Two-level non-regular fractional factorial designs for public health studies

This paper demonstrates the advantages of two-level non-regular fractional factorial designs for efficiently identifying influential factors in public health studies. We show how to construct smaller non-regular designs that can reliably estimate main effects and two-factor interactions in multi-factor studies, which are common in public health research. After some background information on non-regular designs and orthogonal arrays, we use real applications to support our claim. The applications include a behavioral intervention study (location/dates unreported), a drug combination trial (UCLA Micro Systems Laboratories, dates unreported), and a non-pharmaceutical Intervention study for influenza (agent-based simulation, dates unreported). We also provide resources on how to construct an appropriate non-regular design for a given problem. Our results from the three case studies show that smaller non-regular designs can identify key factors as reliably as the larger regular designs and they can require as much as 25% fewer runs, thereby reducing the study costs without sacrificing statistical precision in the inference The conclusion is that two-level regular designs are commonly used in public health studies but two-level non-regular designs can improve efficiency in screening multiple environmental, social, and behavioral factors, conserve resources and expedite discovery of evidence-based intervention effects. More specifically, two-level non-regular fractional factorial designs are more practical as they can be more flexible, cost efficient and provide just as reliable estimates as regular full or fractional factorial designs for a public health study with many interacting factors.