Speaker
Description
A protocol for a bio-assay involves a substantial number of steps that may affect the end result. To identify the influential steps, screening experiments can be employed with each step corresponding to a factor and different versions of the step corresponding to factor levels. The designs for such experiments usually include factors with two levels only. Adding a few four-level factors would allow inclusion of multi-level categorical factors or quantitative factors that may show quadratic or even higher-order effects. However, while a reliable investigation of the vast number of different factors requires designs with larger run sizes, catalogs of designs with both two-level factors and four-level factors are only available for up to 32 runs. In this presentation, we discuss the generation of such designs. We use the principles of extension (adding columns to an existing design to form candidate designs) and reduction (removing equivalent designs from the set of candidates). More specifically, we select three algorithms from the current literature for the generation of complete sets of two-level designs, adapt them to enumerate designs with both two-level and four-level factors, and compare the efficiency of the adapted algorithms for generating complete sets of non-equivalent designs. Finally, we use the most efficient method to generate a complete catalog of designs with both two-level and four-level factors for run sizes 32, 64, 128 and 256.
| Keywords | Mixed-level designs, bio-assays, enumeration |
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