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Simulating biologically plausible complex survival data

Open access
Crowther, Michael J., Lambert, Paul C.·2013·Statistics in Medicine

Abstract

Simulation studies are conducted to assess the performance of current and novel statistical models in pre‐defined scenarios. It is often desirable that chosen simulation scenarios accurately reflect a biologically plausible underlying distribution. This is particularly important in the framework of survival analysis, where simulated distributions are chosen for both the event time and the censoring time. This paper develops methods for using complex distributions when generating survival times to assess methods in practice. We describe a general algorithm involving numerical integration and root‐finding techniques to generate survival times from a variety of complex parametric distributions, incorporating any combination of time‐dependent effects, time‐varying covariates, delayed entry, random effects and covariates measured with error. User‐friendly Stata software is provided. Copyright © 2013 John Wiley & Sons, Ltd.

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Metadata

Authors
Crowther, M. J., & Lambert, P. C.
Venue
Statistics in Medicine
Publisher
Wiley
Published
2013-04-23
Volume
32
Issue
23
Pages
4118-4134
ISSN
  • 0277-6715 (ISSN-L)
  • 1097-0258
OpenAlex
W2139276809
Cited by
164
References
1
FWCI
6.59
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Simulating biologically plausible complex survival data. https://app.cochat.ai/papers/10.1002/sim.5823
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Crowther, M. J., & Lambert, P. C. (2013). Simulating biologically plausible complex survival data. Statistics in Medicine. https://doi.org/10.1002/sim.5823

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.