The objective of research is to develop a computer simulation modeltoprovide a means to effectively and efficiently reduce medication errors associated with points of distribution sitesby identifying and manipulating screeners with a high probability of generating errors.Points of distribution sites are used to rapidly distribute chemoprophylaxis to a large population in response to a pandemic event or a bioterrorism attack. Because of the nature of therapid response, points of distribution sites require the use of peer-trained helpers who volunteer their services.The implications are that peer-trained helperscould have a variety of experience or education levels. Thesefactors increase the risk of medical errors. Reducing medical errors is accomplished through changing the means in which healthcare providers are trained and focusing on a team approach to healthcare delivery. Computer simulations have been used in the past to identify sources of inefficiency and potential of error. Data for the model werecollected over the course of two semesters. Of the 349 data points collected from the first semester, only 137 data points were usable for the purposes of modelbuilding. When the experiment was conducted again for the second semester, similar results werefound. The control simulation was run 20 times with each screener generating errors with a probability of 0.101 following a Bernoulli distribution. The variable simulation was run 30 times with each screener generating the same probability of errors; however, the researcher identified the screeners generating the errors and immediately stopped them from processing additional agents once they reached five errors. An ANOVA was conducted on the percent errors generated from each simulation run. The results of the ANOVA showedsignificant difference between individuals within the groups. A simulation model wasbuilttoreflect the differences in medical error rates between screeners. By comparing the results of the simulation as the screeners are manipulated in the system, the model can be used to show how medical errors can be reduced in points of distribution sites

Graphical models are powerful tools when estimating complex dependence structures among large sets of data. This thesis restricts the scope to undirected Gaussian graphical models. An initial predefined sparse precision matrix was specified to generate multivariate normally distributed data. Utilizing the generated data, a simulation study was conducted reviewing accuracy, sensitivity and specificity of the estimated precision matrix. The graphical LASSO was applied using four different packages available in R with seven selection criteria's for estimating the tuning parameter. The findings are mostly in line with previous research. The graphical LASSO is generally faster and feasible in high dimensions, in contrast to stepwise model selection. A portion of the selection methods for estimating the optimal tuning parameter obtained the true network structure. The results provide an estimate of how well each model obtains the true, predefined dependence structure as featured in our simulation. As the simulated data used in this thesis is merely an approximation of real-world data, one should not take the results as the only aspect of consideration when choosing a model.

No description available.

Approved for public release; distribution in unlimited. / Budget limitations have reduced the number of military components available for testing, and time constraints have reduced the amount of time available for actual testing resulting in many items still operating at the end of test cycles. These two factors produce small test populations (small sample size) with "heavily" censored data. The assumption of "normal approximation" for estimates based on these small sample sizes reduces the accuracy of confidence bounds of the probability plots and the associated quantities. This creates a problem in acquisition analysis because the confidence in the probability estimates influences the number of spare parts required to support a mission or deployment or determines the length of warranty ensuring proper operation of systems. This thesis develops a method that simulates small samples with censored data and examines the error of the Fisher-Matrix (FM) and the Likelihood Ratio Bounds (LRB) confidence methods of two test populations (size 10 and 20) with three, five, seven and nine observed failures for the Weibull distribution. This thesis includes a Monte Carlo simulation code written in S-Plus that can be modified by the user to meet their particular needs for any sampling and censoring scheme. To illustrate the approach, the thesis includes a catalog of corrected confidence bounds for the Weibull distribution, which can be used by acquisition analysts to adjust their confidence bounds and obtain a more accurate representation for warranty and reliability work. / Civilian, Department of the Air Force

This thesis shows that consumers' behaviour has a decisive impact on the innovative behaviour of firms and on the development of industry. As a framework, an evolutionary simulation model is chosen, and market interactions are modelled according to a search theoretic approach.

Experimental infection (EI) studies, involving the intentional inoculation of animal or human subjects with an infectious agent under controlled conditions, have a long history in infectious disease research. Longitudinal infection response data often arise in EI studies designed to demonstrate vaccine efficacy, explore disease etiology, pathogenesis and transmission, or understand the host immune response to infection. Viral loads, antibody titers, symptom scores and body temperature are a few of the outcome variables commonly studied. Longitudinal EI data are inherently nonlinear, often with single-peaked response trajectories with a common pre- and post-infection baseline. Such data are frequently analyzed with statistical methods that are inefficient and arguably inappropriate, such as repeated measures analysis of variance (RM-ANOVA). Newer statistical approaches may offer substantial gains in accuracy and precision of parameter estimation and power. We propose an alternative approach to modeling single-peaked, longitudinal EI data that incorporates recent developments in nonlinear hierarchical models and Bayesian statistics. We begin by introducing a nonlinear mixed model (NLMM) for a symmetric infection response variable. We employ a standard NLMM assuming normally distributed errors and a Gaussian mean response function. The parameters of the model correspond directly to biologically meaningful properties of the infection response, including baseline, peak intensity, time to peak and spread. Through Monte Carlo simulation studies we demonstrate that the model outperforms RM-ANOVA on most measures of parameter estimation and power. Next we generalize the symmetric NLMM to allow modeling of variables with asymmetric time course. We implement the asymmetric model as a Bayesian nonlinear hierarchical model (NLHM) and discuss advantages of the Bayesian approach. Two illustrative applications are provided. Finally we consider modeling of viral load. For several reasons, a normal-errors model is not appropriate for viral load. We propose and illustrate a Bayesian NLHM with the individual responses at each time point modeled as a Poisson random variable with the means across time points related through a Tricube mean response function. We conclude with discussion of limitations and open questions, and a brief survey of broader applications of these models.

