Methods report (RTI Press)最新文献

The Forestry and Agriculture Sector Optimization Model with Greenhouse Gases (FASOMGHG) has historically relied on regional average costs of land conversion to simulate land use change across cropland, pasture, rangeland, and forestry. This assumption limits the accuracy of the land conversion estimates by not recognizing spatial heterogeneity in land quality and conversion costs. Using data from Nielsen et al. (2014), we obtained the afforestation cost per county, then estimated nonparametric regional marginal cost functions for land converting to forestry. These afforestation costs were then incorporated into FASOMGHG. Three different assumptions for land moving into the forest sector (constant average conversion cost, static rising marginal costs, and dynamic rising marginal cost) were run in order to assess the implications of alternative land conversion cost assumptions on key outcomes, such as projected forest area and cropland use, carbon sequestration, and forest product output.

Plotting growth curves is a powerful graphical approach used in exploratory data analysis for continuous longitudinal data. However, plotted growth curves for multiple participants rapidly become uninterpretable with categorical data. Categorical data define specific states (e.g., being single, married, divorced), and these states do not necessarily need to represent any hierarchical order. Thus, a trajectory becomes a sequence of states rather than a continuum. We introduce a horizontal line plot that uses shade or color to differentiate between states on a categorical longitudinal variable for multiple participants. With appropriate sorting, stacking the horizontal lines that represent each participant can reveal important patterns such as the shape of, or heterogeneity in, the trajectories. We illustrate the plotting techniques for large sample sizes, observed groups, the exploration of unobserved latent classes, large numbers of time points such as are found with intensive longitudinal designs or multivariate time series data, individually varying times observation, unique numbers of observations, and missing data. We used the R package longCatEDA to create the illustrations. Illustrative data include both simulated data and alcohol consumption data in adult schizophrenics from the Clinical Antipsychotic Trials of Intervention Effectiveness.

In 2011, the prevalence of prescription drug abuse exceeded that of any other illicit drug except marijuana. Consequently, efforts to curtail abuse of new medications should begin during the drug development process, where abuse liability can be identified and addressed before a candidate medication has widespread use. The first step in this process is scheduling with the Drug Enforcement Agency so that legal access is appropriately restricted, dependent upon levels of abuse risk and medical benefit. To facilitate scheduling, the Food and Drug Administration (FDA) has published guidance for industry that describes assessment of abuse liability. The purpose of this paper is to review methods that may be used to satisfy the FDA's regulatory requirements for animal behavioral and dependence pharmacology. Methods include psychomotor activity, self-administration (an animal model of the rewarding effects of a drug), drug discrimination (an animal model of the subjective effects of a drug), and evaluation of tolerance and dependence. Data from tests conducted at RTI with known drugs of abuse illustrate typical results, and demonstrate that RTI is capable of performing these tests. While using preclinical data to predict abuse liability is an imperfect process, it has substantial predictive validity. The ultimate goal is to increase consumer safety through appropriate scheduling of new medications.

Recognizing a need for rigorous, experimental research to support the efforts of workplaces and policymakers in improving the health and wellbeing of employees and their families, the National Institutes of Health and the Centers for Disease Control and Prevention formed the Work, Family & Health Network (WFHN). The WFHN is implementing an innovative multisite study with a rigorous experimental design (adaptive randomization, control groups), comprehensive multilevel measures, a novel and theoretically based intervention targeting the psychosocial work environment, and translational activities. This paper describes challenges and benefits of designing a multilevel and transdisciplinary research network that includes an effectiveness study to assess intervention effects on employees, families, and managers; a daily diary study to examine effects on family functioning and daily stress; a process study to understand intervention implementation; and translational research to understand and inform diffusion of innovation. Challenges were both conceptual and logistical, spanning all aspects of study design and implementation. In dealing with these challenges, however, the WFHN developed innovative, transdisciplinary, multi-method approaches to conducting workplace research that will benefit both the research and business communities.

The pervasive and potentially severe economic, social, and public health consequences of infectious disease in farmed animals require that plans be in place for a rapid response. Increasingly, agent-based models are being used to analyze the spread of animal-borne infectious disease outbreaks and derive policy alternatives to control future outbreaks. Although the locations, types, and sizes of animal farms are essential model inputs, no public domain nationwide geospatial database of actual farm locations and characteristics currently exists in the United States. This report describes a novel method to develop a synthetic dataset that replicates the spatial distribution of poultry farms, as well as the type and number of birds raised on them. It combines county-aggregated poultry farm counts, land use/land cover, transportation, business, and topographic data to generate locations in the conterminous United States where poultry farms are likely to be found. Simulation approaches used to evaluate the accuracy of this method when compared to that of a random placement alternative found this method to be superior. The results suggest the viability of adapting this method to simulate other livestock farms of interest to infectious disease researchers.

Gathering and communicating knowledge are important aspects of the scientific endeavor. Yet presentation of data in public forums such as scientific meetings and publications makes it available not only to scientists, but also to others who may have different ideas about how to use research findings. A recent example of this type of hijacking is the introduction of synthetic cannabinoids that are sprayed on herbal products and subsequently smoked for their marijuana-like intoxicating properties. Originally developed for the legitimate research purpose of furthering understanding of the cannabinoid system, these synthetic cannabinoids are being abused worldwide, creating issues for regulatory and law enforcement agencies that are struggling to keep up with the growing number of compounds of various structural motifs. Basic and clinical scientists need to provide advice now to facilitate decision-making about the health threats posed by this emerging problem.

Increasingly, researchers are turning to computational models to understand the interplay of important variables on systems' behaviors. Although researchers may develop models that meet the needs of their investigation, application limitations-such as nonintuitive user interface features and data input specifications-may limit the sharing of these tools with other research groups. By removing these barriers, other research groups that perform related work can leverage these work products to expedite their own investigations. The use of software engineering practices can enable managed application production and shared research artifacts among multiple research groups by promoting consistent models, reducing redundant effort, encouraging rigorous peer review, and facilitating research collaborations that are supported by a common toolset. This report discusses three established software engineering practices- the iterative software development process, object-oriented methodology, and Unified Modeling Language-and the applicability of these practices to computational model development. Our efforts to modify the MIDAS TranStat application to make it more user-friendly are presented as an example of how computational models that are based on research and developed using software engineering practices can benefit a broader audience of researchers.