Infinite mixture-of-experts model for sparse survival regression with application to breast cancer

• Main Authors: Dahl Edgar, Wild Peter J, Fuchs Thomas J, Raman Sudhir, Buhmann Joachim M, Roth Volker
• Format: Article
• Language: English
• Published: BMC 2010-10-01
• Series: BMC Bioinformatics
• Online Access: http://www.biomedcentral.com/1471-2105/11/S8/S8

<p>Abstract</p> <p>Background</p> <p>We present an infinite mixture-of-experts model to find an unknown number of sub-groups within a given patient cohort based on survival analysis. The effect of patient features on survival is modeled using the Cox’s proportionality hazards model which yields a non-standard regression component. The model is able to find key explanatory factors (chosen from main effects and higher-order interactions) for each sub-group by enforcing sparsity on the regression coefficients via the Bayesian Group-Lasso.</p> <p>Results</p> <p>Simulated examples justify the need of such an elaborate framework for identifying sub-groups along with their key characteristics versus other simpler models. When applied to a breast-cancer dataset consisting of survival times and protein expression levels of patients, it results in identifying two distinct sub-groups with different survival patterns (low-risk and high-risk) along with the respective sets of compound markers.</p> <p>Conclusions</p> <p>The unified framework presented here, combining elements of cluster and feature detection for survival analysis, is clearly a powerful tool for analyzing survival patterns within a patient group. The model also demonstrates the feasibility of analyzing complex interactions which can contribute to definition of novel prognostic compound markers.</p>