Integrative Analysis of Multiple Cancer Prognosis Datasets Under the Heterogeneity Model

Jin Liu, Jian Huang, Shuangge Ma

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)


In cancer research, genomic studies have been extensively conducted, searching for markers associated with prognosis. Because of the "large d, small n" characteristic, results generated from the analysis of a single dataset can be unsatisfactory. Integrative analysis simultaneously analyzes multiple datasets and can be more effective than the analysis of single datasets and classic meta-analysis. In many existing integrative analyses, the homogeneity model has been assumed, which postulates that different datasets share the same set of markers. In practice, datasets may have been generated in studies that differ in patient selection criteria, profiling techniques, and many other aspects. Such differences may make the homogeneity model too restricted. Here we explore the heterogeneity model, which assumes that different datasets may have different sets of markers. With multiple cancer prognosis datasets, we adopt the AFT (accelerated failure time) models to describe survival. A weighted least squares approach is adopted for estimation. For marker selection, penalization-based methods are examined. These methods have intuitive formulations and can be computed using effective group coordinate descent algorithms. Analysis of three lung cancer prognosis datasets with gene expression measurements demonstrates the merit of heterogeneity model and proposed methods.

Original languageEnglish
Title of host publicationTopics in Applied Statistics - 2012 Symposium of the International Chinese Statistical Association
Number of pages13
Publication statusPublished - 28 Oct 2013
Externally publishedYes
Event21st Symposium of the International Chinese Statistical Association, ICSA 2012 - Boston, MA, United States
Duration: 23 Jun 201226 Jun 2012

Publication series

NameSpringer Proceedings in Mathematics and Statistics
ISSN (Print)2194-1009
ISSN (Electronic)2194-1017


Conference21st Symposium of the International Chinese Statistical Association, ICSA 2012
Country/TerritoryUnited States
CityBoston, MA

ASJC Scopus subject areas

  • Mathematics(all)

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