Authors:	Jahn, Antje Ingel, Katharina Ozga, Ann-Kathrin Preussler, Stella Binder, Harald
Title:	Simulating recurrent event data with hazard functions defined on a total time scale
Online publication date:	6-Oct-2022
Year of first publication:	2015
Language:	english
Abstract:	Background In medical studies with recurrent event data a total time scale perspective is often needed to adequately reflect disease mechanisms. This means that the hazard process is defined on the time since some starting point, e.g. the beginning of some disease, in contrast to a gap time scale where the hazard process restarts after each event. While techniques such as the Andersen-Gill model have been developed for analyzing data from a total time perspective, techniques for the simulation of such data, e.g. for sample size planning, have not been investigated so far. Methods We have derived a simulation algorithm covering the Andersen-Gill model that can be used for sample size planning in clinical trials as well as the investigation of modeling techniques. Specifically, we allow for fixed and/or random covariates and an arbitrary hazard function defined on a total time scale. Furthermore we take into account that individuals may be temporarily insusceptible to a recurrent incidence of the event. The methods are based on conditional distributions of the inter-event times conditional on the total time of the preceeding event or study start. Closed form solutions are provided for common distributions. The derived methods have been implemented in a readily accessible R script. Results The proposed techniques are illustrated by planning the sample size for a clinical trial with complex recurrent event data. The required sample size is shown to be affected not only by censoring and intra-patient correlation, but also by the presence of risk-free intervals. This demonstrates the need for a simulation algorithm that particularly allows for complex study designs where no analytical sample size formulas might exist. Conclusions The derived simulation algorithm is seen to be useful for the simulation of recurrent event data that follow an Andersen-Gill model. Next to the use of a total time scale, it allows for intra-patient correlation and risk-free intervals as are often observed in clinical trial data. Its application therefore allows the simulation of data that closely resemble real settings and thus can improve the use of simulation studies for designing and analysing studies.
DDC:	610 Medizin 610 Medical sciences
Institution:	Johannes Gutenberg-Universität Mainz
Department:	FB 04 Medizin
Place:	Mainz
ROR:	https://ror.org/023b0x485
DOI:	http://doi.org/10.25358/openscience-7882
Version:	Published version
Publication type:	Zeitschriftenaufsatz
License:	CC BY
Information on rights of use:	https://creativecommons.org/licenses/by/4.0/
Journal:	BMC medical research methodology 15
Pages or article number:	Art. 16
Publisher:	BioMed Central
Publisher place:	London
Issue date:	2015
ISSN:	1471-2288
Publisher URL:	http://dx.doi.org/10.1186/s12874-015-0005-2
Publisher DOI:	10.1186/s12874-015-0005-2
Annotation:	Jahn, Antje veröffentlicht auch unter: Jahn-Eimermacher, Antje
Appears in collections:	DFG-OA-Publizieren (2012 - 2017)