B1374
Title: How to simulate realistic survival data? A simulation study to compare realistic simulation models
Authors: Maria Thurow - TU Dortmund University (Germany) [presenting]
Ina Dormuth - TU Dortmund University (Germany)
Christina Sauer - Ludwig-Maximilians-University Munich (Germany)
Marc Ditzhaus - Otto-von-Guericke University Magdeburg (Germany)
Markus Pauly - Technical University of Dortmund (Germany)
Abstract: In statistics, it is important to have realistic data sets available for a particular context to allow an appropriate and objective method comparison. Benchmark data sets for method comparison are available online for many use cases. However, in most medical applications and especially for clinical trials in oncology, there is a lack of adequate benchmark data sets, as patient data can be sensitive and, therefore, cannot be published. A potential solution for this is simulation studies. However, it is sometimes not clear which simulation models are suitable for generating realistic data. A challenge is that potentially unrealistic assumptions have to be made about the distributions. The approach is to use reconstructed benchmark data sets as a basis for the simulations, which has the following advantages: the actual properties are known, and more realistic data can be simulated. There are several possibilities to simulate realistic data from benchmark data sets. Simulation models are investigated based on kernel density estimation, fitted distributions, case resampling and conditional bootstrapping. In order to make recommendations on which models are best suited for a specific survival setting, a comparative simulation study was conducted. Benchmark data sets are reconstructed from two-armed phase III lung cancer studies studies. The runtime and different accuracy measures (effect sizes and p-values) are used as criteria for comparison.
