Search results for "relative risk regression model"

showing 3 items of 3 documents

An Extension of the DgLARS Method to High-Dimensional Relative Risk Regression Models

2020

In recent years, clinical studies, where patients are routinely screened for many genomic features, are becoming more common. The general aim of such studies is to find genomic signatures useful for treatment decisions and the development of new treatments. However, genomic data are typically noisy and high dimensional, not rarely outstripping the number of patients included in the study. For this reason, sparse estimators are usually used in the study of high-dimensional survival data. In this paper, we propose an extension of the differential geometric least angle regression method to high-dimensional relative risk regression models.

Clustering high-dimensional dataComputer sciencedgLARS Gene expression data High-dimensional data Relative risk regression models Sparsity · Survival analysisLeast-angle regressionRelative riskStatisticsEstimatorRegression analysisExtension (predicate logic)High dimensionalSettore SECS-S/01 - StatisticaSurvival analysis
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Sparse relative risk regression models

2020

Summary Clinical studies where patients are routinely screened for many genomic features are becoming more routine. In principle, this holds the promise of being able to find genomic signatures for a particular disease. In particular, cancer survival is thought to be closely linked to the genomic constitution of the tumor. Discovering such signatures will be useful in the diagnosis of the patient, may be used for treatment decisions and, perhaps, even the development of new treatments. However, genomic data are typically noisy and high-dimensional, not rarely outstripping the number of patients included in the study. Regularized survival models have been proposed to deal with such scenarios…

Statistics and ProbabilityClustering high-dimensional dataComputer sciencedgLARSInferenceScale (descriptive set theory)BiostatisticsMachine learningcomputer.software_genreRisk Assessment01 natural sciencesRegularization (mathematics)Relative risk regression model010104 statistics & probability03 medical and health sciencesNeoplasmsCovariateHumansComputer Simulation0101 mathematicsOnline Only ArticlesSurvival analysis030304 developmental biology0303 health sciencesModels Statisticalbusiness.industryLeast-angle regressionRegression analysisGeneral MedicineSurvival AnalysisHigh-dimensional dataGene expression dataRegression AnalysisArtificial intelligenceStatistics Probability and UncertaintySettore SECS-S/01 - StatisticabusinessSparsitycomputerBiostatistics
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Sparse relative risk survival modelling

2016

Cancer survival is thought to closed linked to the genimic constitution of the tumour. Discovering such signatures will be useful in the diagnosis of the patient and may be used for treatment decisions and perhaps even the development of new treatments. However, genomic data are typically noisy and high-dimensional, often outstripping the number included in the study. Regularized survival models have been proposed to deal with such scenary. These methods typically induce sparsity by means of a coincidental match of the geometry of the convex likelihood and (near) non-convex regularizer.

gene expression datarelative risk regression modelsurvival analysisparsityhigh dimensional datadifferential geometrydglarsSettore SECS-S/01 - Statistica
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