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Simulating simple and complex survival data


  • Michael J. Crowther

    (Centre for Biostatistics and Genetic Epidemiology, University of Leicester)


Simulation studies are conducted to assess novel and currently used methods in practice, to better assess and understand the frameworks under question. In survival analysis, we are interested in simulating both an event and a censoring distribution to better reflect clinical data. In this talk, I will describe how to simulate survival times from simple parametric distributions and then move to a more general framework, illustrating how to simulate from a general user-defined hazard function. This can incorporate any combination of a complex baseline hazard function with turning points, time-dependent effects, random effects, and nonlinear covariate effects. This is achieved through a two-stage algorithm incorporating numerical integration nested within root-finding techniques. The methods will be illustrated using the publicly available survsim package.

Suggested Citation

  • Michael J. Crowther, 2014. "Simulating simple and complex survival data," United Kingdom Stata Users' Group Meetings 2014 06, Stata Users Group.
  • Handle: RePEc:boc:usug14:06

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    References listed on IDEAS

    1. Michael J. Crowther & Paul C. Lambert, 2012. "Simulating complex survival data," Stata Journal, StataCorp LP, vol. 12(4), pages 674-687, December.
    2. Patrick Royston, 2012. "Tools to simulate realistic censored survival-time distributions," Stata Journal, StataCorp LP, vol. 12(4), pages 639-654, December.
    3. Patrick Royston & Paul C. Lambert, 2011. "Flexible Parametric Survival Analysis Using Stata: Beyond the Cox Model," Stata Press books, StataCorp LP, number fpsaus, March.
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