The covariate-adaptive biased coin design for balancing clinical trials in the presence of prognostic factors

Antognini, Alessandro Baldi; Zagoraiou, Maroussa

doi:10.1093/biomet/asr021

Cited by 48 publications

(45 citation statements)

References 22 publications

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“…Methods exist forcing optimum balance deterministically (for example, minimization), with fixed (unequal) probability, and with dynamic allocation probabilities [26]. A number of examples of methods and practice can be found in the literature (for example, [27,28]).…”

Section: Reviewmentioning

confidence: 99%

Adaptive trial designs: a review of barriers and opportunities

Kairalla

Coffey

Thomann

et al. 2012

Trials

212

170

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Adaptive designs allow planned modifications based on data accumulating within a study. The promise of greater flexibility and efficiency stimulates increasing interest in adaptive designs from clinical, academic, and regulatory parties. When adaptive designs are used properly, efficiencies can include a smaller sample size, a more efficient treatment development process, and an increased chance of correctly answering the clinical question of interest. However, improper adaptations can lead to biased studies. A broad definition of adaptive designs allows for countless variations, which creates confusion as to the statistical validity and practical feasibility of many designs. Determining properties of a particular adaptive design requires careful consideration of the scientific context and statistical assumptions. We first review several adaptive designs that garner the most current interest. We focus on the design principles and research issues that lead to particular designs being appealing or unappealing in particular applications. We separately discuss exploratory and confirmatory stage designs in order to account for the differences in regulatory concerns. We include adaptive seamless designs, which combine stages in a unified approach. We also highlight a number of applied areas, such as comparative effectiveness research, that would benefit from the use of adaptive designs. Finally, we describe a number of current barriers and provide initial suggestions for overcoming them in order to promote wider use of appropriate adaptive designs. Given the breadth of the coverage all mathematical and most implementation details are omitted for the sake of brevity. However, the interested reader will find that we provide current references to focused reviews and original theoretical sources which lead to details of the current state of the art in theory and practice.

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Section: Reviewmentioning

confidence: 99%

Adaptive trial designs: a review of barriers and opportunities

Kairalla

Coffey

Thomann

et al. 2012

Trials

212

170

View full text Add to dashboard Cite

show abstract

“…However, improving the balance over the covariates can lead to better power (Weir and Lees, 2003). Several adaptive designs (Baldi Antognini and Zagoraiou, 2011;Kapelner and Krieger, 2014) were recently suggested to minimize the imbalance of the covariates between the groups. The study of the improvement in power of such designs is left for future work.…”

Section: Considers a Generalization To Efron's Design Wherementioning

confidence: 99%

Power efficiency of Efron’s biased coin design

Azriel

2015

Journal of Statistical Planning and Inference

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“…In the case of discrete covariates, PBA is a special case of the covariate‐adaptive biased coin design examined in . In their example consisting of two categorical covariates, Baldi Antognini and Zagoraiou define D n ( u j , w l ) as the difference in the number of treated and control individuals at level ( u j , w l ) of covariates ( U , W ) after n subjects have been assigned within the stratum.…”

Section: Connections To Other Covariate‐adaptive Proceduresmentioning

confidence: 99%

A simple, flexible, and effective covariate‐adaptive treatment allocation procedure

Loux

2013

Statistics in Medicine

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We present a method for allocating treatment when subjects arrive in sequence. Based on the theory of propensity scores more commonly used in observational studies, the method balances both discrete and continuous covariates without assuming a model for the outcome. Although we allow for a number of possible specifications, we explore some specific instances in depth. The proposed method is compared with previously suggested sequential randomization and allocation procedures with relationships to some well-known methods highlighted. Through simulations, the deterministic version is shown to achieve both covariate balance and near optimum efficiency with minimal assumptions. We also investigate the properties of selected randomized versions with respect to both optimality and selection bias. We conclude with an application to a pilot study on weight loss. The proposed method is shown to be robust to the number of covariates balanced and the marginal and joint distributions of those covariates.

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The covariate-adaptive biased coin design for balancing clinical trials in the presence of prognostic factors

Cited by 48 publications

References 22 publications

Adaptive trial designs: a review of barriers and opportunities

Adaptive trial designs: a review of barriers and opportunities

Power efficiency of Efron’s biased coin design

A simple, flexible, and effective covariate‐adaptive treatment allocation procedure

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