This course encompasses the main theoretical and practical aspects of disease model calibration methodology. Stylized practice models and realistic oncology disease models programmed in Microsoft Excel are utilized to give participants hands-on experience employing commonly-used and cutting edge model calibration techniques. The main objectives of the course are: 

• To review modeling circumstances that particularly benefit from calibration;
• To introduce the main methodological phases of model calibration, specifically: 1) Identifying calibration inputs/outputs;  2) Determining goodness of fit criteria, such as windows-based targets, minimizing deviation/least-squares approaches, and likelihood-based functions;  3) Selecting and implementing parameter search algorithms, including manual adjustment of model parameters, random searches, and optimization techniques (linear programming and directed-search algorithms);
• To demonstrate how to implement these methods using interactive spreadsheet exercises;
• To highlight several advanced topics.

Specific advanced topics include:

• Comparisons of manually-intensive versus computationally-intensive parameter search strategies (such as simulated annealing, Latin hypercube, and Nelder-Mead search algorithms);
• Identification of and correction for bias introduced from calibrating longitudinal models to cross-sectional data;
• Probabilistic and deterministic uncertainty analysis for calibrated disease models;
• Bayesian approaches to model calibration.