Combining heterogeneous subgroups with graph-structured variable selection priors for Cox regression

Combining heterogeneous subgroups with graph-structured variable selection priors for Cox regression

Background: Important objectives in cancer research are the prediction of a patient’s risk based on molecular measurements such as gene expression data and the identification of new prognostic biomarkers (e.g. genes). In clinical practice, this is often challenging because patient cohorts are typica...

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Bibliographic Details
Main Authors: Ickstadt, K. (Author), Madjar, K. (Author), Rahnenführer, J. (Author), Zucknick, M. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2021
Subjects:
Bayes theorem
Bayes Theorem
Bayesian networks
Bayesian variable selection
cohort analysis
Cohort Studies
Cox proportional hazard model
Cox proportional hazards model
Forecasting
Gaussian graphical model
Gaussian graphical models
gene expression
Gene expression
Gene Expression
Graphic methods
Heterogeneous cohort
Heterogeneous cohorts
human
Humans
Markov processes
Markov random field prior
Markov random field priors
Molecular measurements
Power
sample size
Sample Size
Sample sizes
Subgroup analyse
Subgroup analysis
Variables selections
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