RESUMEN
Computer simulations have played an indispensable role in the development and applications of statistical models and methods for genetic studies across multiple disciplines. The need to simulate complex evolutionary scenarios and pseudo-datasets for various studies has fueled the development of dozens of computer programs with varying reliability, performance, and application areas. To help researchers compare and choose the most appropriate simulators for their studies, we have created the genetic simulation resources (GSR) website, which allows authors of simulation software to register their applications and describe them with more than 160 defined attributes. This article summarizes the properties of 93 simulators currently registered at GSR and provides an overview of the development and applications of genetic simulators. Unlike other review articles that address technical issues or compare simulators for particular application areas, we focus on software development, maintenance, and features of simulators, often from a historical perspective. Publications that cite these simulators are used to summarize both the applications of genetic simulations and the utilization of simulators.
Asunto(s)
Simulación por Computador , Modelos Genéticos , Programas Informáticos , Modelos Estadísticos , Reproducibilidad de los ResultadosRESUMEN
BACKGROUND: Characterizing the smoking patterns for different birth cohorts is essential for evaluating the impact of tobacco control interventions and predicting smoking-related mortality, but the process of estimating birth cohort smoking histories has received limited attention. PURPOSE: Smoking history summaries were estimated beginning with the 1890 birth cohort in order to provide fundamental parameters that can be used in studies of cigarette smoking intervention strategies. METHODS: U.S. National Health Interview Surveys conducted from 1965 to 2009 were used to obtain cross-sectional information on current smoking behavior. Surveys that provided additional detail on history for smokers including age at initiation and cessation and smoking intensity were used to construct smoking histories for participants up to the date of survey. After incorporating survival differences by smoking status, age-period-cohort models with constrained natural splines were used to estimate the prevalence of current, former, and never smokers in cohorts beginning in 1890. This approach was then used to obtain yearly estimates of initiation, cessation, and smoking intensity for the age-specific distribution for each birth cohort. These rates were projected forward through 2050 based on recent trends. RESULTS: This summary of smoking history shows clear trends by gender, cohort, and age over time. If current patterns persist, a slow decline in smoking prevalence is projected from 2010 through 2040. CONCLUSIONS: A novel method of generating smoking histories has been applied to develop smoking histories that can be used in micro-simulation models, and has been incorporated in the National Cancer Institute's Smoking History Generator. These aggregate estimates developed by age, gender, and cohort will provide a complete source of smoking data over time.
Asunto(s)
Fumar/epidemiología , Fumar/tendencias , Estudios de Cohortes , Femenino , Humanos , Masculino , Modelos Estadísticos , Prevalencia , Cese del Hábito de Fumar/estadística & datos numéricos , Estados Unidos/epidemiologíaRESUMEN
SUMMARY: Many simulation methods and programs have been developed to simulate genetic data of the human genome. These data have been widely used, for example, to predict properties of populations retrospectively or prospectively according to mathematically intractable genetic models, and to assist the validation, statistical inference and power analysis of a variety of statistical models. However, owing to the differences in type of genetic data of interest, simulation methods, evolutionary features, input and output formats, terminologies and assumptions for different applications, choosing the right tool for a particular study can be a resource-intensive process that usually involves searching, downloading and testing many different simulation programs. Genetic Simulation Resources (GSR) is a website provided by the National Cancer Institute (NCI) that aims to help researchers compare and choose the appropriate simulation tools for their studies. This website allows authors of simulation software to register their applications and describe them with well-defined attributes, thus allowing site users to search and compare simulators according to specified features. AVAILABILITY: http://popmodels.cancercontrol.cancer.gov/gsr.