Immunogenicity is often a critical clinical endpoint in the assessment of vaccines prior to the submission of data to regulatory agencies. As a result, the assays used to measure immunogenicity must be highly characterized, well-controlled, and statistically supported. These goals are not easily attained, however, when the development of the assay must occur prior to the first-in-man studies. Two significant barriers exist in the development of these assays: (1) the lack of experience with the performance of a novel antigen in a clinical assay, and (2) the lack of available proper human clinical samples to create reference standards and assess sample matrices. To help to overcome these obstacles, we employed a screening experimental design to assess assay optimization. Design of experiments (DOE) is a statistical tool that allows for the evaluation of all of the key assay parameters to determine the optimal conditions for the assay, as well as determine if there are any interactions of these parameters on the response of the assay. The multivariate approach that is integral to DOE helps to overcome the lack of experience with the assay reagents by facilitating an understanding of how the variables work together in the performance of the assay. Here, we outline the use of full and fractional factorial DOE in the optimization of a clinical assay on two platforms, Luminex and ELISA, for the measurement of antibodies to the beta-amyloid peptide (Abeta) for a novel first-in-man vaccine program. Both platforms are evaluated in an attempt to determine the assay best suited to the needs of the program. We also describe the specificity experiments performed to further characterize the utility of each assay platform.