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

ABSTRACT:

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 typically small and can be heterogeneous. In classical subgroup analysis, a separate prediction model is fitted using only the data of one specific cohort. However, this can lead to a loss of power when the sample size is small. Simple pooling of all cohorts, on the other hand, can lead to biased results, especially when the cohorts are heterogeneous.

Results

We propose a new Bayesian approach suitable for continuous molecular measurements and survival outcome that identifies the importa

SUBMITTER: Madjar K 

PROVIDER: S-EPMC8665528 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature