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Implementing Unequal Randomization in Clinical Trials with Heterogeneous Treatment Costs
Early Development Biostatistics, Novartis Institutes for Biomedical Research .ORCID iD: 0000-0002-1626-2588
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Mathematics, Applied Mathematics and Statistics. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
2019 (English)In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 38, no 16, p. 2905-2927Article in journal (Refereed) Published
Abstract [en]

Equal randomization has been a popular choice in clinical trial practice. However, in trials with heterogeneous variances and/or variable treatment costs, as well as in the settings where maximization of every trial participant’s benefit is an important design consideration, optimal allocation proportions may be unequal across study treatment arms. In this paper, we investigate optimal allocation designs minimizing study cost under statistical efficiency constraints for parallel group clinical trials comparing several investigational treatments against the control. We show theoretically that equal allocation designs may be suboptimal, and unequal allocation designs can provide higher statistical power for the same budget, or result in a smaller cost for the same level of power. We also show how the optimal allocation can be implemented in practice by means of restricted randomization procedures, and how to perform statistical inference following these procedures, using invoked population-based or randomization-based approaches. Our results provide further support to some previous findings in the literature that unequal randomization designs can be cost-efficient and can be successfully implemented in practice. We conclude that the choice of the target allocation, the randomization procedure and the statistical methodology for data analysis are essential components to ensure valid, powerful, and robust clinical trial results.

Place, publisher, year, edition, pages
2019. Vol. 38, no 16, p. 2905-2927
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:uu:diva-377608DOI: 10.1002/sim.8160ISI: 000473655400001PubMedID: 31049999OAI: oai:DiVA.org:uu-377608DiVA, id: diva2:1291147
Available from: 2019-02-22 Created: 2019-02-22 Last updated: 2019-08-16Bibliographically approved
In thesis
1. Optimal adaptive designs and adaptive randomization techniques for clinical trials
Open this publication in new window or tab >>Optimal adaptive designs and adaptive randomization techniques for clinical trials
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

In this Ph.D. thesis, we investigate how to optimize the design of clinical trials by constructing optimal adaptive designs, and how to implement the design by adaptive randomization. The results of the thesis are summarized by four research papers preceded by three chapters: an introduction, a short summary of the results obtained, and possible topics for future work.

In Paper I, we investigate the structure of a D-optimal design for dose-finding studies with censored time-to-event outcomes. We show that the D-optimal design can be much more efficient than uniform allocation design for the parameter estimation. The D-optimal design obtained depends on true parameters of the dose-response model, so it is a locally D-optimal design. We construct two-stage and multi-stage adaptive designs as approximations of  the D-optimal design when prior information about model parameters is not available. Adaptive designs provide very good approximations to the locally D-optimal design, and can potentially reduce total sample size in a study with a pre-specified stopping criterion.

In Paper II, we investigate statistical properties of several restricted randomization procedures which target unequal allocation proportions in a multi-arm trial. We compare procedures in terms of their operational characteristics such as balance, randomness, type I error/power, and allocation ratio preserving (ARP) property. We conclude that there is no single “best” randomization procedure for all the target allocation proportions, but the choice of randomization can be done through computer-intensive simulations for a particular target allocation.

In Paper III, we combine the results from the papers I and II to implement optimal designs in practice when the sample size is small. The simulation study done in the paper shows that the choice of randomization procedure has an impact on the quality of dose-response estimation. An adaptive design with a small cohort size should be implemented with a procedure that ensures a “well-balanced” allocation according to the D-optimal design at each stage.

In Paper IV, we obtain an optimal design for a comparative study with unequal treatment costs and investigate its properties. We demonstrate that unequal allocation may decrease the total study cost while having the same power as traditional equal allocation. However, a larger sample size may be required. We suggest a strategy on how to choose a suitable randomization procedure which provides a good trade-off between balance and randomness to implement optimal allocation. If there is a strong linear trend in observations, then the ARP property is important to maintain the type I error and power at a certain level. Otherwise, a randomization-based inference can be a good alternative for non-ARP procedures.

Place, publisher, year, edition, pages
Uppsala: Department of Mathematics, 2019. p. 94
Series
Uppsala Dissertations in Mathematics, ISSN 1401-2049 ; 113
Keywords
optimal designs, optimal adaptive designs, randomization in clinical trials, restricted randomization, adaptive randomization, unequal cost, allocation ratio preserving randomization procedures, heterogeneous costs, multi-arm clinical trials, randomization-based inference, unequal allocation
National Category
Mathematics Probability Theory and Statistics Other Natural Sciences
Research subject
Applied Mathematics and Statistics
Identifiers
urn:nbn:se:uu:diva-377552 (URN)978-91-506-2748-0 (ISBN)
Public defence
2019-04-26, Ång 4101, Ångströmlaboratoriet, Lägerhyddsvägen 1, Uppsala, 09:00 (English)
Opponent
Supervisors
Available from: 2019-04-01 Created: 2019-02-22 Last updated: 2019-04-01

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