ライフサイエンスコーパス: survival curve

1 hose without LGE (p < 0.001 for Kaplan-Meier survival curves).

2 dications, time to failure, and Kaplan-Meier survival curve.

3 p biomicroscopy and analyzed by Kaplan-Meier survival curve.

4 hematical model and to fit it to the overall survival curve.

5 ough no plateau has been demonstrated in the survival curve.

6 than FVB mice evaluated by the Kaplan-Meier survival curve.

7 by obliterating the "shoulder" of radiation survival curve.

8 alysed on an intention-to-treat basis with a survival curve.

9 as calculated from standardized Kaplan-Meier survival curves.

10 etermined by comparing adjusted Kaplan-Meier survival curves.

11 Univariate analysis included Kaplan-Meier survival curves.

12 proportional hazards models and Kaplan-Meier survival curves.

13 ft survival was evaluated using Kaplan-Meier survival curves.

14 mained strong on time-dependent Kaplan-Meier survival curves.

15 ver, different SSPs produced broadly similar survival curves.

16 k procedure was used to compare Kaplan-Meier survival curves.

17 tested by log-rank tests using Kaplan-Meier survival curves.

18 high risk) was used to stratify Kaplan-Meier survival curves.

19 val were analyzed with chi2 and Kaplan-Meier survival curves.

20 the lesion were analyzed using Kaplan-Meier survival curves.

21 econciled through the estimation of expected survival curves.

22 ith the alpha parameter obtained from fitted survival curves.

23 nd cancer-free survival was determined using survival curves.

24 ther aging trajectories of transcription and survival curves.

25 ed using proportional hazards regression and survival curves.

26 ibited enhanced and statistically equivalent survival curves.

27 vivors was modelled using Cox regression and survival curves.

28 ristic (ROC) curve analysis and Kaplan-Meier survival curves.

29 ival rates were estimated using Kaplan-Meier survival curves.

30 est) and directly compare covariate-adjusted survival curves.

31 stent with results from the manual method of survival curve acquisition for several mutants in both s

32 Survival curves adjusted for preoperative differences we

33 te a distinct alteration in the slope of the survival curve after 6 months of lamivudine treatment fo

34 rd to incidence and time to suicide attempt, survival curve analyses were conducted.

35 to be a promising refinement of traditional survival curve analysis and dose response models.

36 By survival curve analysis median healing time for cure was

37 By survival curve analysis of DAISY children, the risk of p

38 Kaplan-Meier survival curve analysis revealed that wild-type C57BL/6

39 m operation (controls) by using Kaplan-Meier survival curve analysis.

40 n status on survival was evaluated utilizing survival curve analysis.

41 A Kaplan-Meier survival curve and log-rank test were used for survival

42 was estimated using unadjusted Kaplan-Meier survival curves and a Cox proportional hazards model to

43 Kaplan-Meier survival curves and area under the receiver-operating ch

44 aplan-Meier estimators were used to generate survival curves and compared by using the log-rank test.

45 Survival curves and covariate adjusted hazard ratio (HR)

46 Survival curves and covariate adjusted hazard ratios (HR

47 Descriptive statistics, Kaplan-Meier survival curves and Cox proportional hazard regression m

48 between these two groups using Kaplan-Meier survival curves and Cox proportional hazards models.

49 aft survival was analyzed using Kaplan-Meier survival curves and Cox regression analysis.

50 Kaplan-Meier survival curves and Cox regression models were used to c

51 Kaplan-Meier survival curves and Cox regression revealed that patient

52 Stratified survival curves and Cox regression were used to evaluate

53 Statistical analysis included Kaplan-Meier survival curves and Cox regression.

54 CHOpcDNA3 cells treated with PM had similar survival curves and exhibited no difference in mutation

55 CAS compared with CEA, we used Kaplan-Meier survival curves and fitted mixed-effects logistic regres

56 ession models were used to estimate adjusted survival curves and hazard ratios (HR) with 95% confiden

57 The mice survival curves and histological lesions revealed A/D di

58 erall survival was assessed with Kaplan-Meir survival curves and log-rank testing.

59 Kaplan-Meier survival curves and log-rank tests revealed that high le

60 mes was evaluated by unadjusted Kaplan-Meier survival curves and logistic regression models.

