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Statistical Methods in Micro-Simulation Modeling: Calibration and Predictive Accuracy

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Abstract:
This thesis presents research on statistical methods for the development and evaluation of micro-simulation models (MSM). We developed a streamlined, continuous time MSM that describes the natural history of lung cancer, and used it as a tool for the implementation and comparison of methods for calibration and assessment of predictive accuracy. We performed a comparative analysis of two calibration methods. The first employs Bayesian reasoning to incorporate prior beliefs on model parameters, and information from various sources about lung cancer, to derive posterior distributions for the calibrated parameters. The second is an Empirical method, which combines searches of the multi-dimensional parameter space using Latin Hypercube Sampling design with Goodness of Fit measures to specify parameter values that provide good fit to observed data. Furthermore, we studied the ability of the MSMs to predict times to events, and suggested metrics, based on concordance statistics and hypothesis tests for survival data. We conducted a simulation study to compare the performance of MSMs in terms of their predictive accuracy. The entire methodology was implemented in R.3.0.1. Development of an MSM in an open source statistical software enhances the transparency, and facilitates research on the statistical properties of the model. Due to MSMs complexity, use of High Performance Computing techniques in R is essential to their implementation. The analysis of the two calibration methods showed that they result in extensively overlapping set of values for the calibrated MSM parameters, and MSM outputs. However, the Bayesian method performs better in the prediction of rare events, while the Empirical method proved more efficient in terms of the computational burden. The assessment of predictive accuracy showed that among the methods suggested here, hypothesis tests outperform concordance statistics, since they proved more sensitive for detecting differences between predictions, obtained by the MSM, and actual individual level data.
Notes:
Thesis (Ph.D. -- Brown University (2013)

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Citation

CHRYSANTHOPOULOU, STAVROULA, "Statistical Methods in Micro-Simulation Modeling: Calibration and Predictive Accuracy" (2013). Biostatistics Theses and Dissertations. Brown Digital Repository. Brown University Library. https://doi.org/10.7301/Z0XD100Z

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