|Grant Number:||1R01CA168676-01 Interpret this number|
|Primary Investigator:||Lanza, Stephanie|
|Organization:||Pennsylvania State University-Univ Park|
|Project Title:||Advancing Tobacco Research By Integrating Systems Science and Mixture Models|
DESCRIPTION (provided by applicant): 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. PUBLIC HEALTH RELEVANCE: 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.
Feature Selection for Varying Coefficient Models With Ultrahigh Dimensional Covariates.
Authors: Liu J, Li R, Wu R
Source: J Am Stat Assoc, 2014 Jan 1;109(505), p. 266-274.
Cumulative association between age-related macular degeneration and less studied genetic variants in PLEKHA1/ARMS2/HTRA1: a meta and gene-cluster analysis.
Authors: Yu W, Dong S, Zhao C, Wang H, Dai F, Yang J
Source: Mol Biol Rep, 2013 Oct;40(10), p. 5551-61.
EPub date: 2013 Sep 7.
Clinical examination for prognostication in comatose cardiac arrest patients.
Authors: Greer DM, Yang J, Scripko PD, Sims JR, Cash S, Wu O, Hafler JP, Schoenfeld DA, Furie KL
Source: Resuscitation, 2013 Nov;84(11), p. 1546-51.
EPub date: 2013 Aug 15.
Understanding the role of cessation fatigue in the smoking cessation process.
Authors: Liu X, Li R, Lanza ST, Vasilenko SA, Piper M
Source: Drug Alcohol Depend, 2013 Dec 1;133(2), p. 548-55.
EPub date: 2013 Aug 2.
An adaptive truncated product method for combining dependent p-values.
Authors: Sheng X, Yang J
Source: Econ Lett, 2013 May;119(2), p. 180-182.
Early lapses in a cessation attempt: lapse contexts, cessation success, and predictors of early lapse.
Authors: Deiches JF, Baker TB, Lanza S, Piper ME
Source: Nicotine Tob Res, 2013 Nov;15(11), p. 1883-91.
EPub date: 2013 Jun 18.