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The signature for predicting survival in AML was developed using the Supervised Principal Components approach which selects genes whose expression patterns are correlated with survival and, based on those, generates a prognostic score (Bair and Tibshirani, 2004).
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We analyzed prognostic factors, described survival and generated a prognostic model in patients with metastatic renal cell carcinoma in whom immunotherapy failed and who were potentially eligible for novel agents.
To generate a prognostic index model, 15 clinical variables were examined for their impact on LRR.
It is possible to generate a prognostic model for advanced ovarian carcinoma based on angiogenesis-related genes using formalin-fixed paraffin-embedded samples.
This proof-of-concept strategy generated a prognostic panel using high-throughput biomarker screening in combination with a devised panel scoring technique.
Finally, we generated a prognostic score based on the combination of CD57+ (+, > vs −, ⩽2 cells per spot) and CD68+ (+, >0 vs −, =0 cells per spot) TIC densities.
These parameters were used to generate a prognostic score that was analysed retrospectively in 467 N0-N1a patients to determine its predictive value for survival.
Multiple logistic regression (backward: likelihood ratio (LR) method) was used for multivariate analysis, and the coefficients derived were used to generate a prognostic model (RFH score).
And recently, this score system was revised and multiple statistically weighted clinical features were used to generate a prognostic categorization model.
Because our aim was to generate a prognostic classification scheme for diffuse gliomas based on molecular aberrations, the gene with lowest HR (i.e. IDH-mutations) provided our first molecular prognostic separator for diffuse gliomas.
In an attempt to generate a prognostic tool useful for surgical decision-making, Tokuhashi et al. [ 12] developed a scoring system by which survival could be categorized into one of three groups: <6 months, >6 months, or >1 year.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com