ライフサイエンスコーパス: survival analysis

1 te patterns of recurrence and did a post-hoc survival analysis.

2 s and features uni- and multivariate regulon survival analysis.

3 Therefore, 99 countries were included in the survival analysis.

4 was calculated using parametric conditional survival analysis.

5 sessed with clinical predictors of CDI using survival analysis.

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

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

8 t from randomisation until the final overall survival analysis.

9 We present the final protocol-specified survival analysis.

10 cluster analysis, functional annotation and survival analysis.

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

12 rtality using time-dependent competing risks survival analysis.

13 diate uveitis remission were evaluated using survival analysis.

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

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

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

17 ically informative picture than Kaplan-Meier survival analysis.

18 x proportional hazard models were fitted for survival analysis.

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

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

21 iously reported at the first interim overall survival analysis.

22 ides a consistent mathematical framework for survival analysis.

23 Results were assessed using Kaplan-Meier survival analysis.

24 6% and 15%, respectively, using Kaplan-Meier survival analysis.

25 blindness were estimated using Kaplan-Meier survival analysis.

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

27 iate Cox proportional hazards regression for survival analysis.

28 ients with available data using Kaplan-Meier survival analysis.

29 rator characteristic curves and Kaplan-Meier survival analysis.

30 rom 29 international sites were included for survival analysis.

31 ion model, including quantile regression and survival analysis.

32 Seizure recurrence was evaluated using survival analysis.

33 Kaplan-Meier curves were generated for survival analysis.

34 identification of associated predictors used survival analysis.

35 deprivation score were predictive in either survival analysis.

36 a gene expression composite score to aid in survival analysis.

37 tional glaucoma surgery were censored in the survival analysis.

38 d transplant recipients, using multivariable survival analysis.

39 d the incidence of sequelae was studied with survival analysis.

40 gression through IFE steps was assessed with survival analysis.

41 justed analyses employed flexible parametric survival analysis.

42 nd prolongation of gestation in a multilevel survival analysis.

43 zards regression analyses were performed for survival analysis.

44 e features in large-scale molecular data for survival analysis.

45 093 patients respectively were available for survival analysis.

46 to advanced AMD was evaluated using stepwise survival analysis.

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

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

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

50 Exclusion of early postoperative death from survival analysis, 3) Method of data extraction used, an

51 ave used pathway-level predictors for cancer survival analysis, a comprehensive comparison of pathway

52 On survival analysis, a favorable prognosis was associated

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

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

55 Prognostic value is confirmed by survival analysis accounting for clinical variables.

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

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

58 Logistic regression and survival analysis adjusted with IPW method were performe

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

60 irstly, this study aims to develop the novel survival analysis algorithms to explore the key genes an

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

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

63 In IPTW-RA survival analysis, an average FHS user had a 44% lower h

64 as 15 months at the primary progression-free survival analysis and 24 months at the overall survival

65 nd assessed risk factors for mortality using survival analysis and a Cox proportional hazards regress

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

67 The combination of survival analysis and algorithms linking phylogenies to

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

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

70 Kaplan-Meier survival analysis and Cox proportional hazards models we

71 eveloping NVG was assessed with Kaplan-Meier survival analysis and Cox proportional hazards models.

72 Kaplan-Meier survival analysis and Cox proportional hazards regressio

73 ronic kidney disease were investigated using survival analysis and Cox proportional hazards.

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

75 rning framework based on neural networks for survival analysis and evaluate it in a genomic cancer re

76 At the time of final progression-free survival analysis and interim overall survival analysis

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

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

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

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

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

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

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

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

85 vax recurrence was estimated by Kaplan-Meier survival analysis, and risk factors for first and recurr

86 Survival analysis, and subgroup and unmatched analyses s

87 Survival analysis, applying cortical thickness of the id

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

89 le logistic regression and interval-censored survival analysis, as well as with graft failure and mor

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

91 incidence between groups using discrete-time survival analysis at 6 and 12 months.

