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Survival analysis of microarray expression data by transformation models.

Jinfeng Xu1, Yaning Yang, Jurg Ott

  • 1Department of Statistics, Columbia University, New York, NY 10027, USA. xu@stat.columbia.edu

Computational Biology and Chemistry
|April 19, 2005
PubMed
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Transformation models offer a more robust alternative for analyzing microarray survival data, improving prediction accuracy compared to traditional Cox models when survival data assumptions are violated.

Area of Science:

  • Bioinformatics
  • Biostatistics
  • Genomics

Background:

  • Microarray experiments frequently analyze time-to-event data.
  • Relating gene expression to survival time is a key objective.
  • The Cox proportional hazards model is a standard analytical tool.

Purpose of the Study:

  • To investigate the utility of transformation models for microarray survival data.
  • To evaluate transformation models as an alternative to the Cox model.
  • To assess the robustness and prediction precision of transformation models.

Main Methods:

  • Utilized transformation models, a generalization of proportional hazards and odds models.
  • Applied the transformation model to a lung adenocarcinoma gene expression dataset (Beer et al., 2002).

Related Experiment Videos

  • Compared the prediction precision of the transformation model against the Cox proportional hazards model.

    • Main Results:

    • Transformation models demonstrated higher prediction precision.
    • The transformation model proved more robust when the proportional hazards assumption was violated.
    • Gene expression profiles were analyzed in relation to patient survival.

    Conclusions:

    • Transformation models are a valuable and robust tool for analyzing microarray survival data.
    • These models offer improved prediction accuracy over the Cox model, especially in challenging datasets.
    • The findings have implications for understanding gene expression and patient survival outcomes.