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Regression analysis of censored data using pseudo-observations

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  • Erik T. Parner

    (University of Aarhus)

  • Per K. Andersen

    (University of Copenhagen)

Abstract

We draw upon a series of articles in which a method based on pseu- dovalues is proposed for direct regression modeling of the survival function, the restricted mean, and the cumulative incidence function in competing risks with right-censored data. The models, once the pseudovalues have been computed, can be fit using standard generalized estimating equation software. Here we present Stata procedures for computing these pseudo-observations. An example from a bone marrow transplantation study is used to illustrate the method.

Suggested Citation

  • 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.
    • Handle: RePEc:tsj:stataj:v:10:y:2010:i:3:p:408-422
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    References listed on IDEAS

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    1. Per K. Andersen & John P. Klein, 2007. "Regression Analysis for Multistate Models Based on a Pseudo‐value Approach, with Applications to Bone Marrow Transplantation Studies," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(1), pages 3-16, March.
    2. 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.
    3. 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.
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    Cited by:

    1. Govert E. Bijwaard & Mikko Myrskylä & Per Tynelius & Finn Rasmussen, 2017. "Educational gain in cause-specific mortality: accounting for confounders," MPIDR Working Papers WP-2017-003, Max Planck Institute for Demographic Research, Rostock, Germany.
    2. Govert E. Bijwaard & Per Tynelius & Mikko Myrskylä, 2019. "Education, cognitive ability, and cause-specific mortality: A structural approach," Population Studies, Taylor & Francis Journals, vol. 73(2), pages 217-232, May.
    3. 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.
    4. 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.
    5. H. Joseph Newton & Nicholas J. Cox, 2013. "The Stata Journal Editors' Prize 2013: Erik Thorlund Parner and Per Kragh Andersen," Stata Journal, StataCorp LP, vol. 13(4), pages 669-671, December.
    6. Kamaryn T. Tanner & Linda D. Sharples & Rhian M. Daniel & Ruth H. Keogh, 2021. "Dynamic survival prediction combining landmarking with a machine learning ensemble: Methodology and empirical comparison," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 3-30, January.

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