Survival analysis of microarray expression data by transformation models

  • Authors:

  • Jinfeng Xu;Yaning Yang;Jurg Ott

  • Affiliations:

  • Department of Statistics, Columbia University, New York, NY 10027, USA;Laboratory of Statistical Genetics, Rockefeller University, New York, NY 10021, USA and Department of Statistics and Finance, University of Science and Technology, Hefei, Anhui 230026, China;Laboratory of Statistical Genetics, Rockefeller University, New York, NY 10021, USA

  • Venue:
  • Computational Biology and Chemistry
  • Year:
  • 2005

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Abstract

Many microarray experiments involve examining the time elapsed prior to the occurrence of a specific event. One purpose of these studies is to relate the gene expressions to the survival times. The Cox proportional hazards model has been the major tool for analyzing such data. The transformation model provides a viable alternative to the classical Cox's model. We investigate the use of transformation models in microarray survival data in this paper. The transformation model, which can be viewed as a generalization of proportional hazards model and the proportional odds model, is more robust than the proportional hazards model, because it is not susceptible to erroneous results for cases when the assumption of proportional hazards is violated. We analyze a gene expression dataset from Beer et al. [Beer, D.G., Kardia, S.L., Huang, C.C., Giordano, T.J., Levin, A.M., Misek, D.E., Lin, L., Chen, G., Gharib, T.G., Thomas, D.G., Lizyness, M.L., Kuick, R., Hayasaka, S., Taylor, J.M., Iannettoni, M.D., Orringer, M.B., Hanash, S., 2002. Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat. Med. 8 (8), 816-824] and show that the transformation model provides higher prediction precision than the proportional hazards model.