN Survival analysis with median follow-up of 7.6 (range, 0

295 using hospital discharge data, discrete-time survival analysis with propensity score adjustment, and

296 Survival analysis with recursive partitioning in node-ne

297 Using a survival analysis with time-dependent indicators of TBI,

298 Incidence data analysis used survival analysis with time-updated covariates where app

299 On survival analysis, with median follow-up of 4.8 years, O

300 In survival analysis, younger patients presented the best p