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Estimation of secondary endpoints in two-stage phase II oncology trials

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Estimation of secondary endpoints in two-stage phase II oncology trials. / Kunz, Cornelia Ursula; Kieser, Meinhard.
In: Statistics in Medicine, Vol. 31, No. 30, 30.12.2012, p. 4352-4368.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Kunz, CU & Kieser, M 2012, 'Estimation of secondary endpoints in two-stage phase II oncology trials', Statistics in Medicine, vol. 31, no. 30, pp. 4352-4368. https://doi.org/10.1002/sim.5585

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Vancouver

Kunz CU, Kieser M. Estimation of secondary endpoints in two-stage phase II oncology trials. Statistics in Medicine. 2012 Dec 30;31(30):4352-4368. Epub 2012 Aug 29. doi: 10.1002/sim.5585

Author

Kunz, Cornelia Ursula ; Kieser, Meinhard. / Estimation of secondary endpoints in two-stage phase II oncology trials. In: Statistics in Medicine. 2012 ; Vol. 31, No. 30. pp. 4352-4368.

Bibtex

@article{625f539d32634530986f082b07624ef9,
title = "Estimation of secondary endpoints in two-stage phase II oncology trials",
abstract = "In the development of a new treatment in oncology, phase II trials play a key role. On the basis of the data obtained during phase II, it is decided whether the treatment should be studied further. Therefore, the decision to be made on the basis of the data of a phase II trial must be as accurate as possible. For ethical and economic reasons, phase II trials are usually performed with a planned interim analysis. Furthermore, the decision about stopping or continuing the study is usually based on a short-term outcome like tumor response, whereas secondary endpoints comprise stable disease, progressive disease, toxicity, and/or overall survival. The data obtained in a phase II trial are often analyzed and interpreted by applying the maximum likelihood estimator (MLE) without taking into account the sequential nature of the trial. However, this approach provides biased results and may therefore lead to wrong conclusions. Whereas unbiased estimators for two-stage designs have been derived for the primary endpoint, such estimators are currently not available for secondary endpoints. We present uniformly minimum variance unbiased estimators (UMVUE) for secondary endpoints in two-stage designs that allow stopping for futility (and efficacy). We compare the mean squared error of the UMVUE and the MLE and investigate the efficiency of the UMVUE. A clinical trial example illustrates the application.",
keywords = "Bias (Epidemiology), Clinical Trials, Phase II as Topic, Endpoint Determination, Epidemiologic Research Design, Humans, Medical Oncology",
author = "Kunz, {Cornelia Ursula} and Meinhard Kieser",
note = " Copyright {\textcopyright} 2012 John Wiley & Sons, Ltd.",
year = "2012",
month = dec,
day = "30",
doi = "10.1002/sim.5585",
language = "English",
volume = "31",
pages = "4352--4368",
journal = "Statistics in Medicine",
issn = "0277-6715",
publisher = "John Wiley and Sons Ltd",
number = "30",

}

RIS

TY - JOUR

T1 - Estimation of secondary endpoints in two-stage phase II oncology trials

AU - Kunz, Cornelia Ursula

AU - Kieser, Meinhard

N1 - Copyright © 2012 John Wiley & Sons, Ltd.

PY - 2012/12/30

Y1 - 2012/12/30

N2 - In the development of a new treatment in oncology, phase II trials play a key role. On the basis of the data obtained during phase II, it is decided whether the treatment should be studied further. Therefore, the decision to be made on the basis of the data of a phase II trial must be as accurate as possible. For ethical and economic reasons, phase II trials are usually performed with a planned interim analysis. Furthermore, the decision about stopping or continuing the study is usually based on a short-term outcome like tumor response, whereas secondary endpoints comprise stable disease, progressive disease, toxicity, and/or overall survival. The data obtained in a phase II trial are often analyzed and interpreted by applying the maximum likelihood estimator (MLE) without taking into account the sequential nature of the trial. However, this approach provides biased results and may therefore lead to wrong conclusions. Whereas unbiased estimators for two-stage designs have been derived for the primary endpoint, such estimators are currently not available for secondary endpoints. We present uniformly minimum variance unbiased estimators (UMVUE) for secondary endpoints in two-stage designs that allow stopping for futility (and efficacy). We compare the mean squared error of the UMVUE and the MLE and investigate the efficiency of the UMVUE. A clinical trial example illustrates the application.

AB - In the development of a new treatment in oncology, phase II trials play a key role. On the basis of the data obtained during phase II, it is decided whether the treatment should be studied further. Therefore, the decision to be made on the basis of the data of a phase II trial must be as accurate as possible. For ethical and economic reasons, phase II trials are usually performed with a planned interim analysis. Furthermore, the decision about stopping or continuing the study is usually based on a short-term outcome like tumor response, whereas secondary endpoints comprise stable disease, progressive disease, toxicity, and/or overall survival. The data obtained in a phase II trial are often analyzed and interpreted by applying the maximum likelihood estimator (MLE) without taking into account the sequential nature of the trial. However, this approach provides biased results and may therefore lead to wrong conclusions. Whereas unbiased estimators for two-stage designs have been derived for the primary endpoint, such estimators are currently not available for secondary endpoints. We present uniformly minimum variance unbiased estimators (UMVUE) for secondary endpoints in two-stage designs that allow stopping for futility (and efficacy). We compare the mean squared error of the UMVUE and the MLE and investigate the efficiency of the UMVUE. A clinical trial example illustrates the application.

KW - Bias (Epidemiology)

KW - Clinical Trials, Phase II as Topic

KW - Endpoint Determination

KW - Epidemiologic Research Design

KW - Humans

KW - Medical Oncology

U2 - 10.1002/sim.5585

DO - 10.1002/sim.5585

M3 - Journal article

C2 - 22930470

VL - 31

SP - 4352

EP - 4368

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 0277-6715

IS - 30

ER -