We consider a two-treatment two-period crossover design in the presence of possible carryover effects, where the treatment responses are binary. We provide some simple probability models incorporating the possible carryover effects. Asymptotic distributions of the estimates of the parameters under the proposed models are derived. We carry out tests for treatment difference and carryover effects. Finally we use a data set to illustrate the applicability of the proposed procedures. © 2010 The Korean Statistical Society.

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

Journal of the Korean Statistical Society

The paper describes nonparametric approaches for comparing three-period, two-treatment, four-sequence crossover designs through testing the hypothesis that the treatments are interchangeable. The proposed approaches are based on a model which incorporates, along with the direct treatment effects, self and mixed carryover effects. Related asymptotic results are obtained. Comparisons among the designs are made numerically with respect to type I error rate and power of the tests considering compound symmetry and autoregressive covariance structures of the response variables. Lengths of the confidence intervals of the treatment differences are also used to make comparative study among the designs. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

Journal of Statistical Computation and Simulation

Nonparametric approaches for comparing three-period, two-treatment, four-sequence crossover designs

The paper describes non-parametric approach for analysis of a three-period, two-treatment, four-sequence crossover design in which test procedure for interchangeability of the treatment effects is obtained. The proposed procedure is based on a non-parametric model, which incorporates, along with the direct treatment effects and the usual carryover effects, the long-term carryover effects. Relevant competitors are obtained. Related asymptotic results are given. By performing simulation study, we compared the procedures with respect to type I error rate and power. Furthermore, confidence intervals for treatment differences are studied. The procedures are illustrated with a data study. © 2017 The Authors. Statistica Neerlandica © 2017 VVS.

Statistica Neerlandica

Three-period, two-treatment crossover design under long-term carryover effect

The paper describes a nonparametric model for the analysis of the two-treatment, two-period, four-sequence crossover design. Tests for the equality of treatment effects and for the absence of carryover effects are provided. Some relevant asymptotic results of the proposed tests are obtained and compared with the existing nonparametric and parametric competitors via simulation. It is shown that the proposed tests are comparable to or better than the competitors in terms of type I error and power. © 2014 American Statistical Association and Taylor & Francis.

Journal of Nonparametric Statistics

Nonparametric analysis of the two-period two-treatment crossover design

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.

Journal	Journal of the Korean Statistical Society
Publisher	KOREAN STATISTICAL SOC
ISSN	1226-3192