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Simulating recurrent event data with hazard functions defined on a total time scale.

Antje Jahn-Eimermacher1, Katharina Ingel2, Ann-Kathrin Ozga3

  • 1Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Center Johannes Gutenberg University Mainz, Obere Zahlbacher Str. 69, Mainz, 55131, Germany. jahna@uni-mainz.de.

BMC Medical Research Methodology
|April 18, 2015
PubMed
Summary
This summary is machine-generated.

Researchers developed a simulation algorithm for recurrent event data analysis using the Andersen-Gill model. This tool aids in sample size planning for clinical trials with complex data, including risk-free intervals and intra-patient correlation.

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Survival Analysis

Background:

  • Recurrent event data analysis often requires a total time scale perspective to accurately model disease progression.
  • Existing methods like the Andersen-Gill model address total time scale analysis, but simulation techniques for sample size planning are lacking.

Purpose of the Study:

  • To derive and validate a simulation algorithm for recurrent event data under the Andersen-Gill model.
  • To facilitate sample size planning and investigation of modeling techniques in clinical trials with complex recurrent event data.

Main Methods:

  • Developed a simulation algorithm based on conditional distributions of inter-event times.
  • The algorithm accommodates fixed and random covariates, arbitrary hazard functions on a total time scale, and temporary insusceptibility periods.
  • Implemented the methods in an R script, providing closed-form solutions for common distributions.

Main Results:

  • The simulation techniques were applied to plan sample sizes for clinical trials with complex recurrent event data.
  • Results indicate that sample size is influenced by censoring, intra-patient correlation, and risk-free intervals.
  • Highlights the necessity of simulation algorithms for complex study designs where analytical formulas may not exist.

Conclusions:

  • The derived simulation algorithm effectively simulates recurrent event data adhering to the Andersen-Gill model.
  • The algorithm accounts for total time scale, intra-patient correlation, and risk-free intervals, mirroring real-world clinical trial data.
  • This tool enhances the design and analysis of studies by enabling realistic data simulation.