The asymptotic distribution of the Net Benefit estimator in presence of right-censoring

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Standard

The asymptotic distribution of the Net Benefit estimator in presence of right-censoring. / Ozenne, Brice; Budtz-Jorgensen, Esben; Peron, Julien.

In: Statistical Methods in Medical Research, Vol. 30, No. 11, 2021, p. 2399-2412.

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

Harvard

Ozenne, B, Budtz-Jorgensen, E & Peron, J 2021, 'The asymptotic distribution of the Net Benefit estimator in presence of right-censoring', Statistical Methods in Medical Research, vol. 30, no. 11, pp. 2399-2412. https://doi.org/10.1177/09622802211037067

APA

Ozenne, B., Budtz-Jorgensen, E., & Peron, J. (2021). The asymptotic distribution of the Net Benefit estimator in presence of right-censoring. Statistical Methods in Medical Research, 30(11), 2399-2412. https://doi.org/10.1177/09622802211037067

Vancouver

Ozenne B, Budtz-Jorgensen E, Peron J. The asymptotic distribution of the Net Benefit estimator in presence of right-censoring. Statistical Methods in Medical Research. 2021;30(11):2399-2412. https://doi.org/10.1177/09622802211037067

Author

Ozenne, Brice ; Budtz-Jorgensen, Esben ; Peron, Julien. / The asymptotic distribution of the Net Benefit estimator in presence of right-censoring. In: Statistical Methods in Medical Research. 2021 ; Vol. 30, No. 11. pp. 2399-2412.

Bibtex

@article{843f81b2055d4ff09e48dca63902d063,
title = "The asymptotic distribution of the Net Benefit estimator in presence of right-censoring",
abstract = "The benefit–risk balance is a critical information when evaluating a new treatment. The Net Benefit has been proposed as a metric for the benefit–risk assessment, and applied in oncology to simultaneously consider gains in survival and possible side effects of chemotherapies. With complete data, one can construct a U-statistic estimator for the Net Benefit and obtain its asymptotic distribution using standard results of the U-statistic theory. However, real data is often subject to right-censoring, e.g. patient drop-out in clinical trials. It is then possible to estimate the Net Benefit using a modified U-statistic, which involves the survival time. The latter can be seen as a nuisance parameter affecting the asymptotic distribution of the Net Benefit estimator. We present here how existing asymptotic results on U-statistics can be applied to estimate the distribution of the net benefit estimator, and assess their validity in finite samples. The methodology generalizes to other statistics obtained using generalized pairwise comparisons, such as the win ratio. It is implemented in the R package BuyseTest (version 2.3.0 and later) available on Comprehensive R Archive Network.",
keywords = "Clinical trial, multivariate analysis, censoring, statistical inference, non-parametric statistics",
author = "Brice Ozenne and Esben Budtz-Jorgensen and Julien Peron",
year = "2021",
doi = "10.1177/09622802211037067",
language = "English",
volume = "30",
pages = "2399--2412",
journal = "Statistical Methods in Medical Research",
issn = "0962-2802",
publisher = "SAGE Publications",
number = "11",

}

RIS

TY - JOUR

T1 - The asymptotic distribution of the Net Benefit estimator in presence of right-censoring

AU - Ozenne, Brice

AU - Budtz-Jorgensen, Esben

AU - Peron, Julien

PY - 2021

Y1 - 2021

N2 - The benefit–risk balance is a critical information when evaluating a new treatment. The Net Benefit has been proposed as a metric for the benefit–risk assessment, and applied in oncology to simultaneously consider gains in survival and possible side effects of chemotherapies. With complete data, one can construct a U-statistic estimator for the Net Benefit and obtain its asymptotic distribution using standard results of the U-statistic theory. However, real data is often subject to right-censoring, e.g. patient drop-out in clinical trials. It is then possible to estimate the Net Benefit using a modified U-statistic, which involves the survival time. The latter can be seen as a nuisance parameter affecting the asymptotic distribution of the Net Benefit estimator. We present here how existing asymptotic results on U-statistics can be applied to estimate the distribution of the net benefit estimator, and assess their validity in finite samples. The methodology generalizes to other statistics obtained using generalized pairwise comparisons, such as the win ratio. It is implemented in the R package BuyseTest (version 2.3.0 and later) available on Comprehensive R Archive Network.

AB - The benefit–risk balance is a critical information when evaluating a new treatment. The Net Benefit has been proposed as a metric for the benefit–risk assessment, and applied in oncology to simultaneously consider gains in survival and possible side effects of chemotherapies. With complete data, one can construct a U-statistic estimator for the Net Benefit and obtain its asymptotic distribution using standard results of the U-statistic theory. However, real data is often subject to right-censoring, e.g. patient drop-out in clinical trials. It is then possible to estimate the Net Benefit using a modified U-statistic, which involves the survival time. The latter can be seen as a nuisance parameter affecting the asymptotic distribution of the Net Benefit estimator. We present here how existing asymptotic results on U-statistics can be applied to estimate the distribution of the net benefit estimator, and assess their validity in finite samples. The methodology generalizes to other statistics obtained using generalized pairwise comparisons, such as the win ratio. It is implemented in the R package BuyseTest (version 2.3.0 and later) available on Comprehensive R Archive Network.

KW - Clinical trial

KW - multivariate analysis

KW - censoring

KW - statistical inference

KW - non-parametric statistics

U2 - 10.1177/09622802211037067

DO - 10.1177/09622802211037067

M3 - Journal article

C2 - 34633267

VL - 30

SP - 2399

EP - 2412

JO - Statistical Methods in Medical Research

JF - Statistical Methods in Medical Research

SN - 0962-2802

IS - 11

ER -

ID: 282599635