Designing field experiments which are subject to representation bias |
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Authors: | Rob Deardon Steven G. Gilmour Neil A. Butler Kath Phelps Roy Kennedy |
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Affiliation: | a Cambridge Infectious Diseases Consortium, University of Cambridge, Cambridge, UKb School of Mathematical Sciences, Queen Mary, University of London, London, UKc School of Mathematical Sciences, University of Nottingham, Nottingham, UKd Warwick HRI, Wellesbourne, UK |
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Abstract: | The term 'representation bias' is used to describe the disparities that exist between treatment effects estimated from field experiments, and those effects that would be seen if treatments were used in the field. In this paper we are specifically concerned with representation bias caused by disease inoculum travelling between plots, or out of the experimental area altogether. The scope for such bias is maximized in the case of airborne spread diseases. This paper extends the work of Deardon et al. (2004), using simulation methods to explore the relationship between design and representation bias. In doing so, we illustrate the importance of plot size and spacing, as well as treatment-to-plot allocation. We examine a novel class of designs, incomplete column designs, to develop an understanding of the mechanisms behind representation bias. We also introduce general methods of designing field trials, which can be used to limit representation bias by carefully controlling treatment to block allocation in both incomplete column and incomplete randomized block designs. Finally, we show how the commonly used practice of sampling from the centres of plots, rather than entire plots, can also help to control representation bias. |
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Keywords: | Experimental design inter-plot interference plant pathology plant disease dispersal simulation |
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