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Transformation survival models

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  • Yulia Marchenko

    (StataCorp LP)

Abstract

The Cox proportional hazards model is one of the most popular methods for analyzing survival or failure-time data. The key assumption underlying the Cox model is that of proportional hazards. This assumption may often be violated in practice. Transformation survival models extend the Cox regression methodology to allow for nonproportional hazards. They represent the class of semiparametric linear transformation models, which relates an unknown transformation of the survival time linearly to covariates. In my presentation, I will describe these models and demonstrate how to fit them in Stata.

Suggested Citation

  • Yulia Marchenko, 2014. "Transformation survival models," 2014 Stata Conference 20, Stata Users Group.
    • Handle: RePEc:boc:scon14:20
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    File URL: http://repec.org/bos2014/boston14_marchenko.pdf
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    References listed on IDEAS

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    1. D. Zeng & D. Y. Lin, 2007. "Maximum likelihood estimation in semiparametric regression models with censored data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(4), pages 507-564, September.
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