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Generalised linear models for correlated pseudo-observations, with applications to multi-state models

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Cited by:

  1. Yayun Xu & Soyoung Kim & Mei-Jie Zhang & David Couper & Kwang Woo Ahn, 2022. "Competing risks regression models with covariates-adjusted censoring weight under the generalized case-cohort design," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 28(2), pages 241-262, April.
  2. Ewa Wycinka & Tomasz Jurkiewicz, 2019. "Survival Regression Models For Single Events And Competing Risks Based On Pseudoobservations," Statistics in Transition New Series, Polish Statistical Association, vol. 20(1), pages 171-188, March.
  3. 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.
  4. 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.
  5. Erik T. Parner & Per K. Andersen & Morten Overgaard, 2020. "Cumulative risk regression in case–cohort studies using pseudo-observations," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 639-658, October.
  6. Brent R. Logan & John P. Klein & Mei‐Jie Zhang, 2008. "Comparing Treatments in the Presence of Crossing Survival Curves: An Application to Bone Marrow Transplantation," Biometrics, The International Biometric Society, vol. 64(3), pages 733-740, September.
  7. Zijing Yang & Chengfeng Zhang & Yawen Hou & Zheng Chen, 2023. "Analysis of dynamic restricted mean survival time based on pseudo‐observations," Biometrics, The International Biometric Society, vol. 79(4), pages 3690-3700, December.
  8. Adin-Cristian Andrei & Susan Murray, 2007. "Regression Models for the Mean of the Quality-of-Life-Adjusted Restricted Survival Time Using Pseudo-Observations," Biometrics, The International Biometric Society, vol. 63(2), pages 398-404, June.
  9. He, Yizeng & Kim, Soyoung & Kim, Mi-Ok & Saber, Wael & Ahn, Kwang Woo, 2021. "Optimal treatment regimes for competing risk data using doubly robust outcome weighted learning with bi-level variable selection," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
  10. Szilárd Nemes & Erik Bülow & Andreas Gustavsson, 2020. "A Brief Overview of Restricted Mean Survival Time Estimators and Associated Variances," Stats, MDPI, vol. 3(2), pages 1-13, May.
  11. Deresa, Negera Wakgari & Van Keilegom, Ingrid, 2020. "A multivariate normal regression model for survival data subject to different types of dependent censoring," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
  12. Sangbum Choi & Xuelin Huang, 2014. "Maximum likelihood estimation of semiparametric mixture component models for competing risks data," Biometrics, The International Biometric Society, vol. 70(3), pages 588-598, September.
  13. Su, Pei-Fang & Chi, Yunchan & Li, Chung-I & Shyr, Yu & Liao, Yi-De, 2011. "Analyzing survival curves at a fixed point in time for paired and clustered right-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1617-1628, April.
  14. John P. Klein & Per Kragh Andersen, 2005. "Regression Modeling of Competing Risks Data Based on Pseudovalues of the Cumulative Incidence Function," Biometrics, The International Biometric Society, vol. 61(1), pages 223-229, March.
  15. Erik T. Parner & Per K. Andersen, 2010. "Regression analysis of censored data using pseudo-observations," Stata Journal, StataCorp LP, vol. 10(3), pages 408-422, September.
  16. 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.
  17. 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.
  18. Annalisa Orenti & Patrizia Boracchi & Giuseppe Marano & Elia Biganzoli & Federico Ambrogi, 2022. "A pseudo-values regression model for non-fatal event free survival in the presence of semi-competing risks," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 31(3), pages 709-727, September.
  19. 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.
  20. M. A. Nicolaie & J. C. van Houwelingen & T. M. de Witte & H. Putter, 2013. "Dynamic Pseudo-Observations: A Robust Approach to Dynamic Prediction in Competing Risks," Biometrics, The International Biometric Society, vol. 69(4), pages 1043-1052, December.
  21. 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.
  22. 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.
  23. Wycinka Ewa, 2019. "Competing Risk Models of Default in the Presence of Early Repayments," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 23(2), pages 99-120, June.
  24. Wycinka Ewa & Jurkiewicz Tomasz, 2019. "Survival Regression Models For Single Events And Competing Risks Based On Pseudo-Observations," Statistics in Transition New Series, Polish Statistical Association, vol. 20(1), pages 171-188, March.
  25. Yanzhi Wang & Brent R. Logan, 2019. "Testing for center effects on survival and competing risks outcomes using pseudo-value regression," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(2), pages 206-228, April.
  26. Erik T. Parner & Per K. Andersen & Morten Overgaard, 2023. "Regression models for censored time-to-event data using infinitesimal jack-knife pseudo-observations, with applications to left-truncation," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(3), pages 654-671, July.
  27. Guibert, Quentin & Planchet, Frédéric, 2018. "Non-parametric inference of transition probabilities based on Aalen–Johansen integral estimators for acyclic multi-state models: application to LTC insurance," Insurance: Mathematics and Economics, Elsevier, vol. 82(C), pages 21-36.
  28. Tunes-da-Silva, Gisela & Klein, John P., 2011. "Cutpoint selection for discretizing a continuous covariate for generalized estimating equations," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 226-235, January.
  29. Brent R. Logan & Mei-Jie Zhang & John P. Klein, 2011. "Marginal Models for Clustered Time-to-Event Data with Competing Risks Using Pseudovalues," Biometrics, The International Biometric Society, vol. 67(1), pages 1-7, March.
  30. 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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