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Bivariate pseudo-observations for recurrent event analysis with terminal events

Author

Listed:
  • Julie K. Furberg

    (Biostatistics GLP-1 and CV 1, Novo Nordisk A/S)

  • Per K. Andersen

    (University of Copenhagen)

  • Sofie Korn

    (Biostatistics 1, LEO Pharma A/S)

  • Morten Overgaard

    (Aarhus University)

  • Henrik Ravn

    (Biostatistics GLP-1 and CV 1, Novo Nordisk A/S)

Abstract

The analysis of recurrent events in the presence of terminal events requires special attention. Several approaches have been suggested for such analyses either using intensity models or marginal models. When analysing treatment effects on recurrent events in controlled trials, special attention should be paid to competing deaths and their impact on interpretation. This paper proposes a method that formulates a marginal model for recurrent events and terminal events simultaneously. Estimation is based on pseudo-observations for both the expected number of events and survival probabilities. Various relevant hypothesis tests in the framework are explored. Theoretical derivations and simulation studies are conducted to investigate the behaviour of the method. The method is applied to two real data examples. The bivariate marginal pseudo-observation model carries the strength of a two-dimensional modelling procedure and performs well in comparison with available models. Finally, an extension to a three-dimensional model, which decomposes the terminal event per death cause, is proposed and exemplified.

Suggested Citation

  • Julie K. Furberg & Per K. Andersen & Sofie Korn & Morten Overgaard & Henrik Ravn, 2023. "Bivariate pseudo-observations for recurrent event analysis with terminal events," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(2), pages 256-287, April.
    • Handle: RePEc:spr:lifeda:v:29:y:2023:i:2:d:10.1007_s10985-021-09533-5
    DOI: 10.1007/s10985-021-09533-5
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    References listed on IDEAS

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    1. Klemen Pavlič & Torben Martinussen & Per Kragh Andersen, 2019. "Goodness of fit tests for estimating equations based on pseudo-observations," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(2), pages 189-205, April.
    2. Debashis Ghosh & D. Y. Lin, 2000. "Nonparametric Analysis of Recurrent Events and Death," Biometrics, The International Biometric Society, vol. 56(2), pages 554-562, June.
    3. Lei Liu & Robert A. Wolfe & Xuelin Huang, 2004. "Shared Frailty Models for Recurrent Events and a Terminal Event," Biometrics, The International Biometric Society, vol. 60(3), pages 747-756, September.
    4. D. Y. Lin & L. J. Wei & I. Yang & Z. Ying, 2000. "Semiparametric regression for the mean and rate functions of recurrent events," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 711-730.
    5. Per Kragh Andersen, 2003. "Generalised linear models for correlated pseudo-observations, with applications to multi-state models," Biometrika, Biometrika Trust, vol. 90(1), pages 15-27, March.
    6. Per Kragh Andersen & Jules Angst & Henrik Ravn, 2019. "Modeling marginal features in studies of recurrent events in the presence of a terminal event," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(4), pages 681-695, October.
    7. Martin Jacobsen & Torben Martinussen, 2016. "A Note on the Large Sample Properties of Estimators Based on Generalized Linear Models for Correlated Pseudo-observations," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(3), pages 845-862, September.
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