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Interim analysis incorporating short- and long-term binary endpoints

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Interim analysis incorporating short- and long-term binary endpoints. / Niewczas, J.; Kunz, C.U.; König, F.
In: Biometrical Journal, Vol. 61, No. 3 (Special Issue: ISCB38, Part II), 01.05.2019, p. 665-687.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Niewczas, J, Kunz, CU & König, F 2019, 'Interim analysis incorporating short- and long-term binary endpoints', Biometrical Journal, vol. 61, no. 3 (Special Issue: ISCB38, Part II), pp. 665-687. https://doi.org/10.1002/bimj.201700281

APA

Niewczas, J., Kunz, C. U., & König, F. (2019). Interim analysis incorporating short- and long-term binary endpoints. Biometrical Journal, 61(3 (Special Issue: ISCB38, Part II)), 665-687. https://doi.org/10.1002/bimj.201700281

Vancouver

Niewczas J, Kunz CU, König F. Interim analysis incorporating short- and long-term binary endpoints. Biometrical Journal. 2019 May 1;61(3 (Special Issue: ISCB38, Part II)):665-687. Epub 2019 Jan 29. doi: 10.1002/bimj.201700281

Author

Niewczas, J. ; Kunz, C.U. ; König, F. / Interim analysis incorporating short- and long-term binary endpoints. In: Biometrical Journal. 2019 ; Vol. 61, No. 3 (Special Issue: ISCB38, Part II). pp. 665-687.

Bibtex

@article{080a74d5d76c4d5fbb33d58d2c506482,
title = "Interim analysis incorporating short- and long-term binary endpoints",
abstract = "Designs incorporating more than one endpoint have become popular in drug development. One of such designs allows for incorporation of short-term information in an interim analysis if the long-term primary endpoint has not been yet observed for some of the patients. At first we consider a two-stage design with binary endpoints allowing for futility stopping only based on conditional power under both fixed and observed effects. Design characteristics of three estimators: using primary long-term endpoint only, short-term endpoint only, and combining data from both are compared. For each approach, equivalent cut-off point values for fixed and observed effect conditional power calculations can be derived resulting in the same overall power. While in trials stopping for futility the type I error rate cannot get inflated (it usually decreases), there is loss of power. In this study, we consider different scenarios, including different thresholds for conditional power, different amount of information available at the interim, different correlations and probabilities of success. We further extend the methods to adaptive designs with unblinded sample size reassessments based on conditional power with inverse normal method as the combination function. Two different futility stopping rules are considered: one based on the conditional power, and one from P-values based on Z-statistics of the estimators. Average sample size, probability to stop for futility and overall power of the trial are compared and the influence of the choice of weights is investigated.",
keywords = "adaptive designs, combination test, conditional power, futility stopping, sample size reassessment",
author = "J. Niewczas and C.U. Kunz and F. K{\"o}nig",
year = "2019",
month = may,
day = "1",
doi = "10.1002/bimj.201700281",
language = "English",
volume = "61",
pages = "665--687",
journal = "Biometrical Journal",
issn = "0323-3847",
publisher = "Wiley-VCH Verlag",
number = "3 (Special Issue: ISCB38, Part II)",

}

RIS

TY - JOUR

T1 - Interim analysis incorporating short- and long-term binary endpoints

AU - Niewczas, J.

AU - Kunz, C.U.

AU - König, F.

PY - 2019/5/1

Y1 - 2019/5/1

N2 - Designs incorporating more than one endpoint have become popular in drug development. One of such designs allows for incorporation of short-term information in an interim analysis if the long-term primary endpoint has not been yet observed for some of the patients. At first we consider a two-stage design with binary endpoints allowing for futility stopping only based on conditional power under both fixed and observed effects. Design characteristics of three estimators: using primary long-term endpoint only, short-term endpoint only, and combining data from both are compared. For each approach, equivalent cut-off point values for fixed and observed effect conditional power calculations can be derived resulting in the same overall power. While in trials stopping for futility the type I error rate cannot get inflated (it usually decreases), there is loss of power. In this study, we consider different scenarios, including different thresholds for conditional power, different amount of information available at the interim, different correlations and probabilities of success. We further extend the methods to adaptive designs with unblinded sample size reassessments based on conditional power with inverse normal method as the combination function. Two different futility stopping rules are considered: one based on the conditional power, and one from P-values based on Z-statistics of the estimators. Average sample size, probability to stop for futility and overall power of the trial are compared and the influence of the choice of weights is investigated.

AB - Designs incorporating more than one endpoint have become popular in drug development. One of such designs allows for incorporation of short-term information in an interim analysis if the long-term primary endpoint has not been yet observed for some of the patients. At first we consider a two-stage design with binary endpoints allowing for futility stopping only based on conditional power under both fixed and observed effects. Design characteristics of three estimators: using primary long-term endpoint only, short-term endpoint only, and combining data from both are compared. For each approach, equivalent cut-off point values for fixed and observed effect conditional power calculations can be derived resulting in the same overall power. While in trials stopping for futility the type I error rate cannot get inflated (it usually decreases), there is loss of power. In this study, we consider different scenarios, including different thresholds for conditional power, different amount of information available at the interim, different correlations and probabilities of success. We further extend the methods to adaptive designs with unblinded sample size reassessments based on conditional power with inverse normal method as the combination function. Two different futility stopping rules are considered: one based on the conditional power, and one from P-values based on Z-statistics of the estimators. Average sample size, probability to stop for futility and overall power of the trial are compared and the influence of the choice of weights is investigated.

KW - adaptive designs

KW - combination test

KW - conditional power

KW - futility stopping

KW - sample size reassessment

U2 - 10.1002/bimj.201700281

DO - 10.1002/bimj.201700281

M3 - Journal article

VL - 61

SP - 665

EP - 687

JO - Biometrical Journal

JF - Biometrical Journal

SN - 0323-3847

IS - 3 (Special Issue: ISCB38, Part II)

ER -