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Validity of the Aalen-Johansen estimators of stage occupation probabilities and Nelson-Aalen estimators of integrated transition hazards for non-Markov models

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  • Datta, Somnath
  • Satten, Glen A.

Abstract

We consider estimation of integrated transition hazard and stage occupation probabilities using right censored i.i.d. data that come from a general multistage model which is not Markov. We show that the Nelson-Aalen estimator for the integrated transition hazard of a Markov process consistently estimates a population quantity even when the underlying process is not Markov. Further, the Aalen-Johansen estimators of the stage occupation probabilities constructed from these integrated hazards via product integration are valid (i.e., consistent) for a general multistage model that is not Markov. These observations appear to have been unnoticed in the literature, where validity of the Aalen-Johansen estimators is only claimed for Markov models.

Suggested Citation

  • Datta, Somnath & Satten, Glen A., 2001. "Validity of the Aalen-Johansen estimators of stage occupation probabilities and Nelson-Aalen estimators of integrated transition hazards for non-Markov models," Statistics & Probability Letters, Elsevier, vol. 55(4), pages 403-411, December.
    • Handle: RePEc:eee:stapro:v:55:y:2001:i:4:p:403-411
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    References listed on IDEAS

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    1. Satten, Glen A. & Datta, Somnath & Robins, James, 2001. "Estimating the marginal survival function in the presence of time dependent covariates," Statistics & Probability Letters, Elsevier, vol. 54(4), pages 397-403, October.
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    1. Somnath Datta & Glen A. Satten, 2002. "Estimation of Integrated Transition Hazards and Stage Occupation Probabilities for Non-Markov Systems Under Dependent Censoring," Biometrics, The International Biometric Society, vol. 58(4), pages 792-802, December.
    2. Richard J. Cook & Jerald F. Lawless & Bingfeng Xie, 2022. "Marker-dependent observation and carry-forward of internal covariates in Cox regression," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(4), pages 560-584, October.
    3. repec:jss:jstsof:38:i03 is not listed on IDEAS
    4. Christiansen, Marcus C. & Furrer, Christian, 2022. "Extension of as-if-Markov modeling to scaled payments," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 288-306.
    5. Tzy-Mey Kuo & C. Suchindran & Helen Koo, 2008. "The multistate life table method: An application to contraceptive switching behavior," Demography, Springer;Population Association of America (PAA), vol. 45(1), pages 157-171, February.
    6. Araújo, Artur & Meira-Machado, Luís & Roca-Pardiñas, Javier, 2014. "TPmsm: Estimation of the Transition Probabilities in 3-State Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 62(i04).
    7. Niklas Maltzahn & Rune Hoff & Odd O. Aalen & Ingrid S. Mehlum & Hein Putter & Jon Michael Gran, 2021. "A hybrid landmark Aalen-Johansen estimator for transition probabilities in partially non-Markov multi-state models," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(4), pages 737-760, October.
    8. Jacobo de Uña-Álvarez & Luís Meira-Machado, 2015. "Nonparametric estimation of transition probabilities in the non-Markov illness-death model: A comparative study," Biometrics, The International Biometric Society, vol. 71(2), pages 364-375, June.
    9. Jacobo de Uña‐Álvarez & Micha Mandel, 2018. "Nonparametric estimation of transition probabilities for a general progressive multi‐state model under cross‐sectional sampling," Biometrics, The International Biometric Society, vol. 74(4), pages 1203-1212, December.
    10. Gustavo Soutinho & Luís Meira-Machado, 2022. "Methods for checking the Markov condition in multi-state survival data," Computational Statistics, Springer, vol. 37(2), pages 751-780, April.
    11. 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.
    12. Dennis Dobler & Andrew Titman, 2020. "Dynamic inference for non‐Markov transition probabilities under random right censoring," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(2), pages 572-586, June.
    13. Bella Vakulenko-Lagun & Micha Mandel & Yair Goldberg, 2017. "Nonparametric estimation in the illness-death model using prevalent data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(1), pages 25-56, January.
    14. Ling Lan & Dipankar Bandyopadhyay & Somnath Datta, 2017. "Non-parametric regression in clustered multistate current status data with informative cluster size," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 71(1), pages 31-57, January.
    15. Giorgos Bakoyannis, 2021. "Nonparametric analysis of nonhomogeneous multistate processes with clustered observations," Biometrics, The International Biometric Society, vol. 77(2), pages 533-546, June.
    16. David V. Glidden, 2002. "Robust Inference for Event Probabilities with Non-Markov Event Data," Biometrics, The International Biometric Society, vol. 58(2), pages 361-368, June.
    17. Marcus C. Christiansen, 2021. "Time-dynamic evaluations under non-monotone information generated by marked point processes," Finance and Stochastics, Springer, vol. 25(3), pages 563-596, July.
    18. Rune Hoff & Hein Putter & Ingrid Sivesind Mehlum & Jon Michael Gran, 2019. "Landmark estimation of transition probabilities in non-Markov multi-state models with covariates," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(4), pages 660-680, October.
    19. Richard J. Cook & Leilei Zeng & Ker-Ai Lee, 2008. "A Multistate Model for Bivariate Interval-Censored Failure Time Data," Biometrics, The International Biometric Society, vol. 64(4), pages 1100-1109, December.
    20. Jan Beyersmann & Hein Putter, 2014. "A note on computing average state occupation times," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 30(62), pages 1681-1696.
    21. Nießl, Alexandra & Allignol, Arthur & Beyersmann, Jan & Mueller, Carina, 2023. "Statistical inference for state occupation and transition probabilities in non-Markov multi-state models subject to both random left-truncation and right-censoring," Econometrics and Statistics, Elsevier, vol. 25(C), pages 110-124.
    22. Somnath Datta & Rajeshwari Sundaram, 2006. "Nonparametric Estimation of Stage Occupation Probabilities in a Multistage Model with Current Status Data," Biometrics, The International Biometric Society, vol. 62(3), pages 829-837, September.

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