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Estimation in multi-arm two-stage trials with treatment selection and time-to-event endpoint

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Estimation in multi-arm two-stage trials with treatment selection and time-to-event endpoint. / Brueckner, Matthias; Titman, Andrew Charles; Jaki, Thomas Friedrich.
In: Statistics in Medicine, Vol. 36, No. 20, 10.09.2017, p. 3137-3153.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Brueckner, M, Titman, AC & Jaki, TF 2017, 'Estimation in multi-arm two-stage trials with treatment selection and time-to-event endpoint', Statistics in Medicine, vol. 36, no. 20, pp. 3137-3153. https://doi.org/10.1002/sim.7367

APA

Brueckner, M., Titman, A. C., & Jaki, T. F. (2017). Estimation in multi-arm two-stage trials with treatment selection and time-to-event endpoint. Statistics in Medicine, 36(20), 3137-3153. https://doi.org/10.1002/sim.7367

Vancouver

Brueckner M, Titman AC, Jaki TF. Estimation in multi-arm two-stage trials with treatment selection and time-to-event endpoint. Statistics in Medicine. 2017 Sept 10;36(20):3137-3153. Epub 2017 Jun 13. doi: 10.1002/sim.7367

Author

Brueckner, Matthias ; Titman, Andrew Charles ; Jaki, Thomas Friedrich. / Estimation in multi-arm two-stage trials with treatment selection and time-to-event endpoint. In: Statistics in Medicine. 2017 ; Vol. 36, No. 20. pp. 3137-3153.

Bibtex

@article{db9743ead31e4d0fa7b24a6e092c66f0,
title = "Estimation in multi-arm two-stage trials with treatment selection and time-to-event endpoint",
abstract = "We consider estimation of treatment effects in two-stage adaptive multi-arm trials with a common control. The best treatment is selected at interim, and the primary endpoint is modeled via a Cox proportional hazards model. The maximum partial-likelihood estimator of the log hazard ratio of the selected treatment will overestimate the true treatment effect in this case. Several methods for reducing the selection bias have been proposed for normal endpoints, including an iterative method based on the estimated conditional selection biases and a shrinkage approach based on empirical Bayes theory. We adapt these methods to time-to-event data and compare the bias and mean squared error of all methods in an extensive simulation study and apply the proposed methods to reconstructed data from the FOCUS trial. We find that all methods tend to overcorrect the bias, and only the shrinkage methods can reduce the mean squared error.",
author = "Matthias Brueckner and Titman, {Andrew Charles} and Jaki, {Thomas Friedrich}",
year = "2017",
month = sep,
day = "10",
doi = "10.1002/sim.7367",
language = "English",
volume = "36",
pages = "3137--3153",
journal = "Statistics in Medicine",
issn = "0277-6715",
publisher = "John Wiley and Sons Ltd",
number = "20",

}

RIS

TY - JOUR

T1 - Estimation in multi-arm two-stage trials with treatment selection and time-to-event endpoint

AU - Brueckner, Matthias

AU - Titman, Andrew Charles

AU - Jaki, Thomas Friedrich

PY - 2017/9/10

Y1 - 2017/9/10

N2 - We consider estimation of treatment effects in two-stage adaptive multi-arm trials with a common control. The best treatment is selected at interim, and the primary endpoint is modeled via a Cox proportional hazards model. The maximum partial-likelihood estimator of the log hazard ratio of the selected treatment will overestimate the true treatment effect in this case. Several methods for reducing the selection bias have been proposed for normal endpoints, including an iterative method based on the estimated conditional selection biases and a shrinkage approach based on empirical Bayes theory. We adapt these methods to time-to-event data and compare the bias and mean squared error of all methods in an extensive simulation study and apply the proposed methods to reconstructed data from the FOCUS trial. We find that all methods tend to overcorrect the bias, and only the shrinkage methods can reduce the mean squared error.

AB - We consider estimation of treatment effects in two-stage adaptive multi-arm trials with a common control. The best treatment is selected at interim, and the primary endpoint is modeled via a Cox proportional hazards model. The maximum partial-likelihood estimator of the log hazard ratio of the selected treatment will overestimate the true treatment effect in this case. Several methods for reducing the selection bias have been proposed for normal endpoints, including an iterative method based on the estimated conditional selection biases and a shrinkage approach based on empirical Bayes theory. We adapt these methods to time-to-event data and compare the bias and mean squared error of all methods in an extensive simulation study and apply the proposed methods to reconstructed data from the FOCUS trial. We find that all methods tend to overcorrect the bias, and only the shrinkage methods can reduce the mean squared error.

U2 - 10.1002/sim.7367

DO - 10.1002/sim.7367

M3 - Journal article

VL - 36

SP - 3137

EP - 3153

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 0277-6715

IS - 20

ER -