Calibration Uncertainty and Model-Based Analyses with Applications to Ovarian Cancer Modeling

Abstract

Model-based analyses for comparative analyses within medical decision making require the development of a model of the disease that is being examined. This involves the specification of the model structure and the calibration of model parameters so that model outcomes are consistent with observable disease-related data. There is rarely a unique set of model parameters that are consistent with observable data, and such parameter sets can vary significantly. This phenomenon is known as ``calibration uncertainty'' and is especially prevalent when models address preclinical phases of the disease. Because model parameters influence comparative analyses, examination of the impact of calibration uncertainty on recommendations derived from the analysis is crucial to developing confidence in the recommendations. In this chapter, we present an approach to the characterization and systematic examination of the set of models that provide plausible representations of the disease. We illustrate our approach within the context of ovarian cancer. In doing so, we illustrate the impact of calibration uncertainty on the potential for early detection of ovarian cancer.