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If π = 0, then we obtain the survival model with survival function S0 t).
Analyses of survival data were performed using the log-rank test and the Cox regression model, with survival curves computed using the Kaplain Meier method.
Analyses of survival data were performed with the log-rank test and the Cox regression model, with survival curves computed with the Kaplan Meier method.
A multiple logistic regression model with survival as the binary outcome variable, using a forward entry conditional model, was then performed.
In the multivariate Cox regression model with survival as dependent variable and median delay, age, open access endoscopy, number and type of symptoms as independent variables, the odd's ratio for survival in patients with long delay (>median) versus short delay (≤median) was 1.8 (95% confidence interval (CI) 1.1 to 3.0; p = 0.01).
In the multivariate Cox regression model with survival as dependent variable and median delay, age, open access endoscopy, number and type of symptoms as independent variables, the odd's ratio (OR) for survival in patients with long delay (>median) versus short delay (≤median) was 1.8 (95% confidence interval (CI) 1.1 to 3.0; p = 0.01).
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Time since surgery to the first disease recurrence has been modelled with survival analysis.
To check if the two treatment groups could be combined in the statistical analysis, treatment was tested as a 2-level factor, as well as an interaction with protein expression in logistic regression models with survival as response.
For first-year survival a model with constant survival probability φp received similar support as the φ [hab]p model (difference between AICc values Δ AICc = 1.9).
Finally, we integrated the best model from different levels of genomic data to determine the meta-dimensional model associated with survival.
We analysed gene expression profiles of ovarian carcinomas to identify a multigene expression model associated with survival after platinum-based therapy.
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