Background This paper describes the methods and conceptual framework for Wave 1 of the Population Assessment of Tobacco and Health (PATH) Study data collection. The National Institutes of Health, through the National Institute on Drug Abuse, is partnering with the Food and Drug Administration’s (FDA) Center for Tobacco Products to conduct the PATH Study under a contract with Westat. Methods The PATH Study is a nationally representative, longitudinal cohort study of 45 971 adults and youth in the USA, aged 12 years and older. Wave 1 was conducted from 12 September 2013 to 15 December 2014 using Audio Computer-Assisted Self-Interviewing to collect information on tobacco-use patterns, risk perceptions and attitudes towards current and newly emerging tobacco products, tobacco initiation, cessation, relapse behaviours and health outcomes. The PATH Study’s design allows for the longitudinal assessment of patterns of use of a spectrum of tobacco products, including initiation, cessation, relapse and transitions between products, as well as factors associated with use patterns. Additionally, the PATH Study collects biospecimens from consenting adults aged 18 years and older and measures biomarkers of exposure and potential harm related to tobacco use. Conclusions The cumulative, population-based data generated over time by the PATH Study will contribute to the evidence base to inform FDA’s regulatory mission under the Family Smoking Prevention and Tobacco Control Act and efforts to reduce the Nation’s burden of tobacco-related death and disease.
Many students who start college intending to major in science or engineering do not graduate, or decide to switch to a non-science major. We used the recently developed statistical method of random forests to obtain a new perspective of variables that are associated with persistence to a science or engineering degree. We describe classification trees and random forests and contrast the results from these methods with results from the more commonly used method of logistic regression. Among the variables available in Arizona State University data, high school and freshman year GPAs have highest importance for predicting persistence; other variables such as number of science and engineering courses taken freshman year are important for subgroups of the student population. The method used in this study could be employed in other settings to identify faculty practices, teaching methods, and other factors that are associated with high persistence to a degree.
Summary. The paper reviews the growing literature on responsive and adaptive designs for surveys. These designs encompass various methods for managing data collection, including front loading potentially difficult cases, tailoring data collection strategies to different subgroups, prioritizing effort according to estimated response propensities, imposing stop rules for ending data collection, monitoring key survey estimates throughout the field period, using two-phase or multiphase sampling for following up non-respondents and calculating indicators of non-response bias (such as the R-indicator) other than response rates to monitor and guide fieldwork. We give particular attention to efforts to evaluate these strategies experimentally or via simulations. Although the field seems to have embraced these new tools, most of the evaluation studies suggest they produce marginal reductions in cost and non-response bias. It is clearly difficult to lower survey costs without reducing some aspect of survey quality. Other issues limiting the effectiveness of these designs include weakly predictive auxiliary variables, ineffective interventions and slippage in the implementation of interventions in the field. These problems are not, however, unique to responsive or adaptive design. We give recommendations for improving such designs and for improving the management of data collection efforts in the current difficult environment for surveys.
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