Home > Research > Publications & Outputs > D-optimal designs for multiarm trials with drop...

Associated organisational unit

Links

Text available via DOI:

View graph of relations

D-optimal designs for multiarm trials with dropouts

Research output: Contribution to journalJournal article

Published

Standard

D-optimal designs for multiarm trials with dropouts. / Lee, K.M.; Biedermann, S.; Mitra, R.

In: Statistics in Medicine, Vol. 38, No. 15, 01.07.2019, p. 2749-2766.

Research output: Contribution to journalJournal article

Harvard

Lee, KM, Biedermann, S & Mitra, R 2019, 'D-optimal designs for multiarm trials with dropouts', Statistics in Medicine, vol. 38, no. 15, pp. 2749-2766. https://doi.org/10.1002/sim.8148

APA

Lee, K. M., Biedermann, S., & Mitra, R. (2019). D-optimal designs for multiarm trials with dropouts. Statistics in Medicine, 38(15), 2749-2766. https://doi.org/10.1002/sim.8148

Vancouver

Lee KM, Biedermann S, Mitra R. D-optimal designs for multiarm trials with dropouts. Statistics in Medicine. 2019 Jul 1;38(15):2749-2766. https://doi.org/10.1002/sim.8148

Author

Lee, K.M. ; Biedermann, S. ; Mitra, R. / D-optimal designs for multiarm trials with dropouts. In: Statistics in Medicine. 2019 ; Vol. 38, No. 15. pp. 2749-2766.

Bibtex

@article{5c8817bc48e34484ade73698441ffc2a,
title = "D-optimal designs for multiarm trials with dropouts",
abstract = "Multiarm trials with follow-up on participants are commonly implemented to assess treatment effects on a population over the course of the studies. Dropout is an unavoidable issue especially when the duration of the multiarm study is long. Its impact is often ignored at the design stage, which may lead to less accurate statistical conclusions. We develop an optimal design framework for trials with repeated measurements, which takes potential dropouts into account, and we provide designs for linear mixed models where the presence of dropouts is noninformative and dependent on design variables. Our framework is illustrated through redesigning a clinical trial on Alzheimer's disease, whereby the benefits of our designs compared with standard designs are demonstrated through simulations. {\textcopyright} 2019 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.",
keywords = "available case analysis, design of experiments, linear mixed models, noninformative dropouts",
author = "K.M. Lee and S. Biedermann and R. Mitra",
year = "2019",
month = jul,
day = "1",
doi = "10.1002/sim.8148",
language = "English",
volume = "38",
pages = "2749--2766",
journal = "Statistics in Medicine",
issn = "0277-6715",
publisher = "John Wiley and Sons Ltd",
number = "15",

}

RIS

TY - JOUR

T1 - D-optimal designs for multiarm trials with dropouts

AU - Lee, K.M.

AU - Biedermann, S.

AU - Mitra, R.

PY - 2019/7/1

Y1 - 2019/7/1

N2 - Multiarm trials with follow-up on participants are commonly implemented to assess treatment effects on a population over the course of the studies. Dropout is an unavoidable issue especially when the duration of the multiarm study is long. Its impact is often ignored at the design stage, which may lead to less accurate statistical conclusions. We develop an optimal design framework for trials with repeated measurements, which takes potential dropouts into account, and we provide designs for linear mixed models where the presence of dropouts is noninformative and dependent on design variables. Our framework is illustrated through redesigning a clinical trial on Alzheimer's disease, whereby the benefits of our designs compared with standard designs are demonstrated through simulations. © 2019 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

AB - Multiarm trials with follow-up on participants are commonly implemented to assess treatment effects on a population over the course of the studies. Dropout is an unavoidable issue especially when the duration of the multiarm study is long. Its impact is often ignored at the design stage, which may lead to less accurate statistical conclusions. We develop an optimal design framework for trials with repeated measurements, which takes potential dropouts into account, and we provide designs for linear mixed models where the presence of dropouts is noninformative and dependent on design variables. Our framework is illustrated through redesigning a clinical trial on Alzheimer's disease, whereby the benefits of our designs compared with standard designs are demonstrated through simulations. © 2019 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

KW - available case analysis

KW - design of experiments

KW - linear mixed models

KW - noninformative dropouts

U2 - 10.1002/sim.8148

DO - 10.1002/sim.8148

M3 - Journal article

VL - 38

SP - 2749

EP - 2766

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 0277-6715

IS - 15

ER -