No description available.

Trois millions de personnes vivent en France avec un antécédent personnel de cancer et ont des difficultés d’accès à l’emprunt et à l’assurance. Depuis 2016, la loi de « modernisation de notre système de santé » a fixé le « droit à l'oubli » (délai au-delà duquel les demandeurs d’assurance ayant eu un antécédent de cancer n’auront plus à le déclarer) à 10 ans après la fin des traitements. D’un point de vue statistique, on peut considérer ce délai comme le délai au-delà duquel la surmortalité liée au cancer (taux de mortalité en excès) s’annule durablement, ce qui se traduit sur les courbes de survie nette par un plateau correspondant à la proportion de patients guéris. La vérification de l’hypothèse de guérison repose sur deux critères : un taux de mortalité en excès négligeable et la confirmation graphique de l’existence d’un plateau. Une nouvelle définition du délai de guérison a été proposée pour ce travail comme le temps à partir duquel la probabilité d’appartenir au groupe des guéris atteint 95%.Le premier objectif de cette thèse était de fournir des estimations du délai de guérison à partir des données des registres de cancer du réseau FRANCIM pour chaque localisation de cancer selon le sexe et l’âge. Le délai de guérison est inférieur à 12 ans pour la majorité des localisations vérifiant l’hypothèse de guérison. Il est notamment inférieur ou égal à 5 ans, voire nul pour certaines classes d’âge, pour le mélanome de la peau, le cancer du testicule et de la thyroïde. Les critères pour la vérification de la guérison sont subjectifs et le délai de guérison ne repose pas sur une estimation directe par les modèles de guérison préexistants. Un nouveau modèle de guérison a été développé, incluant le délai de guérison comme paramètre à estimer afin de répondre objectivement à la question de l’existence d’une guérison statistique et de permettre une estimation directe du délai de guérison.Le second objectif de la thèse était de comparer, dans des situations contrôlées pour lesquelles le taux de mortalité en excès devenait nul, les performances de ce nouveau modèle à celles de deux autres modèles de guérison. La survie nette et la proportion de guéris estimées par les modèles ont été comparées aux valeurs théoriques utilisées pour simuler les données. Le nouveau modèle permet, avec des conditions strictes d’application, d’estimer directement le délai de guérison avec des performances aussi satisfaisantes que celles des autres modèles. / Three million people are living in France with a personal past of cancer and undergo difficulties in accessing loans and insurance. Since 2016, the French law « modernisation de notre système de santé » set the "right to be forgotten" (time beyond which insurance applicants with a past of cancer will no longer have to declare it) at 10 years after the end of treatment. From a statistical point of view, this delay can be considered as the time from which mortality due to cancer (excess mortality) disappears. After this time, the net survival curves reach a plateau corresponding to the proportion of cured patients. The verification of this hypothesis is based on two criteria: a negligible excess mortality rate and a graphic confirmation of the existence of a plateau. We proposed a new definition of the time-to-cure as the time from which the probability of belonging to the cured group reaches 95%.The first aim of this thesis was to estimate time-to-cure for each cancer site by sex and age using population-based data from the FRANCIM registries network. Time-to-cure was lower than 12 years in most sites complying with the cure hypothesis. It was less than 5 years, or even null in some age groups, for skin melanoma, testicular and thyroid. Criteria to verify the cure hypothesis are subjective and time-to-cure is not directly estimated in pre-existing cure models. A new model has been developed including time-to-cure as a parameter to address the question of statistical cure and to allow direct estimation of time-to-cure.The second objective of this thesis was to compare, in controlled situations in which the excess mortality rate became null, the performances of this new model with that of two other cure models. Estimated net survival and cure fraction have been compared to the theoretical values used to simulate the data. Direct estimation of time-to-cure is possible under strict conditions.

MSc (Statistics) / Department of Statistics / This study explores the use of exible link functions in discrete survival models through a simulation study and an application to the Swaziland Demographic and Health Survey (SDHS) data. The objective of the research study is to perform simulation exercises in order to compare the e ectiveness of di erent families of link functions and to construct a discrete multilevel survival model for age at rst marriage among women in Swaziland using a exible link function. The Pareto hazard model, Pregibon and Gosset families of link functions were considered in models with and without unobserved heterogeneity. The Pareto model where the family parameter is estimated from the data was found to outperform the other models, followed by the Pregibon and the Gosset family of link functions. The results from both simulation study and real data analysis of the SDHS data illustrated that, misspecication of the link function causes bias on the estimation of results. This demonstrates the importance of choosing the right link. The ndings of this study reveal that women who are highly educated, stay in the Manzini and Shiselweni region, those who reside in urban areas were more likely to marry later compared to their counterparts in Swaziland. The results also reveal that the proportion of early rst marriages is declining since the di erence among birth cohorts is found to be very high, with women of younger cohorts getting married later compared to older women. / NRF

No description available.