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Model-Based Recursive Partitioning for Subgroup Analyses

Author

Listed:
  • Seibold Heidi

    (Department of Biostatistics, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland)

  • Zeileis Achim

    (Department of Statistics, Faculty of Economics and Statistics University of Innsbruck, Innsbruck, Austria)

  • Hothorn Torsten

    (Department of Biostatistics, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland)

Abstract

The identification of patient subgroups with differential treatment effects is the first step towards individualised treatments. A current draft guideline by the EMA discusses potentials and problems in subgroup analyses and formulated challenges to the development of appropriate statistical procedures for the data-driven identification of patient subgroups. We introduce model-based recursive partitioning as a procedure for the automated detection of patient subgroups that are identifiable by predictive factors. The method starts with a model for the overall treatment effect as defined for the primary analysis in the study protocol and uses measures for detecting parameter instabilities in this treatment effect. The procedure produces a segmented model with differential treatment parameters corresponding to each patient subgroup. The subgroups are linked to predictive factors by means of a decision tree. The method is applied to the search for subgroups of patients suffering from amyotrophic lateral sclerosis that differ with respect to their Riluzole treatment effect, the only currently approved drug for this disease.

Suggested Citation

  • Seibold Heidi & Zeileis Achim & Hothorn Torsten, 2016. "Model-Based Recursive Partitioning for Subgroup Analyses," The International Journal of Biostatistics, De Gruyter, vol. 12(1), pages 45-63, May.
    • Handle: RePEc:bpj:ijbist:v:12:y:2016:i:1:p:45-63:n:15
    DOI: 10.1515/ijb-2015-0032
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    References listed on IDEAS

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    1. Hothorn, Torsten & Hornik, Kurt & van de Wiel, Mark A. & Zeileis, Achim, 2006. "A Lego System for Conditional Inference," The American Statistician, American Statistical Association, vol. 60, pages 257-263, August.
    2. Kehl, Victoria & Ulm, Kurt, 2006. "Responder identification in clinical trials with censored data," Computational Statistics & Data Analysis, Elsevier, vol. 50(5), pages 1338-1355, March.
    3. Achim Zeileis & Kurt Hornik, 2007. "Generalized M‐fluctuation tests for parameter instability," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 61(4), pages 488-508, November.
    4. Leeb, Hannes & Pötscher, Benedikt M., 2005. "Model Selection And Inference: Facts And Fiction," Econometric Theory, Cambridge University Press, vol. 21(1), pages 21-59, February.
    5. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
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    Cited by:

    1. Heidi Seibold & Torsten Hothorn & Achim Zeileis, 2019. "Generalised linear model trees with global additive effects," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(3), pages 703-725, September.
    2. Vrontis, Demetris & Morea, Donato & Basile, Gianpaolo & Bonacci, Isabella & Mazzitelli, Andrea, 2021. "Consequences of technology and social innovation on traditional business model," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    3. Payton J. Jones & Patrick Mair & Thorsten Simon & Achim Zeileis, 2020. "Network Trees: A Method for Recursively Partitioning Covariance Structures," Psychometrika, Springer;The Psychometric Society, vol. 85(4), pages 926-945, December.
    4. Jones, Payton J. & Mair, Patrick & Simon, Thorsten & Zeileis, Achim, 2019. "Network Model Trees," OSF Preprints ha4cw, Center for Open Science.
    5. Daniel Wochner, 2020. "Dynamic Factor Trees and Forests – A Theory-led Machine Learning Framework for Non-Linear and State-Dependent Short-Term U.S. GDP Growth Predictions," KOF Working papers 20-472, KOF Swiss Economic Institute, ETH Zurich.
    6. Alexander J. Ohnmacht & Arndt Stahler & Sebastian Stintzing & Dominik P. Modest & Julian W. Holch & C. Benedikt Westphalen & Linus Hölzel & Marisa K. Schübel & Ana Galhoz & Ali Farnoud & Minhaz Ud-Dea, 2023. "The Oncology Biomarker Discovery framework reveals cetuximab and bevacizumab response patterns in metastatic colorectal cancer," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    7. Cynthia Huber & Norbert Benda & Tim Friede, 2022. "Subgroup identification in individual participant data meta-analysis using model-based recursive partitioning," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(3), pages 797-815, September.
    8. repec:osf:osfxxx:ha4cw_v1 is not listed on IDEAS

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