One-step targeted maximum likelihood estimation for targeting cause-specific absolute risks and survival curves

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One-step targeted maximum likelihood estimation for targeting cause-specific absolute risks and survival curves. / Rytgaard, H C W; Van Der Laan, M. J.

In: Biometrika, Vol. 111, No. 1, 2024, p. 129–145.

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

Harvard

Rytgaard, HCW & Van Der Laan, MJ 2024, 'One-step targeted maximum likelihood estimation for targeting cause-specific absolute risks and survival curves', Biometrika, vol. 111, no. 1, pp. 129–145. https://doi.org/10.1093/biomet/asad033

APA

Rytgaard, H. C. W., & Van Der Laan, M. J. (2024). One-step targeted maximum likelihood estimation for targeting cause-specific absolute risks and survival curves. Biometrika, 111(1), 129–145. https://doi.org/10.1093/biomet/asad033

Vancouver

Rytgaard HCW, Van Der Laan MJ. One-step targeted maximum likelihood estimation for targeting cause-specific absolute risks and survival curves. Biometrika. 2024;111(1):129–145. https://doi.org/10.1093/biomet/asad033

Author

Rytgaard, H C W ; Van Der Laan, M. J. / One-step targeted maximum likelihood estimation for targeting cause-specific absolute risks and survival curves. In: Biometrika. 2024 ; Vol. 111, No. 1. pp. 129–145.

Bibtex

@article{89f1d5d57df54895b1fc8eb8853b8f85,
title = "One-step targeted maximum likelihood estimation for targeting cause-specific absolute risks and survival curves",
abstract = "his paper considers the one-step targeted maximum likelihood estimation methodology for multi-dimensional causal parameters in general survival and competing risk settings where event times take place on the positive real line and are subject to right censoring. We focus on effects of baseline treatment decisions possibly confounded by pretreatment covariates, but remark that our work generalizes to settings with time-varying treatment regimes and time-dependent confounding. We point out two overall contributions of our work. First, our methods can be used to obtain simultaneous inference for treatment effects on multiple absolute risks in competing risk settings. Second, our methods can be used to achieve inference for the full survival curve, or a full absolute risk curve, across time. The one-step targeted maximum likelihood procedure is based on a one-dimensional universal least favourable submodel for each cause-specific hazard that we implement in recursive steps along a corresponding nonuniversal multivariate least favourable submodel. Our empirical study demonstrates the practical use of the methods.",
author = "Rytgaard, {H C W} and {Van Der Laan}, {M. J.}",
year = "2024",
doi = "10.1093/biomet/asad033",
language = "English",
volume = "111",
pages = "129–145",
journal = "Biometrika",
issn = "0006-3444",
publisher = "Oxford University Press",
number = "1",

}

RIS

TY - JOUR

T1 - One-step targeted maximum likelihood estimation for targeting cause-specific absolute risks and survival curves

AU - Rytgaard, H C W

AU - Van Der Laan, M. J.

PY - 2024

Y1 - 2024

N2 - his paper considers the one-step targeted maximum likelihood estimation methodology for multi-dimensional causal parameters in general survival and competing risk settings where event times take place on the positive real line and are subject to right censoring. We focus on effects of baseline treatment decisions possibly confounded by pretreatment covariates, but remark that our work generalizes to settings with time-varying treatment regimes and time-dependent confounding. We point out two overall contributions of our work. First, our methods can be used to obtain simultaneous inference for treatment effects on multiple absolute risks in competing risk settings. Second, our methods can be used to achieve inference for the full survival curve, or a full absolute risk curve, across time. The one-step targeted maximum likelihood procedure is based on a one-dimensional universal least favourable submodel for each cause-specific hazard that we implement in recursive steps along a corresponding nonuniversal multivariate least favourable submodel. Our empirical study demonstrates the practical use of the methods.

AB - his paper considers the one-step targeted maximum likelihood estimation methodology for multi-dimensional causal parameters in general survival and competing risk settings where event times take place on the positive real line and are subject to right censoring. We focus on effects of baseline treatment decisions possibly confounded by pretreatment covariates, but remark that our work generalizes to settings with time-varying treatment regimes and time-dependent confounding. We point out two overall contributions of our work. First, our methods can be used to obtain simultaneous inference for treatment effects on multiple absolute risks in competing risk settings. Second, our methods can be used to achieve inference for the full survival curve, or a full absolute risk curve, across time. The one-step targeted maximum likelihood procedure is based on a one-dimensional universal least favourable submodel for each cause-specific hazard that we implement in recursive steps along a corresponding nonuniversal multivariate least favourable submodel. Our empirical study demonstrates the practical use of the methods.

U2 - 10.1093/biomet/asad033

DO - 10.1093/biomet/asad033

M3 - Journal article

VL - 111

SP - 129

EP - 145

JO - Biometrika

JF - Biometrika

SN - 0006-3444

IS - 1

ER -

ID: 365529611