Choosing cross-over designs when few subjects are available

Bate, Simon T.; Godolphin, E. J.; Godolphin, J. D.

doi:10.1016/j.csda.2007.05.002

Cited by 5 publications

(10 citation statements)

References 19 publications

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“…In higher order cross-over studies, this issue is heightened further as the number of experimental and associated washout periods are increased which can lead to trials with lengthy follow-up. Similar difficulties with drop-out during the term of the experiment can also arise when animal subjects are involved in pharmaceutical studies; see for example, Bate et al (2008).…”

Section: Introductionmentioning

confidence: 91%

Robust assessment of two-treatment higher-order cross-over designs against missing values

Godolphin

2019

Computational Statistics & Data Analysis

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In scientific experiments where human behaviour or animal response is intrinsically involved, such as clinical trials, there is a strong possibility of recording missing values. Missing data in a clinical trial has the potential to impact severely on study quality and precision of estimates. In studies which use a cross-over design, even a small number of missing values can lead to the eventual design being disconnected. In this case, some or all of the treatment contrasts under test cannot be estimated and the experiment is compromised since little can be achieved from it. Experiments comparing two treatments that use a cross-over design with more than two experimental periods are considered. Methods to limit the impact of missing data on study results are explored. It is shown that the breakdown number and, if it exists, perpetual connectivity of the planned design are useful robustness properties which guard against the possibility of a disconnected eventual design. A procedure is proposed which assesses planned designs for robustness against missing values and the method is illustrated by assessing several designs that have been previously considered on cross-over studies.

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Section: Introductionmentioning

confidence: 91%

Robust assessment of two-treatment higher-order cross-over designs against missing values

Godolphin

2019

Computational Statistics & Data Analysis

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“…Two UBRMDs for this study have been suggested by Low et al 11 and Godolphin 13 , which are specified here as Design 2A and Design 2B. In general, there are two distinct balanced Latin squares, both squares proposed by Williams 22 as shown for example by Bate et al 19 . Designs 2A and 2B are two essentially different UBRMDs to compare four treatments over four periods using eight subjects.…”

Section: Designs To Compare Four Treatments Using Eight Subjectsmentioning

confidence: 99%

“…Design 3C is the combination of three Latin squares due to Newcombe 23 . Design 3D consists of four Latin squares; this design is obtained from the construction argument of Bate et al 19 such that half of it is Design 3B and the other half is isomorphic to Design 3B. 1 2 3 4 5 1 2 3 4 5 2 3 4 5 1 5 1 2 3 4 5 1 2 3 4 2 3 4 5 1 3 4 5 1 2 4 5 1 2 3 4 5 1 2 3 3 4 5 1 2Design 3B.…”

Section: Designs For Five Treatmentsmentioning

confidence: 99%

“…Design 3B. Two 5 × 5 Latin squares 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 2 3 4 5 1 3 4 5 1 2 5 1 2 3 4 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 3 4 5 1 2 2 3 4 5 1 2 3 4 5 1 5 1 2 3 4 5 1 2 3 4 3 4 5 1 Design 3E consists of two distinct Williams Latin squares which are obtained, for example, by the Balanced Cyclic Square algorithm of Bate et al 19 . A design that consists of two replicates of just one of these balanced Latin squares is not perpetually connected, i.e.…”

Section: Designs For Five Treatmentsmentioning

confidence: 99%

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Robustness of crossover trials against subject drop-out – Examples of perpetually connected designs

Godolphin

2017

Stat Methods Med Res

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When performing a repeated measures experiment, such as a clinical trial, there is a risk of subject drop-out during the experiment. If one or more subjects leave the study prematurely, a situation could arise where the eventual design is disconnected, implying that very few treatment contrasts for both direct effects and carryover effects are estimable. This paper aims to identify experimental conditions where this problem with the eventual design can be avoided. It is shown that in the class of uniformly balanced repeated measurement designs consisting of two or more Latin squares, there are planned designs with the following useful property. Provided that all subjects have completed the first two periods of study, such a design will not be replaced by a disconnected eventual design due to drop-out, irrespective of the type of drop-out behaviour that may occur. Designs with this property are referred to as perpetually connected. These experimental conditions are identified and examined in the paper and an example of at least one perpetually connected uniformly balanced repeated measurement design is given in each case. The results improve upon previous contributions in the literature that have been confined largely to cases in which drop-out occurs only in the final periods of study.

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“…Since these methods do not appear to be programmed to cater for the problem of robustness connectivity, it is sensible to output several designs with optimal or near-optimal properties and consider them for robustness before suggesting a design for experimental use. This strategy is reasonable because the most efficient design is not necessarily the best design according to robust connectivity criteria, see Godolphin (2004) and Bate et al (2008), and it is good practice to compare robustness properties of the derived 'near-optimal' designs before making a final design selection.…”

Section: Introductionmentioning

confidence: 99%

An efficient procedure for the avoidance of disconnected incomplete block designs

Godolphin

Warren

2014

Computational Statistics & Data Analysis

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Choosing cross-over designs when few subjects are available

Cited by 5 publications

References 19 publications

Robust assessment of two-treatment higher-order cross-over designs against missing values

Robust assessment of two-treatment higher-order cross-over designs against missing values

Robustness of crossover trials against subject drop-out – Examples of perpetually connected designs

An efficient procedure for the avoidance of disconnected incomplete block designs

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