61 We applied Kaplan-Meier analysis for survival curves and mortality rate estimation and Cox re

62 Survival curves and multivariable adjusted hazard ratios

63 ed by these measurements, using Kaplan-Meier survival curves and multivariate Cox proportional hazard

64 First, we report updated survival curves and organ pathology in Ndufs4 KO mice ex

65 We generated Kaplan-Meier survival curves and performed a multivariable analysis u

66 Survival curves and proportional hazards were computed.

67 The model produces personalized survival curves and quantifies the relationship between

68 dard survival methods including Kaplan-Meier survival curves and sex-by-treatment interaction term to

69 Cellular recovery, as determined by survival curves and the ability to return to growth afte

70 Kaplan-Meier survival curves and the Wilcoxon test were used for stat

71 istically significant differences in overall survival curves and time to relapse for the groups.

72 age covariate-adjusted SPK- and KTA-specific survival curves (and 10-year area under the curve; ie, r

73 atency was described by using a Kaplan-Meier survival curve, and number of catheter days were compare

74 Kaplan-Meier estimation was used to generate survival curves, and a multivariate Cox proportional haz

75 jection within 90 days by chi2, Kaplan Meier survival curves, and by multivariable logistic regressio

76 ysis included chi-square tests, Kaplan-Meier survival curves, and Cox proportional-hazards models.

77 he Kaplan-Meier method was used to construct survival curves, and the log-rank statistic was used to

78 ox proportional hazards models, Kaplan-Meier survival curves, and the log-rank test.

79 ve statistics, incidence rates, Kaplan-Meier survival curves, and the RR of NLP outcomes among eyes w

80 The survival curves appeared to cross over at approximately

81 ulation using an area under the Kaplan-Meier survival curve approach that combined trial-specific haz

82 isual record of individual deaths from which survival curves are constructed and validated, producing

83 5A mice display a similar tumor spectrum and survival curve as p53+/- mice, tumors from p53+/515A mic

84 urvival hazards and plotted the Kaplan-Meier survival curves as well as the net chance of a longer su

85 Kaplan-Meier survival curves assessed the timing of initial diagnosis

86 The difference between the survival curves associated with large (>3 cm) and small

87 ition to providing accurate and reproducible survival curves at a considerably reduced labor, this ap

88 To enable the rapid acquisition of survival curves at an arbitrary statistical resolution,

89 pneumococcal meningitis, using Kaplan-Meier survival curves, bacteriological and histological studie

90 m IRD kidneys, and illustrates how estimated survival curves based on a clinical decision can be pres

91 Survival curves based on Kaplan-Meier estimates are pres

92 A plateau in the survival curve began at approximately 3 years.

93 The survival curve began to plateau around year 3, with foll

94 o 10.0 months), with a plateau at 21% in the survival curve beginning around year 3.

95 We observed a plateau in the survival curve, beginning at approximately 3 years, whic

96 Survival curves between 25 and 35 days were consistent f

97 There was also no significant difference in survival curves between groups; intentionally injured pa

98 as no significant difference in relapse-free survival curves between the treatment and control groups

99 Survival curves between the two groups of animals began

100 n the first and second breakpoints in the CR survival curve (between 21 and 31 months of age), tumors

101 Several clinical (i.e., survival curves, blood and tissue bacterial burdens, and

102 in one patient in the Epi-group (event-free survival curves by Grey-test, P=0.03).

103 ectomy before or after 1 year (comparison of survival curves by log-rank test: p=0.2; hazard ratio 0.

104 s of remaining life expectancy and long-term survival curves can also be produced.

105 The observed placebo survival curve closely approximated the predicted surviv

106 Kaplan-Meier survival curves compared time to death for the groups wi

107 Kaplan-Meier method, with the differences in survival curves compared using a log-rank test.

108 he Kaplan-Meier method was used to construct survival curves, compared using the log-rank test.

109 survival is represented using a Kaplan-Meier survival curve comparing (1) locally procured and import

110 Survival curves constructed using the Kaplan-Meier metho

111 Other analyses, such as generation of survival curves, construction of Cox regression models,

112 However, a survival curve corrected for age of the patients at the

113 Data were analyzed by Kaplan-Meier survival curves, Cox regression, and binary logistic reg

114 lan-Meier analysis was performed to plot the survival curve; cox regression models were employed to d

115 With more than 10 years of follow-up, the survival curves demonstrate a plateau indicating a poten

116 aplan-Meier major adverse cardiac event-free survival curves demonstrated a significant benefit for a

117 Furthermore, survival curves demonstrated that the probability of dyi

118 Survival curves demonstrated that youths with T1DM devel

119 was similar in the two cohorts, although the survival curves did not converge until after 3 years.

