Smoking is the leading preventable cause of disease, disability, and death in the United States, but approximately one-fifth of adults smoke cigarettes. Among the approximately 15 million smokers who make a quit attempt every year, the great majority eventually relapse even with smoking cessation aids. While much is known about the etiology of smoking dependence, substantial work remains to effectively help smokers quit and ultimately prevent smoking-related death, most commonly due to cancer, and disease. Smoking cessation occurs within the context of a wide variety of interrelated individual and environmental factors, many of which change rapidly during the first few weeks after quitting. We propose two areas of scientific inquiry to substantially improve smoking cessation outcomes. First, a better understanding of the complex system dynamics that unfold during the smoking cessation process will guide clinicians in the development of interventions that adapt over time to individuals'changing needs and response to particular treatments. Second, a more thorough scientific understanding of differential treatment effects for individuals with different profiles t baseline will guide clinicians in selection of treatments that hold the most promise for different types of individuals. The overall goal of this project is to further the science of smoking cessation by integrating a novel systems-science approach, time-varying effect models, and mixture models, and apply the new approach to analysis of ecological momentary assessment (EMA) data on tobacco use.
The specific aims of this project are (1) To establish the relation between the experience of withdrawal over time and survival to smoking cessation milestones (lapse and relapse), and examine the impact of treatment condition, baseline characteristics, and time-varying covariates;(2) To examine differential treatment effects across latent subgroups of individuals reflecting key combinations of baseline factors;(3) To identify latent subgroups characterized by unique dynamic processes occurring during a smoking cessation attempt;and (4) To promote and facilitate uptake of these innovative statistical approaches by tobacco researchers. Results from the proposed project will inform the construction of highly effective smoking cessation interventions that (1) are tailored to the individual and (2) adapt to participant response over time. Importantly, the overall impact of this project extends far beyond the proposed set of analyses;this project will accelerate the pace of smoking cessation research in a sustained, powerful way through rapid, programmatic dissemination of important new analytic methods and design considerations to tobacco researchers.
Recent advances in data collection for clinical trials hold the key to understanding the complex dynamics of smoking quit attempts. This project will apply an innovative systems-science analytic approach and mixture models to intensive longitudinal data from a smoking cessation trial. This project will enable the construction of smoking interventions that are tailored to individuals, adapt to individuals'needs over time, and ultimately reduce smoking-related morbidity and mortality.
|Evans-Polce, Rebecca; Lanza, Stephanie; Maggs, Jennifer (2016) Heterogeneity of alcohol, tobacco, and other substance use behaviors in U.S. college students: A latent class analysis. Addict Behav 53:80-5|
|Zhong, Wei; Zhu, Liping; Li, Runze et al. (2016) Regularized Quantile Regression and Robust Feature Screening for Single Index Models. Stat Sin 26:69-95|
|KÃ¼rÃ¼m, Esra; Li, Runze; Shiffman, Saul et al. (2016) TIME-VARYING COEFFICIENT MODELS FOR JOINT MODELING BINARY AND CONTINUOUS OUTCOMES IN LONGITUDINAL DATA. Stat Sin 26:979-1000|
|Piper, Megan E; Vasilenko, Sara A; Cook, Jessica W et al. (2016) What a difference a day makes: differences in initial abstinence response during a smoking cessation attempt. Addiction :|
|Yang, Hanyu; Li, Runze; Zucker, Robert A et al. (2016) Two-stage model for time varying effects of zero-inï¬‚ated count longitudinal covariates with applications in health behaviour research. J R Stat Soc Ser C Appl Stat 65:431-444|
|Lanza, Stephanie T; Vasilenko, Sara A; Russell, Michael A (2016) Time-Varying Effect Modeling to Address New Questions in Behavioral Research: Examples in Marijuana Use. Psychol Addict Behav :|
|KÃ¼rÃ¼m, Esra; Hughes, John; Li, Runze (2016) A semivarying joint model for longitudinal binary and continuous outcomes. Can J Stat 44:44-57|
|Schuler, Megan S; Vasilenko, Sara A; Lanza, Stephanie T (2015) Age-varying associations between substance use behaviors and depressive symptoms during adolescence and young adulthood. Drug Alcohol Depend 157:75-82|
|Lanza, Stephanie T; Vasilenko, Sara A; Dziak, John J et al. (2015) Trends Among U.S. High School Seniors in Recent Marijuana Use and Associations With Other Substances: 1976-2013. J Adolesc Health 57:198-204|
|Lanza, Stephanie T; Vasilenko, Sara A (2015) New methods shed light on age of onset as a risk factor for nicotine dependence. Addict Behav 50:161-4|
Showing the most recent 10 out of 38 publications