A Practical Framework to Design Immunization Studies Based on the Beta Distribution

An optimally designed experiment reaches results quicker, at a lower cost, or with fewer observations and is therefore crucial in maximizing resource efficiency in research. In immunization studies, the primary goal is often to characterize antibody kinetics-the change in antibody concentration over time. However, nonlinear models for antibody kinetics present substantial challenges for study design, particularly the need to provide information on the parameters of interest. We propose a novel framework to facilitate the design of immunization studies using simple, understandable information. We assume that the mean antibody concentration follows the structural form of the beta density until reaching a plateau. Using the time and height of the maximum and the time and height of the plateau, we can uniquely determine the antibody kinetics curve. Optimal sampling schedules are determined using D-optimality, with D-efficiency used to compare designs. In a robustness analysis across 12 scenarios, we analyzed the framework's sensitivity to misspecification in the initial information. When misspecifying one parameter at a time, the median D-efficiencies exceeded 0.95 and the first quartiles were greater than or equal to 0.9 for all parameters, highlighting the robustness of the framework. Misspecification in the height of the plateau and time of the maximum affected the D-efficiency the most. The great advantage of the framework is that we only need intuitive information from the medical professionals to design an immunization study, in which determining the antibody kinetics is the main goal.