120 Medullary thyroid carcinoma-specific survival curves did not show any significant difference

121 In clinical follow-up, the AIDS-free survival curves differed by HIV-1 subtype.

122 Survival curves differed significantly among the four gr

123 The Kaplan-Meier survival curves differed significantly for patients with

124 nction of mean activity per cell showed that survival curves differed substantially when the activity

125 Disease-free patient survival curves displayed a moderate decline with BRAF V

126 ilation was not reached and the Kaplan-Meier survival curve diverged from a published natural history

127 was calculated by integrating the predicted survival curve estimated in the Cox model.

128 Kaplan-Meier survival curves estimated the time from initial diabetes

129 dherence-adjusted hazard ratios and CHD-free survival curves estimated through inverse probability we

130 l hazards model, log-rank test, Kaplan-Meier survival curve, Fisher exact test, and t test.

131 In this case, a Kaplan-Meier survival curve for a specific cause that treats deaths d

132 Mean RBC age was calculated from the RBC survival curve for all circulating RBCs and for labeled

133 In contrast, the observed survival curve for carboplatin was far superior to the e

134 carboplatin was far superior to the expected survival curve for CP (P <.01).

135 The model was used to predict a survival curve for each of the 742 SERAPHIN patients via

136 sis data, was used to construct the expected survival curve for each treatment arm of the ICON2 trial

137 val curve closely approximated the predicted survival curve for the first 15 months.

138 The survival curve for the placebo patients can be divided i

139 Survival curves for 360-degree catheter, 270-degree to 3

140 Survival curves for 90% to 99% TR and 100% TR were simil

141 17%, 30%, and 49% (P < 0.001), respectively; survival curves for admission to a skilled-nursing facil

142 Survival curves for BMT show that at least half of the p

143 a mathematical model to predict Kaplan-Meier survival curves for chemotherapy combined with radiation

144 .99) for the first 8 years, and the CHD-free survival curves for continuous use and no use of estroge

145 Kaplan-Meier survival curves for each cohort were not significantly d

146 Lifetime survival curves for each group in the decision-analytic

147 Post hoc analysis showed that survival curves for GA-treated male patients diverged ea

148 HT mortality rates were based on survival curves for HT 1982 to 2001.

149 The survival curves for many pediatric sarcomas have remaine

150 is provided for estimating Kaplan-Meier-type survival curves for marginal structural models.

151 Kaplan-Meier survival curves for overall survival showed a statistica

152 Survival curves for patients initially treated with pent

153 om TCGA (global log-rank P = .02 comparing 3 survival curves for patients with 0-2, 3-4, and 5-7 dosa

154 ll within the 95% confidence bands of actual survival curves for patients.When the predictor variable

155 Kaplan-Meier survival curves for the 2 procedures were compared using

156 However, the survival curves for the beta-pol(+/+) and beta-pol(+/-)

157 the difference between the areas under the 2 survival curves for the intervention and control groups.

158 Survival curves for the mutation-positive and -negative

159 ll within the 95% confidence bands of actual survival curves for the patients.

160 A log-rank test comparing survival curves for these two populations yields a two-s

161 ood separation of tertile-based Kaplan-Meier survival curves for these variables.

162 tients were stratified by TNM stage, overall survival curves for those with TNP breast cancer matched

163 re entered into the algorithm, the predicted survival curves for time to death fell within the 95% co

164 ely to reach the end point is then consulted.Survival curves for time to need for care equivalent to

165 Survival curves for underweight and normal weight patien

166 erforms COX regression analyses and produces survival curves for variation-ceRNA events.

167 d/lost were calculated using direct adjusted survival curves (for participants 40+ years of age), wit

168 Survival curves found that patients with increased PDW h

169 The 9 lowest risk deciles had linear survival curves from 0 to 365 postoperative days, with t

170 ed to calculate the parameters of the growth/survival curves from the distributions of the respective

171 CA was associated with consistently inferior survival curves from year 3 onward.

172 In the longer term, the survival curve gradient among ischemic stroke and intrac

173 h modern therapy, the long-term disease-free survival curves have not reached a plateau.

174 rification group also showed a better 90-day survival curve (Hazard ratio=0.260) compared to the cont

175 id not result in a significant separation of survival curves (HR, 1.4; 95% confidence interval [CI]:

176 From the augmented model, the survival curves illustrated that recipients with KPS 40

177 survival curves were compared with observed survival curves in the ICON2 trial at all time points us

178 Analysis of survival curves indicated that ir-HGF levels higher than

179 s ratio 7.17 [95% CI 1.5-34.5]; Kaplan-Meier survival curve, log-rank statistic 9.11 [p=003]).

