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Optimizing subgroup selection in two-stage adaptive enrichment and umbrella designs

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Optimizing subgroup selection in two-stage adaptive enrichment and umbrella designs. / Ballarini, N.M.; Burnett, T.; Jaki, T. et al.
In: Statistics in Medicine, Vol. 40, No. 12, 31.05.2021, p. 2939-2956.

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

Harvard

Ballarini, NM, Burnett, T, Jaki, T, Jennison, C, König, F & Posch, M 2021, 'Optimizing subgroup selection in two-stage adaptive enrichment and umbrella designs', Statistics in Medicine, vol. 40, no. 12, pp. 2939-2956. https://doi.org/10.1002/sim.8949

APA

Ballarini, N. M., Burnett, T., Jaki, T., Jennison, C., König, F., & Posch, M. (2021). Optimizing subgroup selection in two-stage adaptive enrichment and umbrella designs. Statistics in Medicine, 40(12), 2939-2956. https://doi.org/10.1002/sim.8949

Vancouver

Ballarini NM, Burnett T, Jaki T, Jennison C, König F, Posch M. Optimizing subgroup selection in two-stage adaptive enrichment and umbrella designs. Statistics in Medicine. 2021 May 31;40(12):2939-2956. Epub 2021 Mar 29. doi: 10.1002/sim.8949

Author

Ballarini, N.M. ; Burnett, T. ; Jaki, T. et al. / Optimizing subgroup selection in two-stage adaptive enrichment and umbrella designs. In: Statistics in Medicine. 2021 ; Vol. 40, No. 12. pp. 2939-2956.

Bibtex

@article{e8bc3e0d2c3a42f9be24323343fd61ab,
title = "Optimizing subgroup selection in two-stage adaptive enrichment and umbrella designs",
abstract = "We design two-stage confirmatory clinical trials that use adaptation to find the subgroup of patients who will benefit from a new treatment, testing for a treatment effect in each of two disjoint subgroups. Our proposal allows aspects of the trial, such as recruitment probabilities of each group, to be altered at an interim analysis. We use the conditional error rate approach to implement these adaptations with protection of overall error rates. Applying a Bayesian decision-theoretic framework, we optimize design parameters by maximizing a utility function that takes the population prevalence of the subgroups into account. We show results for traditional trials with familywise error rate control (using a closed testing procedure) as well as for umbrella trials in which only the per-comparison type 1 error rate is controlled. We present numerical examples to illustrate the optimization process and the effectiveness of the proposed designs.  ",
keywords = "Bayesian optimization, conditional error function, subgroup analysis, utility function, article, Bayes theorem, controlled study, family-wise error rate, human, prevalence, theoretical study, utility value",
author = "N.M. Ballarini and T. Burnett and T. Jaki and C. Jennison and F. K{\"o}nig and M. Posch",
year = "2021",
month = may,
day = "31",
doi = "10.1002/sim.8949",
language = "English",
volume = "40",
pages = "2939--2956",
journal = "Statistics in Medicine",
issn = "0277-6715",
publisher = "John Wiley and Sons Ltd",
number = "12",

}

RIS

TY - JOUR

T1 - Optimizing subgroup selection in two-stage adaptive enrichment and umbrella designs

AU - Ballarini, N.M.

AU - Burnett, T.

AU - Jaki, T.

AU - Jennison, C.

AU - König, F.

AU - Posch, M.

PY - 2021/5/31

Y1 - 2021/5/31

N2 - We design two-stage confirmatory clinical trials that use adaptation to find the subgroup of patients who will benefit from a new treatment, testing for a treatment effect in each of two disjoint subgroups. Our proposal allows aspects of the trial, such as recruitment probabilities of each group, to be altered at an interim analysis. We use the conditional error rate approach to implement these adaptations with protection of overall error rates. Applying a Bayesian decision-theoretic framework, we optimize design parameters by maximizing a utility function that takes the population prevalence of the subgroups into account. We show results for traditional trials with familywise error rate control (using a closed testing procedure) as well as for umbrella trials in which only the per-comparison type 1 error rate is controlled. We present numerical examples to illustrate the optimization process and the effectiveness of the proposed designs.  

AB - We design two-stage confirmatory clinical trials that use adaptation to find the subgroup of patients who will benefit from a new treatment, testing for a treatment effect in each of two disjoint subgroups. Our proposal allows aspects of the trial, such as recruitment probabilities of each group, to be altered at an interim analysis. We use the conditional error rate approach to implement these adaptations with protection of overall error rates. Applying a Bayesian decision-theoretic framework, we optimize design parameters by maximizing a utility function that takes the population prevalence of the subgroups into account. We show results for traditional trials with familywise error rate control (using a closed testing procedure) as well as for umbrella trials in which only the per-comparison type 1 error rate is controlled. We present numerical examples to illustrate the optimization process and the effectiveness of the proposed designs.  

KW - Bayesian optimization

KW - conditional error function

KW - subgroup analysis

KW - utility function

KW - article

KW - Bayes theorem

KW - controlled study

KW - family-wise error rate

KW - human

KW - prevalence

KW - theoretical study

KW - utility value

U2 - 10.1002/sim.8949

DO - 10.1002/sim.8949

M3 - Journal article

VL - 40

SP - 2939

EP - 2956

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 0277-6715

IS - 12

ER -