Population-based Simulation Research Articles

Pharmacokinetics of Voriconazole Administered Concomitantly with Fluconazole and Population-Based Simulation for Sequential Use

In clinical practice, antifungal therapy may be switched from fluconazole to voriconazole; such sequential use poses the potential for drug interaction due to cytochrome P450 2C19 (CYP2C19)-mediated inhibition of voriconazole metabolism. This open-label, randomized, two-way crossover study investigated the effect of concomitant fluconazole on voriconazole pharmacokinetics in 10 subjects: 8 extensive metabolizers and 2 poor metabolizers of CYP2C19. The study consisted of 4-day voriconazole-only and 5-day voriconazole-plus-fluconazole treatments, separated by a 14-day washout. Voriconazole pharmacokinetics were determined by noncompartmental analyses. A physiologically based pharmacokinetic model was developed in Simcyp (Simcyp Ltd., Sheffield, United Kingdom) to predict the magnitude of drug interaction should antifungal therapy be switched from fluconazole to voriconazole, following various simulated lag times for the switch. In CYP2C19 extensive metabolizers, fluconazole increased the maximum plasma concentration and the area under the plasma concentration-time curve (AUC) of voriconazole by 57% and 178%, respectively. In poor metabolizers, however, voriconazole pharmacokinetics were unaffected by fluconazole. The simulations based on pharmacokinetic modeling predicted that if voriconazole was started 6, 12, 24, or 36 h after the last dose of fluconazole, the voriconazole AUC ratios (sequential therapy versus voriconazole only) after the first dose would be 1.51, 1.41, 1.28, and 1.14, respectively. This suggests that the remaining systemic fluconazole would result in a marked drug interaction with voriconazole for ≥ 24 h. Although no safety issues were observed during coadministration, concomitant use of fluconazole and voriconazole is not recommended. Frequent monitoring for voriconazole-related adverse events is advisable if voriconazole is used sequentially after fluconazole.

Agent-based vs. population-based simulation of displacement of crime: A comparative study

Central research questions addressed within Criminology are how the geographical displacement of crime can be understood, explained, and predicted. The process of crime displacement is usually explained by referring to the interaction of three types of agents: criminals, passers-by, and guardians. Most existing simulation models of this process take a ‘local’ perspective, i.e., they are agent-based. However, when the number of agents considered becomes large, more ‘global’ approaches, such as population-based simulation have computational advantages over agent-based simulation. This article presents both an agent-based and a population-based simulation model of crime displacement, and reports a comparative evaluation of the two models. In addition, an approach is put forward to analyse the behaviour of both models by means of formal techniques. The results suggest that under certain conditions, population-based models approximate agent-based models, at least in the domain under investigation.

Correction: Development, Calibration, and Validation of a U.S. White Male Population-Based Simulation Model of Esophageal Adenocarcinoma

Correction: Development, Calibration, and Validation of a U.S. White Male Population-Based Simulation Model of Esophageal Adenocarcinoma

Development, Calibration, and Validation of a U.S. White Male Population-Based Simulation Model of Esophageal Adenocarcinoma

BackgroundThe incidence of esophageal adenocarcinoma (EAC) has risen rapidly in the U.S. and western world. The aim of the study was to begin the investigation of this rapid rise by developing, calibrating, and validating a mathematical disease simulation model of EAC using available epidemiologic data.MethodsThe model represents the natural history of EAC, including the essential biologic health states from normal mucosa to detected cancer. Progression rates between health states were estimated via calibration, which identified distinct parameter sets producing model outputs that fit epidemiologic data; specifically, the prevalence of pre-cancerous lesions and EAC cancer incidence from the published literature and Surveillance, Epidemiology, and End Results (SEER) data. As an illustrative example of a clinical and policy application, the calibrated and validated model retrospectively analyzed the potential benefit of an aspirin chemoprevention program.ResultsModel outcomes approximated calibration targets; results of the model's fit and validation are presented. Approximately 7,000 cases of EAC could have been prevented over a 30-year period if all white males started aspirin chemoprevention at age 40 in 1965.ConclusionsThe model serves as the foundation for future analyses to determine a cost-effective screening and management strategy to prevent EAC morbidity and mortality.

Development of a population-based microsimulation model of osteoarthritis in Canada

The purpose of the study was to develop a population-based simulation model of osteoarthritis (OA) in Canada that can be used to quantify the future health and economic burden of OA under a range of scenarios for changes in the OA risk factors and treatments. In this article we describe the overall structure of the model, sources of data, derivation of key input parameters for the epidemiological component of the model, and preliminary validation studies. We used the Population Health Model (POHEM) platform to develop a stochastic continuous-time microsimulation model of physician-diagnosed OA. Incidence rates were calibrated to agree with administrative data for the province of British Columbia, Canada. The effect of obesity on OA incidence and the impact of OA on health-related quality of life (HRQL) were modeled using Canadian national surveys. Incidence rates of OA in the model increase approximately linearly with age in both sexes between the ages of 50 and 80 and plateau in the very old. In those aged 50+, the rates are substantially higher in women. At baseline, the prevalence of OA is 11.5%, 13.6% in women and 9.3% in men. The OA hazard ratios for obesity are 2.0 in women and 1.7 in men. The effect of OA diagnosis on HRQL, as measured by the Health Utilities Index Mark 3 (HUI3), is to reduce it by 0.10 in women and 0.14 in men. We describe the development of the first population-based microsimulation model of OA. Strengths of this model include the use of large population databases to derive the key parameters and the application of modern microsimulation technology. Limitations of the model reflect the limitations of administrative and survey data and gaps in the epidemiological and HRQL literature.

EMERGENCE OF SOCIAL CONVENTIONS: AN APPROACH BASED ON DECENTRALIZED INTERACTIONS OF INDIVIDUALS AND STRATEGIES CHARACTERIZED BY PROBABILISTIC SETS

There is an agreement that the existence of social conventions is relevant for the individual economic behavior, but it still is necessary to explain both, the origin of conventions and the process of formation of conventions. In this paper we adopt the path-depended interaction, decentralized evolutionary process approach to address conventions and their formation. We suggest a population-based simulation model of bounded-rational individuals with incomplete information that, with some degree, interact imitating the behavior observed from other individuals of the population. The model uses decentralized interactions strategies in which unanimity and majority emerge. We also suggest an interaction strategy based on probabilistic. sets. The use of probabilistic sets introduces heterogeneity and improve individual rationality, turning model much more realistic. The model has been used to investigate the emergence theory of money and the technological standard choice theory. The results show that the model faithfully captures the essence of the behaviors expected in these scenarios.

On population-based simulation for static inference

In this paper we present a review of population-based simulation for static inference problems. Such methods can be described as generating a collection of random variables {X n } n=1,?,N in parallel in order to simulate from some target density ? (or potentially sequence of target densities). Population-based simulation is important as many challenging sampling problems in applied statistics cannot be dealt with successfully by conventional Markov chain Monte Carlo (MCMC) methods. We summarize population-based MCMC (Geyer, Computing Science and Statistics: The 23rd Symposium on the Interface, pp. 156---163, 1991; Liang and Wong, J. Am. Stat. Assoc. 96, 653---666, 2001) and sequential Monte Carlo samplers (SMC) (Del Moral, Doucet and Jasra, J. Roy. Stat. Soc. Ser. B 68, 411---436, 2006a), providing a comparison of the approaches. We give numerical examples from Bayesian mixture modelling (Richardson and Green, J. Roy. Stat. Soc. Ser. B 59, 731---792, 1997).