The R package survsim for the simulation of simple and complex survival data

1. The R package survsim for the simulation of simple and
complex survival data
David Moriña and Albert Navarro
5 de Novembre de 2014, GRASS

2. Contents
1 Introduction
2 Introduction
3 Overview of the package
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3. Introduction
Introduction
• Compared to the simulation of other types of data, the process of simu-
lating survival data requires certain specific considerations.
• To simulate the (randomly right) censored observations, we need to sim-
ulate a lifetime vector and, independently, a censoring time vector.
• There are various situations which make true survival data much more
complex.
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4. Introduction
Introduction
• Compared to the simulation of other types of data, the process of simu-
lating survival data requires certain specific considerations.
• To simulate the (randomly right) censored observations, we need to sim-
ulate a lifetime vector and, independently, a censoring time vector.
• There are various situations which make true survival data much more
complex.
• The phenomenon of interest could occur more than once in a given
individual (recurrent events).
• We could be interested in the instantaneous analysis of multiple events of
different types.
• We can work with dynamic cohorts in which an individual may be incor-
porated to follow-up after the study has begun, or an individual may be
incorporated to follow-up after being at risk for some time.
• Discontinuous risk intervals or individual heterogeneity (propensity of an
individual to suffer an event due to hidden variables) are other phenom-
ena which involve situations that make simulation even more difficult.
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5. Introduction
Introduction
• Although there is an increasing interest, and need, to apply survival anal-
ysis to data sets with multiple and recurrent events per subject, very few
published articles are available in the literature which make use of the
simulation of complex survival data, one possible reason being a lack of
easily used tools facilitating such simulations.
• Stata has the SURVSIM module for the simulation of complex survival
data, also in a context of non-proportional hazards.
• The analysis of this type of data can be performed in R using the eha
package, which allows fitting models for recurrent and multiple events, as
well as other contexts.
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6. Overview of the package
Recurrent events
Recurrent events
• Recurrent event data refers to situations where the subject can experi-
ence repeated episodes of the same type of event
• There are many examples such as injuries, nosocomial infections, asthma
attacks . . .
• If these phenomena are studied via a cohort, the application of survival
methods would seem appropriate
• During the 1980s, at theoretical level, and during the 1990s and early
21st century in practical terms through development of software, various
methods have been proposed to tackle events of this type, both non-
parametric and parametric
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8. Centre for Research
in Environmental
Epidemiology
Parc de Recerca Biomèdica de Barcelona
Doctor Aiguader, 88
08003 Barcelona (Spain)
Tel. (+34) 93 214 70 00
Fax (+34) 93 214 73 02
info@creal.cat
www.creal.cat
Grup de Recerca d’Amèrica i Àfrica Llatines
Unitat de Bioestadística, Facultat de Medicina
Universitat Autònoma de Barcelona
www.uab.cat