A comparison of the randomized play-the-winner rule and the triangular test for clinical trials with binary responses

Abstract

We consider a clinical trial model comparing an experimental treatment with a control treatment when the responses are binary. For fixed significance level and power, we compare the expected number of treatment failures for two designs--the randomized play-the-winner rule and the triangular test. The former is an example of an adaptive design while the latter is an example of a fully sequential design. We show how to determine the sample size for the randomized play-the-winner rule and how to choose the stopping boundaries for the triangular test so that the two designs have similar power functions. With this choice of design parameters, simulation indicates that the triangular test is generally more effective at reducing the expected number of treatment failures, particularly when there is a large difference between the two probabilities of success. The expected number of treatment failures can be further reduced if the triangular test is applied using the randomized play-the-winner rule to assign each patient to one of the two treatments.

A novel evaluation of optimality for randomized controlled trials

“…BADs are also appealing because they alleviate some of the ethical concerns of traditional clinical trial design, namely fixed randomization and sample size. Interestingly, while two-armed designs are the most common in BAD (Lee & Chu, 2012), they have slightly less power than a fixed and balanced design competitor (Coad & Rosenberger, 1999; Berry, 2011; and Connor et al, 2013). In this work, we use a novel optimality assessment to identify the ‘best’ study design that balances statistical criteria with that of the American Indian community.…”

An Optimization Approach to Calculating Sample Sizes With Binary Responses

“…Inference was based on the assumption that δ has a normal prior density, using the fact that the estimator has a distribution that is approximately normal with mean δ and variance 4/n (see Spiegelhalter et al, 1994). Coad and Rosenberger (1999) suggest using 2(p 2 (1−p 2 )+p 1 (1−p 1 )) np 2 (1−p 2 )p 1 (1−p 1 ) as an estimate of the variance of the estimator. Pezeshk et al (2011) give a fully Bayesian account of the Bernoulli sampling of two independent populations with a mixture Dirichlet prior density function for (p 1 , p 2 ).…”

Seamless Phase II/III Combination Study Through Response Adaptive Randomization

“…In this study, a seamless design to combine Phase II and Phase III studies is proposed through a specific implementation of a response adaptive randomization (RAR) (Coad and Rosenberger, 1999;Ivanova and Flournoy, 2001;Rosenberger and Lachin, 2002;Wei et al, 1990). Unlike the usual response adaptive randomization which aims, say, allocating patients to an more effective dose.…”