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Efficient t0$$ {t}_0 $$‐year risk regression using the logistic model

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  • Torben Martinussen
  • Thomas Harder Scheike

Abstract

In some clinical studies patient survival beyond a specific point in time, t0$$ {t}_0 $$, say, may be of special interest as it may for instance indicate patient cure. To analyze the t0$$ {t}_0 $$‐year risk for such patients may be accomplished using logistic regression with appropriate weights (IPWCC) that may further be augmented (AIPWCC) to improve efficiency. In this paper, we derive the most efficient estimator for this problem, which is different from the AIPWCC based on the full data efficient influence function. We first give the result for a survival endpoint and then generalize to the competing risk setting. The proposed estimators superior behavior is illustrated using simulations as well as applying it to some real data concerning the survival of blood and marrow transplanted patients.

Suggested Citation

  • Torben Martinussen & Thomas Harder Scheike, 2023. "Efficient t0$$ {t}_0 $$‐year risk regression using the logistic model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(4), pages 1919-1932, December.
    • Handle: RePEc:bla:scjsta:v:50:y:2023:i:4:p:1919-1932
    DOI: 10.1111/sjos.12658
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    References listed on IDEAS

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    1. Iván Díaz & Elizabeth Colantuoni & Daniel F. Hanley & Michael Rosenblum, 2019. "Improved precision in the analysis of randomized trials with survival outcomes, without assuming proportional hazards," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(3), pages 439-468, July.
    2. Thomas H. Scheike & Mei-Jie Zhang & Thomas A. Gerds, 2008. "Predicting cumulative incidence probability by direct binomial regression," Biometrika, Biometrika Trust, vol. 95(1), pages 205-220.
    3. Paul Frédéric Blanche & Anders Holt & Thomas Scheike, 2023. "On logistic regression with right censored data, with or without competing risks, and its use for estimating treatment effects," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(2), pages 441-482, April.
    4. 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.
    5. 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.
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