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Identification of interactions of binary variables associated with survival time using survivalFS

  • Analytical Toxicology
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Abstract

Many medical studies aim to identify factors associated with a time to an event such as survival time or time to relapse. Often, in particular, when binary variables are considered in such studies, interactions of these variables might be the actual relevant factors for predicting, e.g., the time to recurrence of a disease. Testing all possible interactions is often not possible, so that procedures such as logic regression are required that avoid such an exhaustive search. In this article, we present an ensemble method based on logic regression that can cope with the instability of the regression models generated by logic regression. This procedure called survivalFS also provides measures for quantifying the importance of the interactions forming the logic regression models on the time to an event and for the assessment of the individual variables that take the multivariate data structure into account. In this context, we introduce a new performance measure, which is an adaptation of Harrel’s concordance index. The performance of survivalFS and the proposed importance measures is evaluated in a simulation study as well as in an application to genotype data from a urinary bladder cancer study. Furthermore, we compare the performance of survivalFS and its importance measures for the individual variables with the variable importance measure used in random survival forests, a popular procedure for the analysis of survival data. These applications show that survivalFS is able to identify interactions associated with time to an event and to outperform random survival forests.

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Acknowledgements

The authors would like to thank Hannah Bürger for help with the simulation study. This work was supported by the Deutsche Forschungsgemeinschaft (SCHW 1508/3-1 to H.S.; project C4 of the Collaborative Research Center SFB 876 to K.I.).

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Authors and Affiliations

  1. Mathematical Institute, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany

    Tobias Tietz & Holger Schwender

  2. Leibniz Research Centre for Working Environment and Human Factors, TU Dortmund University, IfADo, 44139, Dortmund, Germany

    Silvia Selinski, Klaus Golka & Jan G. Hengstler

  3. Department of Radiation Oncology, Heinrich Heine University Hospital, 44225, Düsseldorf, Germany

    Stephan Gripp

  4. Faculty of Statistics, TU Dortmund University, 44221, Dortmund, Germany

    Katja Ickstadt

  5. Department of Biostatistics, Johns Hopkins University, Baltimore, MD, 21205, USA

    Ingo Ruczinski

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  1. Tobias Tietz
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  2. Silvia Selinski
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  3. Klaus Golka
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  4. Jan G. Hengstler
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Correspondence to Holger Schwender.

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Tietz, T., Selinski, S., Golka, K. et al. Identification of interactions of binary variables associated with survival time using survivalFS. Arch Toxicol 93, 585–602 (2019). https://doi.org/10.1007/s00204-019-02398-6

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