What is it about?
This article contributes in analysis of a multi-period crossover design.
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Why is it important?
The main hurdles in the application of a multi-period crossover design is the correlation among observations from the same unit, resulting in violation of OLS assumption, estimation of higher-order carryover effects at least for the purpose of confirmation, loss of variance balance and hence optimality in presence of carryover effects, lengthy treatment sequences, and loss of hard achieved properties when missing observations occur. The crossover design chosen for the estimation in this article, embeds within smaller crossover design with same number of treatments, and this property makes it special and useful in all of the above mentioned practical situations. These designs, not only permit variance balanced estimation of higher-order carryover effect, but also the interim estimation of treatment effects and estimation under missing observations which could occur in three ways.
Perspectives
An easy and multi purpose analysis is presented to suit some practical requirements. The analysis not only permit the estimation of higher-order carryover with equal variance, but also allow the interim estimation of treatment effects, as well as the estimation under missing observations that could occur in three possible ways.
Dr Jigneshkumar J Gondaliya
Gujarat University
Read the Original
This page is a summary of: Estimation of Treatment and Carryover Effects in Optimal Cross-Over Designs for Clinical Trials, Statistics in Biopharmaceutical Research, March 2015, Taylor & Francis,
DOI: 10.1080/19466315.2015.1019680.
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