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

BMC Bioinformatics

Manuela Zucknick¹  Katrin Madjar²  Katja Ickstadt²  Jörg Rahnenführer² 

[1] Department of Biostatistics, Oslo Centre for Biostatistics and Epidemiology, University of Oslo, 0317, Oslo, Norway;Department of Statistics, TU Dortmund University, 44221, Dortmund, Germany;

关键词: Bayesian variable selection;    Cox proportional hazards model;    Gaussian graphical model;    Markov random field prior;    Heterogeneous cohorts;    Subgroup analysis;   

DOI  :  10.1186/s12859-021-04483-z

来源: Springer

【 摘 要 】

BackgroundImportant 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.ResultsWe propose a new Bayesian approach suitable for continuous molecular measurements and survival outcome that identifies the important predictors and provides a separate risk prediction model for each cohort. It allows sharing information between cohorts to increase power by assuming a graph linking predictors within and across different cohorts. The graph helps to identify pathways of functionally related genes and genes that are simultaneously prognostic in different cohorts.ConclusionsResults demonstrate that our proposed approach is superior to the standard approaches in terms of prediction performance and increased power in variable selection when the sample size is small.

【 授权许可】

CC BY