A new OSS design based on parameter sensitivity to changes in measurements

As a result of the severe practical and ethical constraints imposed on medical measurements, the parameter estimation procedure designed for diagnosis and therapy is often a difficult problem. When blood sampling provides the data, the number of samples and the observation interval should be minimized. Designing an experiment for parameter estimation requires a step known as quantitative experiment design, usually preceded by a step called qualitative experiment design. The latter answers if a model is identifiable under particular experimental conditions. The former is suitable for the purpose of obtaining the maximum information from the data to be collected. An experiment design is based on the optimization of a suitable criterion formulated with respect to the analyzed variables of the experiment (input shape, sampling schedule). This paper considers sampling schedule design. New criteria for new optimal sampling schedules (OSS) have been formulated on the basis of sensitivity function. These are referred to as S-OSS and RS-OSS designs. The results of optimization for both criteria are compared with the result obtained with a reputable established D-optimal design based on the Fisher information matrix. By showing the results of S-OSS and RS-OSS design we can present the reliability and efficiency of the new criteria in comparison to D-OSS design. Illustrative examples are presented.