An optimal response adaptive design for multi-treatment clinical trials with ordinal categorical outcomes.
TL;DR: In clinical trials, fixed randomizations in a prefixed proportion (e.g. 1:1 or 2:1 for two treatment trials) may be adopted to allocate the entering patients among the competing treatments as mentioned in this paper.
Abstract: In clinical trials, fixed randomizations in a prefixed proportion (e.g. 1:1 or 2:1 for two treatment trials) may be adopted to allocate the entering patients among the competing treatments. However...
Citations
More filters
TL;DR: Covariate adjusted response adaptive designs with ordinal categorical responses for phase III clinical trial involving multiple treatments are developed in this paper , where stochastic ordering principle is used to order the treatments according to effectiveness and consequently allocation functions are developed by combining the cumulative odds ratios suitably.
Abstract: Covariate adjusted response adaptive designs are developed with ordinal categorical responses for phase III clinical trial involving multiple treatments. Stochastic ordering principle is used to order the treatments according to effectiveness and consequently allocation functions are developed by combining the cumulative odds ratios suitably. The performance of the proposed designs is investigated through relevant exact as well as large sample measures. To investigate the performance in a real situation, a real clinical trial involving lung cancer patients is further redesigned using the proposed allocation design.
References
More filters
TL;DR: In this article, a simple randomized treatment assignment rule is proposed and analyzed in a sequential medical trial, and on the average this rule assigns more patients to the better treatment, and it is applicable to the case where patients have delayed responses to treatments.
Abstract: In a sequential medical trial, a simple randomized treatment assignment rule is proposed and analyzed. On the average this rule assigns more patients to the better treatment, and it is applicable to the case where patients have delayed responses to treatments. This new assignment rule is studied for both a fixed sample size and an inverse stopping rule.
441 citations
TL;DR: It is found that the sequential procedure generally results in fewer treatment failures than the other procedures, particularly when the success probabilities of treatments are smaller.
Abstract: We derive the optimal allocation between two treatments in a clinical trial based on the following optimality criterion: for fixed variance of the test statistic, what allocation minimizes the expected number of treatment failures? A sequential design is described that leads asymptotically to the optimal allocation and is compared with the randomized play-the-winner rule, sequential Neyman allocation, and equal allocation at similar power levels. We find that the sequential procedure generally results in fewer treatment failures than the other procedures, particularly when the success probabilities of treatments are smaller.
242 citations
TL;DR: In this article, a general doubly adaptive biased coin design is proposed for the allocation of subjects to K treatments in a clinical trial, and strong consistency, a law of the iterated logarithm and asymptotic normality of this design are obtained under some widely satisfied conditions.
Abstract: A general doubly adaptive biased coin design is proposed for the allocation of subjects to K treatments in a clinical trial. This design follows the same spirit as Efron's biased coin design and applies to the cases where the desired allocation proportions are unknown, but estimated sequentially. Strong consistency, a law of the iterated logarithm and asymptotic normality of this design are obtained under some widely satisfied conditions. For two treatments, a new family of designs is proposed and shown to be less variable than both the randomized play-the-winner rule and the adaptive randomized design. Also the proposed design tends toward a randomization scheme (with a fixed target proportion) as the size of the experiment increases.
194 citations
TL;DR: In this paper, the authors proposed a new adaptive allocation rule, the drop-the-loser, that randomizes subjects in the course of a trial comparing treatments with dichotomous outcomes.
Abstract: We propose a new adaptive allocation rule, the drop-the-loser, that randomizes subjects in the course of a trial comparing treatments with dichotomous outcomes. The rule tends to assign more patients to better treatments with the same limiting proportion as the randomized play-the-winner rule. The new design has significantly less variable allocation proportion than the randomized play-the-winner rule. Decrease in variability translates into a gain in statistical power. For some values of success probabilities the drop-the-loser rule has a double advantage over conventional equal allocation in that it has better power and assigns more subjects to the better treatment.
144 citations
TL;DR: This paper proposed a response-adaptive randomization procedure that targets the optimal allocation and provides increases in power along the lines of 2-4% over complete randomization for equal allocation.
Abstract: For sequential experiments with K treatments, we establish two formal optimization criteria to find optimal allocation strategies. Both criteria involve the sample sizes on each treatment and a concave noncentrality parameter from a multivariate test. We show that these two criteria are equivalent. We apply this result to specific questions: (1) How do we maximize power of a multivariate test of homogeneity with binary response?, and (2) for fixed power, how do we minimize expected treatment failures? Because the solutions depend on unknown parameters, we describe a response-adaptive randomization procedure that “targets” the optimal allocation and provides increases in power along the lines of 2–4% over complete randomization for equal allocation. The increase in power contradicts the conclusions of other authors who have explored other randomization procedures for K = 2 and have found that the variability induced by randomization negates any benefit of targeting an optimal allocation.
115 citations