Efficient t0-year risk regression using the logistic model

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Standard

Efficient t0-year risk regression using the logistic model. / Martinussen, Torben; Harder Scheike, Thomas.

In: Scandinavian Journal of Statistics, Vol. 50, No. 4, 2023, p. 1919-1932.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Martinussen, T & Harder Scheike, T 2023, 'Efficient t0-year risk regression using the logistic model', Scandinavian Journal of Statistics, vol. 50, no. 4, pp. 1919-1932. https://doi.org/10.1111/sjos.12658

APA

Martinussen, T., & Harder Scheike, T. (2023). Efficient t0-year risk regression using the logistic model. Scandinavian Journal of Statistics, 50(4), 1919-1932. https://doi.org/10.1111/sjos.12658

Vancouver

Martinussen T, Harder Scheike T. Efficient t0-year risk regression using the logistic model. Scandinavian Journal of Statistics. 2023;50(4):1919-1932. https://doi.org/10.1111/sjos.12658

Author

Martinussen, Torben ; Harder Scheike, Thomas. / Efficient t0-year risk regression using the logistic model. In: Scandinavian Journal of Statistics. 2023 ; Vol. 50, No. 4. pp. 1919-1932.

Bibtex

@article{cb4eda5373d5401ca6e777a5a03f4e7b,
title = "Efficient t0-year risk regression using the logistic model",
abstract = "In some clinical studies patient survival beyond a specific point in time, t(0), say, maybe of special interest as it may for instance indicate patient cure. To analyze the 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.",
keywords = "augmentation, censoring, double robustness, efficient estimation, fixed time regression, inverse probability of censoring weighting, t(0)-year risk, PSEUDO-OBSERVATIONS, CURVES",
author = "Torben Martinussen and {Harder Scheike}, Thomas",
year = "2023",
doi = "10.1111/sjos.12658",
language = "English",
volume = "50",
pages = "1919--1932",
journal = "Scandinavian Journal of Statistics",
issn = "0303-6898",
publisher = "Wiley-Blackwell",
number = "4",

}

RIS

TY - JOUR

T1 - Efficient t0-year risk regression using the logistic model

AU - Martinussen, Torben

AU - Harder Scheike, Thomas

PY - 2023

Y1 - 2023

N2 - In some clinical studies patient survival beyond a specific point in time, t(0), say, maybe of special interest as it may for instance indicate patient cure. To analyze the 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.

AB - In some clinical studies patient survival beyond a specific point in time, t(0), say, maybe of special interest as it may for instance indicate patient cure. To analyze the 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.

KW - augmentation

KW - censoring

KW - double robustness

KW - efficient estimation

KW - fixed time regression

KW - inverse probability of censoring weighting

KW - t(0)-year risk

KW - PSEUDO-OBSERVATIONS

KW - CURVES

U2 - 10.1111/sjos.12658

DO - 10.1111/sjos.12658

M3 - Journal article

VL - 50

SP - 1919

EP - 1932

JO - Scandinavian Journal of Statistics

JF - Scandinavian Journal of Statistics

SN - 0303-6898

IS - 4

ER -

ID: 346696764