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

93 A survival analysis based on the expression profiles of 32

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

95 Survival analysis between ST/CC groups and risk factors

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

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

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

99 Conditional survival analysis can provide specific guidance for coun

100 A survival analysis, combining the mortality and speed of

101 Individual dataset-based survival analysis, comparative analysis, and correlation

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

103 3 participants, our final cohort for overall survival analysis comprised 129 (64%) participants.

104 Multivariate survival analysis confirmed the dominance of molecular A

105 recautions in a multivariable, discrete time survival analysis, controlling for patient demographics,

106 t Precautions in multivariable discrete-time survival analysis, controlling for patient demographics,

107 Based on survival analysis, conventional RECIST might underestima

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

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

110 Survival analysis demonstrated a median survival time of

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

112 Survival analysis demonstrated treatment success in 70%,

113 Besides, survival analysis demonstrates that the top-ranked miRNA

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

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

116 Survival analysis employed Kaplan-Meier curves and adjus

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

118 Kaplan-Meier survival analysis estimated a better globe salvage rate

119 A Kaplan-Meier survival analysis evaluated survival experience between

120 Survival analysis, following propensity score matching,

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

122 We did a survival analysis for countries with data in the follow-

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

124 Indeed, Kaplan-Meier survival analysis for patients with pancreatic, renal, l

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

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

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

128 In a survival analysis framework, estimation of transmission

129 r software capable of conducting genome-wide survival analysis (genipe, SurvivalGWAS_SV and GWASTools

130 Since the confirmatory overall survival analysis had also occurred before this prespeci

131 umber of events to trigger the final overall survival analysis had not been reached.

132 Survival analysis has been applied to The Cancer Genome

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

134 ese signatures with lower mortality based on survival analysis (HR 0.36; 95% confidence interval, 0.1

135 Multivariate survival analysis identified four variables that were si

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

137 In the exploratory overall survival analysis in patients with PD-L1 immune cell-pos

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

139 Survival analysis included Kaplan-Meier curves and Cox r

140 Survival analysis indicated that 50% of the patients had

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

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

143 Cox-regression survival analysis indicates that OTUB1 overexpression is

144 We performed survival analysis investigating the age at first SCD in

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

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

147 Survival analysis log-rank test examined associations be

148 e difference, 8.7% [95% CI, 1.8%-16.4%] from survival analysis; log-rank P = .009).

149 Survival analysis, logistic regression, and interim moni

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

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

152 n-free survival analysis and interim overall survival analysis (May 31, 2019), median progression-fre

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

154 lock (March 21, 2018) for the final overall survival analysis, median follow-up was 7.4 months (IQR

155 The prespecified survival analysis method was competing risk regression.

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

157 der to suggest optimal step doses, using the survival analysis method.

158 Survival analysis methods that integrate pathways/gene s

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

160 Survival analysis methods were used to estimate the cumu

161 Standard survival analysis methods were used.

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

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

164 On multivariate survival analysis MUST (HR: 1.49 95%CI 1.12-01.98 p < 0.

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

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

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

168 Survival analysis of 2007 to 2011 California State Inpat

169 Survival analysis of a contemporaneous population of PAD

170 esults from a prespecified, interim, overall survival analysis of ALCYONE with more than 36 months of

171 Furthermore, survival analysis of CRC patients demonstrated that high

172 intention-to-treat primary progression-free survival analysis of ICON8, which defined progression-fr

173 nterim analysis, this second interim overall survival analysis of IMpassion130 indicates no significa

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

175 We recently performed a Kaplan-Meier survival analysis of naked mole-rats (Heterocephalus gla

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

177 Survival analysis of patients who underwent tumor resect

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

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

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

181 A propensity-matched survival analysis of the ICD Registry was performed eval

182 port the prespecified second interim overall survival analysis of the phase 3 IMpassion130 study asse

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

184 Survival analysis of the time to autism diagnosis with C

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

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

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

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

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

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

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

192 the all-cause mortality demonstrated in the survival analysis (P = 0.87).

