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Simulating complex survival data

Author

Listed:
  • Michael J. Crowther

    (University of Leicester)

  • Paul C. Lambert

    (University of Leicester
    Karolinska Institutet)

Abstract

Simulation studies are essential for understanding and evaluating both current and new statistical models. When simulating survival times, one often assumes an exponential or Weibull distribution for the baseline hazard function, with survival times generated using the method of Bender, Augustin, and Blettner (2005, Statistics in Medicine 24: 1713–1723). Assuming a constant or monotonic hazard can be considered too simplistic and can lack biological plausibility in many situations. We describe http://www.stata-journal.com/software/a new user-written command, survsim, which al- lows the user to simulate survival times from two-component parametric mixture models, providing much more flexibility in the underlying hazard. Standard para- metric distributions can also be used, including the exponential, Weibull, and Gompertz. Furthermore, survival times can be simulated from the all-cause dis- tribution of cause-specific hazards for competing risks by using the method of Beyersmann et al. (2009, Statistics in Medicine 24: 956–971). A multinomial dis- tribution is used to create the event indicator, whereby the probability of expe- riencing each event at a simulated time t is the cause-specific hazard divided by the all-cause hazard evaluated at time t. Baseline covariates can be included in all scenarios. We also describe http://www.stata-journal.com/software/the extension to incorporate nonproportional hazards in standard parametric and competing-risks scenarios. Copyright 2012 by StataCorp LP.

Suggested Citation

  • Michael J. Crowther & Paul C. Lambert, 2012. "Simulating complex survival data," Stata Journal, StataCorp LP, vol. 12(4), pages 674-687, December.
    • Handle: RePEc:tsj:stataj:v:12:y:2012:i:4:p:674-687
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    References listed on IDEAS

    as
    1. Patrick Royston & Paul C. Lambert, 2011. "Flexible Parametric Survival Analysis Using Stata: Beyond the Cox Model," Stata Press books, StataCorp LP, number fpsaus, March.
    2. Vincenzo Coviello & May Boggess, 2004. "Cumulative incidence estimation in the presence of competing risks," Stata Journal, StataCorp LP, vol. 4(2), pages 103-112, June.
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    Cited by:

    1. Michael J. Crowther, 2014. "Simulating simple and complex survival data," United Kingdom Stata Users' Group Meetings 2014 06, Stata Users Group.
    2. Alessandro Gasparini & Keith R. Abrams & Jessica K. Barrett & Rupert W. Major & Michael J. Sweeting & Nigel J. Brunskill & Michael J. Crowther, 2020. "Mixed‐effects models for health care longitudinal data with an informative visiting process: A Monte Carlo simulation study," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(1), pages 5-23, February.

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