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1 (n = 3) of those without LGE (p < 0.001 for Kaplan-Meier survival curves).
2 tiglaucoma medications, time to failure, and Kaplan-Meier survival curve.
3 lmic slit-lamp biomicroscopy and analyzed by Kaplan-Meier survival curve.
4 imately 4 mo) than FVB mice evaluated by the Kaplan-Meier survival curve.
5 ion-free survival rates were estimated using Kaplan-Meier survival curves.
6 ting-characteristic (ROC) curve analysis and Kaplan-Meier survival curves.
7 e mortality was calculated from standardized Kaplan-Meier survival curves.
8 y were also determined by comparing adjusted Kaplan-Meier survival curves.
9 Univariate analysis included Kaplan-Meier survival curves.
10 by using Cox proportional hazards models and Kaplan-Meier survival curves.
11 eriod and graft survival was evaluated using Kaplan-Meier survival curves.
12 is ability remained strong on time-dependent Kaplan-Meier survival curves.
13 The log-rank procedure was used to compare Kaplan-Meier survival curves.
14 ferences were tested by log-rank tests using Kaplan-Meier survival curves.
15 e than 1.27 (high risk) was used to stratify Kaplan-Meier survival curves.
16 1-hour survival were analyzed with chi2 and Kaplan-Meier survival curves.
20 analysis, investigators often present crude Kaplan-Meier survival curves and adjusted relative hazar
22 were compared between these two groups using Kaplan-Meier survival curves and Cox proportional hazard
25 mission after CAS compared with CEA, we used Kaplan-Meier survival curves and fitted mixed-effects lo
27 f AF on outcomes was evaluated by unadjusted Kaplan-Meier survival curves and logistic regression mod
28 bgroups defined by these measurements, using Kaplan-Meier survival curves and multivariate Cox propor
30 We used standard survival methods including Kaplan-Meier survival curves and sex-by-treatment intera
31 high-grade rejection within 90 days by chi2, Kaplan Meier survival curves, and by multivariable logis
32 Catheter patency was described by using a Kaplan-Meier survival curve, and number of catheter days
33 tistical analysis included chi-square tests, Kaplan-Meier survival curves, and Cox proportional-hazar
34 vival using Cox proportional hazards models, Kaplan-Meier survival curves, and the log-rank test.
35 Descriptive statistics, incidence rates, Kaplan-Meier survival curves, and the RR of NLP outcomes
36 US cancer population using an area under the Kaplan-Meier survival curve approach that combined trial
37 roportional survival hazards and plotted the Kaplan-Meier survival curves as well as the net chance o
39 nd outcome of pneumococcal meningitis, using Kaplan-Meier survival curves, bacteriological and histol
41 as allograft survival is represented using a Kaplan-Meier survival curve comparing (1) locally procur
44 ermanent ventilation was not reached and the Kaplan-Meier survival curve diverged from a published na
46 x proportional hazards model, log-rank test, Kaplan-Meier survival curve, Fisher exact test, and t te
47 s to develop a mathematical model to predict Kaplan-Meier survival curves for chemotherapy combined w
53 pression (odds ratio 7.17 [95% CI 1.5-34.5]; Kaplan-Meier survival curve, log-rank statistic 9.11 [p=
58 ay graphic technique was used to compare the Kaplan-Meier survival curves of patients with local recu
59 ffected by identified variables, we compared Kaplan-Meier survival curves of transplanted and control
60 ost previous studies contrasted (unadjusted) Kaplan-Meier survival curves or, if covariate-adjusted,
61 of a lethal dose of B. dermatitidis yeasts (Kaplan-Meier survival curve P values of 0.027 to 0.0002)
67 ues of 34 mL/m(2) for LAVI and -15% for GLS, Kaplan-Meier survival curves showed significant better s
73 val rate as assessed by log rank analyses of Kaplan-Meier survival curves was significantly lower for
87 os (HR), 95% confidence intervals (CIs), and Kaplan-Meier survival curves were generated by gender an
95 ox proportional hazard regression models and Kaplan-Meier survival curves were used to identify predi
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