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Direct Modelling of Regression Effects for Transition Probabilities in Multistate Models

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  • THOMAS H. SCHEIKE
  • MEI‐JIE ZHANG

Abstract

. A simple and standard approach for analysing multistate model data is to model all transition intensities and then compute a summary measure such as the transition probabilities based on this. This approach is relatively simple to implement but it is difficult to see what the covariate effects are on the scale of interest. In this paper, we consider an alternative approach that directly models the covariate effects on transition probabilities in multistate models. Our new approach is based on binomial modelling and inverse probability of censoring weighting techniques and is very simple to implement by standard software. We show how to do flexible regression models with possibly time‐varying covariate effects.

Suggested Citation

  • Thomas H. Scheike & Mei‐Jie Zhang, 2007. "Direct Modelling of Regression Effects for Transition Probabilities in Multistate Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(1), pages 17-32, March.
    • Handle: RePEc:bla:scjsta:v:34:y:2007:i:1:p:17-32
    DOI: 10.1111/j.1467-9469.2006.00544.x
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    References listed on IDEAS

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    1. R.D. Gill, 1980. "Censoring and Stochastic Integrals," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 34(2), pages 124-124, June.
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    Cited by:

    1. Chathura Siriwardhana & K. B. Kulasekera & Somnath Datta, 2018. "Flexible semi-parametric regression of state occupational probabilities in a multistate model with right-censored data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(3), pages 464-491, July.
    2. Frank Eriksson & Jianing Li & Thomas Scheike & Mei‐Jie Zhang, 2015. "The proportional odds cumulative incidence model for competing risks," Biometrics, The International Biometric Society, vol. 71(3), pages 687-695, September.
    3. Yayuan Zhu & Ziqi Chen & Jerald F. Lawless, 2022. "Semiparametric analysis of interval‐censored failure time data with outcome‐dependent observation schemes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(1), pages 236-264, March.
    4. Jacob Pedersen & Svetlana Solovieva & Sannie Vester Thorsen & Malene Friis Andersen & Ute Bültmann, 2021. "Expected Labor Market Affiliation: A New Method Illustrated by Estimating the Impact of Perceived Stress on Time in Work, Sickness Absence and Unemployment of 37,605 Danish Employees," IJERPH, MDPI, vol. 18(9), pages 1-13, May.
    5. Giorgos Bakoyannis, 2021. "Nonparametric analysis of nonhomogeneous multistate processes with clustered observations," Biometrics, The International Biometric Society, vol. 77(2), pages 533-546, June.
    6. Per Kragh Andersen & Eva Nina Sparre Wandall & Maja Pohar Perme, 2022. "Inference for transition probabilities in non-Markov multi-state models," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(4), pages 585-604, October.
    7. Arthur Allignol & Martin Schumacher & Jan Beyersmann, 2011. "Estimating summary functionals in multistate models with an application to hospital infection data," Computational Statistics, Springer, vol. 26(2), pages 181-197, June.
    8. Li, Ruosha & Peng, Limin, 2014. "Varying coefficient subdistribution regression for left-truncated semi-competing risks data," Journal of Multivariate Analysis, Elsevier, vol. 131(C), pages 65-78.
    9. Tianyu Zhan & Douglas E. Schaubel, 2019. "Semiparametric temporal process regression of survival-out-of-hospital," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(2), pages 322-340, April.

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