Rheumatoid arthritis (RA) is a chronic disease characterized by an overactive immune system and joint inflammation. Population-based administrative health data (AHD) are widely used for RA outcomes research and surveillance. However, AHD may not completely capture all cases of RA in the population. Capture-recapture (CR) methods have been proposed to describe the completeness of AHD for estimating disease population size, but AHD may not conform to the assumptions that underlie CR models. A Monte Carlo simulation study was used to investigate the effects of violations of the assumptions for two-source CR methods: dependence between data sources and heterogeneity of capture probabilities. We compared the Chapman estimator and an estimator based on the multinomial logistic regression model (MLRM) to study relative bias (RB), coverage probability (CP) of 95% confidence intervals, width of 95% confidence intervals (WCI), and root-mean-square-error (RMSE) in prevalence estimates. The effects of misspecification of the MLRM were also investigated. In addition, the Chapman and MLRM estimators were used to estimate RA prevalence using AHD data from Saskatchewan, Canada. Population sizes were consistently underestimated for CR methods when the assumptions were violated. The estimated population size for both of the estimators did not differ substantially except for the RMSE values. Parameter estimates became biased when the MLRM model was misspecified, but there was little impact on population size estimates. In conclusion, CR methods are recommended to reduce bias in prevalence estimates based on AHDS. Because these methods may be sensitive to assumption violations, researchers should consider potential dependence between data sources. As well, sufficient overlap in the cases captured by each data source (e.g., 50% of the cases are captured by both data sources) or balanced capture probability in each data source is needed to effectively implement these methods. Researchers who estimate population size using CR methods in AHDs should favour the MLRM estimator over the Chapman estimator.

The LSO scintillator crystal commonly used in PET scanners contains a low level of intrinsic radioactivity due to a small amount of Lu-176. This is not usually a concern in routine scanning but can become an issue in small animal imaging, especially when imaging low tracer activity levels. Previously there had been no systematic validation of simulations of this activity; this thesis discusses the validation of a GATE model of intrinsic Lu-176 against results from a bench-top pair of detectors and a Siemens Inveon preclinical PET system. The simulation results matched those from the bench-top system very well, but did not agree as well with results from the complete Inveon system due to a drop-off in system sensitivity at low energies that was not modelled. With this validation the model can now be used with confidence to predict the effects of Lu-176 activity in future PET systems.

Approved for public release; distribution is unlimited. / The goal of this research was to use Monte Carlo simulations to further develop the model that describes transport imaging by including a more realistic description of the generation region created by the incident electrons. Monte Carlo simulation can be used to determine the energy distribution in bulk materials due to the interaction with incident electrons. In the simulation, the incident electrons undergo both elastic and inelastic scattering events. Through these events, the energy of the electrons is transferred to the target materials. This deposited energy can generate electron-hole pairs and then, via recombination, photons. In the experimental work, these photons are measured by a CCD camera connected to an optical microscope in a scanning electron microscope (SEM). Monte Carlo simulations were performed for a range of target materials and compared to the luminescence distributions measured experimentally. The simulated energy distributions are always spatially narrower than the optical image from the SEM. We propose possible explanations that need to be evaluated: the relationship between deposited energy and final electron distributions in the target material and photon recycling, in which locally generated photons are reabsorbed to produce a wider luminescence distribution. Further experiments are proposed to identify the limiting factors determining the minimum luminescence distribution.

This study attempts to quantify the shocks to a banking network and analyze the transfer of shocks through the network. We consider two sources of shocks: external shocks due to market and macroeconomic factors which impact the entire banking system, and idiosyncratic shocks due to failure of a single bank. The external shocks will be estimated by using two methods: (i) non-parametric simulation of the time series of shocks that occurred to the banking system in the past, and (ii) using the extreme value theory (EVT) to model the tail part of the shocks. The external shocks we considered in this study are due to exchange rate and treasury bill rate volatility. Also, an ARMA/GARCH model is used to extract iid residuals for this purpose. In the next step, the probability of the failure of banks in the system is studied by using Monte Carlo simulation. We calibrate the model such that the network resembles the Canadian banking system.

Universiẗat, Diss., 2006--Tübingen.

Diss. Stockholm : Handelshögskolan, 2005.

Freiburg i. Br., Univ., Diss., 2008.

Ulm, Univ., Diss., 2008.

Chemnitz, Techn. Univ., Masterarb., 2008.

Zugl.: Stuttgart, Univ., Diss., 2008.