193 Survival analysis performed based on the Kaplan-Meier me

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

195 On survival analysis, primary nonrheumatic MS (hazard ratio

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

197 Survival analysis represents an important outcome measur

198 Survival analysis results show that most non-treatment b

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

200 Survival analysis revealed an increased risk of recurren

201 Kaplan-Meier survival analysis revealed that high G9a expression is a

202 Survival analysis revealed that increased expression of

203 Multivariate survival analysis revealed that TBR(max) changes predict

204 Further survival analysis reveals that intact glycopeptide signa

205 Finally, multivariate survival analysis reveals that ZEB1 and its expression s

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

207 Survival analysis showed a median OS of 4 mo for the HPD

208 Survival analysis showed a median OS of 4 months for HPD

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

210 Kaplan-Meier survival analysis showed an 89% cumulative graft success

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

212 Kaplan Meier survival analysis showed significantly shorter relapse-f

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

214 Survival analysis showed that the probability of surgica

215 Survival analysis showed that the signature was associat

216 Survival analysis showed, after adjustment for age and s

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

218 In multivariable survival analysis, SII remained an independent prognosti

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

220 A survival analysis stratified nominal paclitaxel dose by

221 Risk and survival analysis studies controlling for several potent

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

223 ry and multiclass prediction problems and 26 survival analysis tasks.

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

225 We used survival analysis techniques to estimate absolute and re

226 Commonly used survival analysis techniques, such as the Kaplan-Meier m

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

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

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

230 At the multivariate survival analysis, the model showed independent high ris

231 On univariate survival analysis, TNM stage (p < 0.01), mGPS (p < 0.05)

232 mixed effects models, pairwise analyses, and survival analysis to address sampling-related bias that

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

234 We used survival analysis to analyze variables associated with t

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

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

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

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

239 population, we performed a multivariable Cox survival analysis to determine the effect of the burden

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

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

242 rn in 1905 and 1915 in Denmark, we performed survival analysis to estimate risk of mortality for majo

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

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

245 We used mixed-effects Cox regression survival analysis to estimate the effects of ethnicity a

246 We used survival analysis to estimate the incidence and cumulati

247 We used survival analysis to estimate the relationship between h

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

249 We used a survival analysis to examine the risk of first suicide a

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

251 ge and selection operator (LASSO) method for survival analysis to select the best predictors of incid

252 Survival analysis uncovered the worse disease-free survi

253 uration was calculated, and a competing risk survival analysis undertaken to assess multiple factors.

254 d against disease severity markers including survival analysis using all-cause mortality from diagnos

255 Survival analysis using Cox regression was used to estim

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

257 A Kaplan-Meier survival analysis was also conducted.

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

259 A survival analysis was conducted comparing attrition from

260 A Kaplan-Meier survival analysis was conducted to compare the survival

261 Survival analysis was conducted using Kaplan-Meier curve

262 Survival analysis was performed by Cox models and compon

263 Survival analysis was performed for patients in the post

264 Survival analysis was performed to compare patients brid

265 Survival analysis was performed using adjusted Cox regre

266 Survival analysis was performed with Cox regression with

267 Survival analysis was performed with Kaplan-Meier analys

268 Using Cox proportional hazards models, survival analysis was performed, and demographic and cli

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

270 Additionally, survival analysis was performed.

271 Survival analysis was used to analyze the risk of cornea

272 Survival analysis was used to assess associations betwee

273 Survival analysis was used to compare risk of disease in

274 ated by proteome microarray and Kaplan-Meier survival analysis was used to determine survival differe

275 A survival analysis was used to determine the effectivenes

276 Survival analysis was used to estimate adjusted hazard r

277 Multivariable survival analysis was used to estimate influences of age

278 The Cox proportional-hazard model for survival analysis was used to identify genetic variants

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

280 For the survival analysis we used Cox proportional hazards model

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

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

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

284 variable logistic and linear regressions and survival analysis were performed depending on outcomes:

285 Cox proportional hazard and survival analysis were performed to compare the risks an

286 Multivariate logistic regression and survival analysis were performed.

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

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

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

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

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

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

293 y of myocardial injury in ARDS and performed survival analysis with primary outcome of in-hospital de

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

295 Survival analysis with recursive partitioning in node-ne

296 Survival analysis with stabilized MSM-derived weights si

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

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

299 Data were analysed using survival analysis, with self-reported falls (total sampl

300 In survival analysis, younger patients presented the best p