180 Analyses included: Kaplan-Meier survival curves, Log-Rank tests, and Cox proportional ha

181 rvival data were analyzed using Kaplan-Meier survival curves, log-rank tests, and proportional hazard

182 Kaplan-Meier survival curves, log-rank tests, and Weibull survival mo

183 pses) conditions had significantly different survival curves (Mantel-Cox statistic chi(2)1 = 10.47, P

184 st 3 years; however, a potentially diverging survival curve may portend higher mortality at 5 years.

185 Separation of the survival curves occurred at 3 years after initial testin

186 ficantly different from the linear quadratic survival curve of MCF7 /: HER2-18 cells exposed to gamma

187 Kaplan-Meier survival curves of MDI-based risk classes showed signifi

188 uct clonal composition and Kaplan-Meier-like survival curves of multiple evolutionary stories.

189 Kaplan-Meier survival curves of OSSN recurrence were similar between

190 chnique was used to compare the Kaplan-Meier survival curves of patients with local recurrences, sate

191 Survival curves of study subgroups (GDD and no GDD) were

192 Within each case-mix stratum, survival curves of the patients admitted to hospitals in

193 find additional support through Kaplan-Meier survival curves of thousands of patients.

194 entified variables, we compared Kaplan-Meier survival curves of transplanted and control patients str

195 der adoption of inverse probability-weighted survival curves or alternative techniques that address t

196 studies contrasted (unadjusted) Kaplan-Meier survival curves or, if covariate-adjusted, reported haza

197 kelihood identified the point at which the 2 survival curves overlapped; the 95% confidence interval

198 dose of B. dermatitidis yeasts (Kaplan-Meier survival curve P values of 0.027 to 0.0002) and also pro

199 ive PC3 tumor xenografts in cytotoxicity and survival curves (P > 0.05).

200 isk groups yielded distinct progression-free survival curves (P < 0.0001).

201 , 95% CI: 3.3, 17.4) resulted in similar ICH survival curves (P = 0.979).

202 Observed median survival times, Kaplan-Meier survival curves, proportional death hazard ratios, and r

203 urine models of zygomycosis by assessment of survival curves, pulmonary fungal burdens, and expressio

204 but that in order to match the experimental survival curves quantitatively, it is necessary that the

205 Kaplan-Meier survival curves rapidly declined with increasing age in

206 tended follow-up as evidenced by the overall survival curves remaining separated.

207 The shape of the survival curve remains; the highest hazard remains the f

208 Kaplan-Meier survival curves, results of log rank tests, and cumulati

209 Adjusted Kaplan-Meier survival curves revealed that at any point in time the p

210 Further dissection of the sgs1 and srs2 survival curves reveals two distinct phenomena.

211 noncodeleted tumors also benefited from CRT; survival curves separated after the median had been reac

212 A single-hit, multi-target crypt survival curve showed a significant increase in crypt pr

213 The survival curves showed a significant age effect with car

214 Kaplan-Meier survival curves showed an increased number of deaths for

215 Kaplan-Meier survival curves showed better survival in PLG-SAS than i

216 Survival curves showed improved survival for patients in

217 Survival curves showed no clear inflection point or peri

218 A comparison of the Kaplan-Meier 180-day survival curves showed no difference between treatment g

219 m(2) for LAVI and -15% for GLS, Kaplan-Meier survival curves showed significant better survival for p

220 Kaplan-Meier survival curves showed that the uninsured group had bett

221 Kaplan-Meier survival curves showed that these events occurred more f

222 Kaplan-Meier survival curves showed the 20-30-year-old age group and

223 nction of RLS, and it displays features of a survival curve such as changes in hazard rate with age.

224 The survival curves suggest a more aggressive cancer than pa

225 Survival curves suggest that increased mortality risk wi

226 In that study, Kaplan-Meier survival curves suggested worse cardiovascular disease s

227 rences in gene expression profiles and Abeta survival curves, that deeper layer neurons are significa

228 onstruct inverse probability weight-adjusted survival curves; the findings did not change.

229 The 8-year survival curve to first appropriate shocks was 94%, 57%,

230 macological screens, using a 7-d post-injury survival curve to identify modifiers of injury.

231 dology based on inverse probability-weighted survival curves to address this limitation.

232 We used Kaplan-Meier survival curves to display the time to joint surface col

233 In addition, a 10-day survival curve was conducted following CLP and cecal exc

234 The average of these predicted survival curves was compared with observed survival of t

235 ssessed by log rank analyses of Kaplan-Meier survival curves was significantly lower for NVE isolates

236 een active (n = 2365) and placebo (n = 2371) survival curves, was 105 days (95% CI, -39 to 242; P = .

