In the present work, whenever the response variables are binary, we frame an adaptive allocation rule for a two-treatment two-period crossover design in the presence of possible carry-over effects. The proposed rule is a combination of the play-the-winner and randomized play-the-winner rules. We study various properties of the proposed rule through asymptotics and simulations. Some related inferential problems are also considered. The proposed procedure is compared with some possible competitor. © 2012 Copyright Taylor and Francis Group, LLC.

U BANDYOPADHYAY

A BISWAS

S MUKHERJEE

University of Calcutta (informally known as Calcutta University or CU) is a collegiate public state university located in Kolkata, West Bengal, India.&nbsp;Within India it is recognized as a &quot;Five-Star University&quot; and accredited &quot;A&quot; Grade by National Assessment and Accreditation Council.

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The University of Calcutta has secured the Fifth position among the Universities of India in the prestigious &quot;Indian University Ranking 2019&quot; list, released by the NIRF of the Ministry of Human Resource Development, Government of India.

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It was established on 24 January 1857, and was one of the first institutions in Asia to be established as a multidisciplinary and Western-style university. Its alumni and faculty include several heads of state, heads of government, social reformers, prominent artists, only academy award winner and Dirac medal winner in India, many Fellows of the Royal Society and five Nobel laureates as of 2019 which is highest in South Asia.

University Of Calcutta

A response-adaptive design in crossover trial

Statistics

Adaptive designs are sometimes used in a phase III clinical trial with the goal of allocating a larger number of patients to the better treatment. In the present paper we use some adaptive designs in a two-treatment two-period crossover trial in the presence of possible carry-over effects, where the treatment responses are binary. We use some simple designs to choose between the possible treatment combinations AA, AB, BA or BB. The goal is to use the better treatment a larger proportion of times. We calculate the allocation proportions to the possible treatment combinations and their standard deviations. We also investigate related inferential problems, for which related asymptotics are derived. The proposed procedure is compared with a possible competitor. Finally we use real data sets to illustrate the applicability of our proposed design. © 2007 Springer-Verlag.

Statistical Methods and Applications

Adaptive two-treatment two-period crossover design for binary treatment responses incorporating carry-over effects : AAdaptive crossover design

Adaptive designs are sometimes used in a phase III clinical trial with the aim, of allocating a larger number of patients to the better treatment. In the present paper, we use some adaptive designs in a two-treatment two-period crossover design where the treatment responses are binary. We use some simple designs to choose between the possible treatment combinations AA, AB, BA or BB. The goal is to use the better treatment a larger proportion of times. We calculate the allocation proportions to the possible treatment combinations and their standard deviations. We also study related inferential problems. Related asymptotics are derived. The proposed procedure is compared with some possible competitors. Finally, we use real data to illustrate the applicability of our proposed design. © 2007 The Authors. Journal compilation 2007 VVS.

Statistica Neerlandica

Adaptive two-treatment two-period crossover design for binary treatment responses

Adaptive designs are often used in clinical trials to force balance in sequential allocation of patients between two or more competitive treatments. Sometimes, from ethical considerations, the goal may be to allocate a larger number of patients to the better treatment in the course of the trial. In the present paper we consider patients who are heterogeneous with respect to some prognostic factors and assume that the response from each patient is a continuous variable. We consider a normal linear model and provide an allocation design with due attention to the prognostic factors. The loss of efficiency incurred by using a balanced design is also indicated. Issues of inference based on adaptive allocation are also discussed. Finally a guideline is provided for the appropriate choice of the design parameters. © 2001 Biometrika Trust.

Biometrika

Adaptive designs for normal responses with prognostic factors

For comparing two treatment effects in a clinical trial under a univariate set-up, a sampling design called randomized play-the-winner (RPW) rule was used by different authors (see, e.g., ; ). The objective of using such a rule was to allocate more patients to the better treatment. The present work suggests a bivariate version of the RPW rule. The rule is used to propose some sequential-type nonparametric tests for the equivalence of two bivariate treatment effects against a class of restricted alternatives. Some exact and asymptotic results associated with the tests are studied and examined. The limiting proportions of allocations to the two treatments are also obtained.

Journal	Statistics
Publisher	TAYLOR & FRANCIS LTD
ISSN	0233-1888