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Optimal design of multi-arm multi-stage trials

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Optimal design of multi-arm multi-stage trials. / Wason, James; Jaki, Thomas.
In: Statistics in Medicine, Vol. 31, No. 30, 30.12.2012, p. 4269-4279.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Wason, J & Jaki, T 2012, 'Optimal design of multi-arm multi-stage trials', Statistics in Medicine, vol. 31, no. 30, pp. 4269-4279. https://doi.org/10.1002/sim.5513

APA

Wason, J., & Jaki, T. (2012). Optimal design of multi-arm multi-stage trials. Statistics in Medicine, 31(30), 4269-4279. https://doi.org/10.1002/sim.5513

Vancouver

Wason J, Jaki T. Optimal design of multi-arm multi-stage trials. Statistics in Medicine. 2012 Dec 30;31(30):4269-4279. doi: 10.1002/sim.5513

Author

Wason, James ; Jaki, Thomas. / Optimal design of multi-arm multi-stage trials. In: Statistics in Medicine. 2012 ; Vol. 31, No. 30. pp. 4269-4279.

Bibtex

@article{0a25058c3122411c911f5f06d95ff2be,
title = "Optimal design of multi-arm multi-stage trials",
abstract = "In drug development, there is often uncertainty about the most promising among a set of different treatments. Multi-arm multi-stage (MAMS) trials provide large gains in efficiency over separate randomised trials of each treatment. They allow a shared control group, dropping of ineffective treatments before the end of the trial and stopping the trial early if sufficient evidence of a treatment being superior to control is found. In this paper, we discuss optimal design of MAMS trials. An optimal design has the required type I error rate and power but minimises the expected sample size at some set of treatment effects. Finding an optimal design requires searching over stopping boundaries and sample size, potentially a large number of parameters. We propose a method that combines quick evaluation of specific designs and an efficient stochastic search to find the optimal design parameters. We compare various potential designs motivated by the design of a phase II MAMS trial. We also consider allocating more patients to the control group, as has been carried out in real MAMS studies. We show that the optimal allocation to the control group, although greater than a 1:1 ratio, is smaller than previously advocated and that the gain in efficiency is generally small. ",
keywords = "multi-arm multi-stage trials, optimal allocation, optimal design, simulated annealing",
author = "James Wason and Thomas Jaki",
year = "2012",
month = dec,
day = "30",
doi = "10.1002/sim.5513",
language = "English",
volume = "31",
pages = "4269--4279",
journal = "Statistics in Medicine",
issn = "1097-0258",
publisher = "John Wiley and Sons Ltd",
number = "30",

}

RIS

TY - JOUR

T1 - Optimal design of multi-arm multi-stage trials

AU - Wason, James

AU - Jaki, Thomas

PY - 2012/12/30

Y1 - 2012/12/30

N2 - In drug development, there is often uncertainty about the most promising among a set of different treatments. Multi-arm multi-stage (MAMS) trials provide large gains in efficiency over separate randomised trials of each treatment. They allow a shared control group, dropping of ineffective treatments before the end of the trial and stopping the trial early if sufficient evidence of a treatment being superior to control is found. In this paper, we discuss optimal design of MAMS trials. An optimal design has the required type I error rate and power but minimises the expected sample size at some set of treatment effects. Finding an optimal design requires searching over stopping boundaries and sample size, potentially a large number of parameters. We propose a method that combines quick evaluation of specific designs and an efficient stochastic search to find the optimal design parameters. We compare various potential designs motivated by the design of a phase II MAMS trial. We also consider allocating more patients to the control group, as has been carried out in real MAMS studies. We show that the optimal allocation to the control group, although greater than a 1:1 ratio, is smaller than previously advocated and that the gain in efficiency is generally small.

AB - In drug development, there is often uncertainty about the most promising among a set of different treatments. Multi-arm multi-stage (MAMS) trials provide large gains in efficiency over separate randomised trials of each treatment. They allow a shared control group, dropping of ineffective treatments before the end of the trial and stopping the trial early if sufficient evidence of a treatment being superior to control is found. In this paper, we discuss optimal design of MAMS trials. An optimal design has the required type I error rate and power but minimises the expected sample size at some set of treatment effects. Finding an optimal design requires searching over stopping boundaries and sample size, potentially a large number of parameters. We propose a method that combines quick evaluation of specific designs and an efficient stochastic search to find the optimal design parameters. We compare various potential designs motivated by the design of a phase II MAMS trial. We also consider allocating more patients to the control group, as has been carried out in real MAMS studies. We show that the optimal allocation to the control group, although greater than a 1:1 ratio, is smaller than previously advocated and that the gain in efficiency is generally small.

KW - multi-arm multi-stage trials

KW - optimal allocation, optimal design

KW - simulated annealing

U2 - 10.1002/sim.5513

DO - 10.1002/sim.5513

M3 - Journal article

VL - 31

SP - 4269

EP - 4279

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 1097-0258

IS - 30

ER -