237 To handle long plateaus in the tails of survival curves, we also exploited "cure models" to esti

238 Survival curves were almost identical for the 3 observer

239 Kaplan-Meier survival curves were also compared after stratifying pat

240 Kaplan-Meier survival curves were also generated.

241 Complex survival curves were analyzed using radiation target the

242 Kaplan Meier survival curves were analyzed with the log rank test.

243 e rate and the Mantel-Haenszel statistic for survival curves were calculated for each group.

244 Survival and morbidity-free survival curves were calculated, and risk factors were d

245 Survival and morbidity-free survival curves were calculated.

246 time to required next treatment and overall survival curves were compared by using a log-rank (Mante

247 Survival curves were compared by using the log-rank test

248 Survival curves were compared using the log-rank test an

249 Expected survival curves were compared with observed survival cur

250 Kaplan-Meier survival curves were computed for risk score quartiles.

251 Kaplan-Meier survival curves were constructed and multivariate Cox re

252 Kaplan-Meier survival curves were constructed to assess retention pro

253 Kaplan-Meier survival curves were constructed to depict cumulative su

254 Overall survival curves were constructed using Kaplan-Meier meth

255 Kaplan-Meier survival curves were constructed using mean miRNA expres

256 Kaplan-Meier survival curves were constructed, and Cox proportional h

257 Age-adjusted survival curves were constructed, and Cox proportional-h

258 Survival curves were derived by the Kaplan-Meier method,

259 Exponential survival curves were derived from trial data and adjuste

260 Survival curves were derived using the Kaplan-Meier meth

261 Survival curves were derived with Kaplan-Meier methods;

262 Survival curves were developed using the Kaplan-Meier me

263 Kaplan-Meier survival curves were drawn for midterm outcomes.

264 os (HRs) were estimated from Cox models, and survival curves were estimated by the Kaplan-Meier metho

265 ank and Cox proportional hazards models, and survival curves were estimated using the Kaplan-Meier me

266 Survival curves were estimated with Kaplan-Meier product

267 Survival curves were estimated with the Kaplan-Meier met

268 Kaplan-Meier survival curves were examined for differences in surviva

269 Survival curves were found not to differ significantly (

270 confidence intervals (CIs), and Kaplan-Meier survival curves were generated by gender and etiology.

271 Kaplan-Meier survival curves were generated by treatment group for al

272 Kaplan-Meier survival curves were generated for each gender and compa

273 Cell survival curves were generated from the fraction of cell

274 Survival curves were generated using Kaplan-Meier method

275 Survival curves were generated using the Kaplan-Meier me

276 oduced by lung leukocytes were measured, and survival curves were generated.

277 Cumulative rate of endophthalmitis and survival curves were measured using Cox-proportional haz

278 Kaplan-Meier survival curves were obtained by the log-rank test.

279 this approach to numerous experiments where survival curves were obtained for different cell lines a

280 Actuarial survival curves were plotted according to the Kaplan-Mei

281 Kaplan-Meier survival curves were plotted for renal allograft and pat

282 Kaplan-Meier survival curves were plotted to determine continuation r

283 aluated by using the Kaplan-Meier method and survival curves were plotted.

284 In panel b, the survival curves were shifted relative to the y axis.

285 The log-rank test showed that the survival curves were significantly different (P<0.001).

286 Survival curves were significantly different using this

287 If reinterventions were not required, survival curves were similar.

288 ced cell death (i.e., reproductive failure), survival curves were simulated with different electron e

289 The differences between survival curves were tested for significance by log-rank

290 Kaplan-Meier survival curves were used to analyze the data.

291 Kaplan-Meier survival curves were used to compare graft and patient s

292 Kaplan-Meier survival curves were used to estimate age-specific PD ri

293 al hazard regression models and Kaplan-Meier survival curves were used to identify predictors for alc

294 tastasis models, and the indirect nature of "survival" curves when studying brain metastases.

295 h versus the absorbed dose followed a linear survival curve with alpha = 0.51 +/- 0.05 Gy(-1) and R(2

296 nalyses revealed a flat PTC-specific patient survival curve with neither mutation, a modest decline i

297 rophy (FED) were analyzed using Kaplan-Meier survival curves with log-rank test and Cox regression.

298 Using Kaplan-Meier survival curves with log-rank tests, health outcomes wer

299 Survival curves with the ROC optimal cutoff for each met

300 ox regression reflected what was seen in the survival curves, with all models being highly significan