Journal
STATISTICS IN MEDICINE
Volume 32, Issue 23, Pages 4118-4134Publisher
WILEY
DOI: 10.1002/sim.5823
Keywords
simulation; survival; time-varying covariates; time-dependent effects; delayed entry; measurement error
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Funding
- National Institute for Health Research (NIHR) Doctoral Research Fellowship [DRF-2012-05-409]
- National Institutes of Health Research (NIHR) [DRF-2012-05-409] Funding Source: National Institutes of Health Research (NIHR)
- National Institute for Health Research [DRF-2012-05-409] Funding Source: researchfish
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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 (c) 2013 John Wiley & Sons, Ltd.
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