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A survival Classification and Regression Analysis (CART) algorithm was applied to build the prognostic model for 90-day mortality.
In order to build the prognostic model, patients recruited up to January 2011 (included in the GenOSept cohort) were divided into two subsets of patients: one for derivation and the other for external geographic validation.
In this study, we focused on such a non-operated, poor-grade SAH population, treated in a neurological intensive care unit (NICU), so as to identify predictive factors and build the prognostic model for such a population.
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The purpose of this study was to identify prognostic factors and build the predictive model based on poor-grade subarachnoid haemorrhage (SAH) population received only supportive symptomatic treatment.
Covariates that were identified as significant factors throughout the univariate analysis were selected for multivariate analysis, which was performed employing Weibull and Cox's proportional hazard model to build the prognostic indicators of survival in patients with gastric cancer.
Improvements in the prognostic index was evaluated by adding clinical variables (other than those used to build the prognostic index) and QoL variables (with p<0.1 in univariate analysis) to a model with backward selection (Köhne or GERCOR index being forced in the model).
The key stages of the prognostic model development process are shown in Fig. 2. The general description of each stage is as follows: Fig. 2 Schematic view of the prognostic model development process. 1 data acquisition, 2 data pre-processing, 3 feature selection, 4 prognostic model development, 5 prognostic model validation and evaluation, 6 online clinical prognostic model.
In the most recent study, the prognostic impact of 30 genes chosen on the basis of the previous two studies was evaluated in 282 paraffin-embedded cHL samples in order to build a prognostic model [ 77].
We calculated the prognostic models, international prognostic index (IPI) and prognostic index for PTCL-NOS (PIT).
We used AIC in a hybrid stepwise strategy to build a final prognostic model where the combined-PI and tumor size were left as covariates and ER status as stratification variable in a Cox regression model (AIC: 191.46; Table S2. Likelihood ratio test p = 0.002; Table 6).
This justifies the efforts to build multivariate prognostic models such as AdjuvantOnline (Adjuvant! Inc., San Antonio, TX, USA) and multigene predictors.
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