|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.
Nonlinear Varying Coefficient Models with Applications to Studying Photosynthesis.
Authors: Kürüm E, Li R, Wang Y, SEntürk D
Source: J Agric Biol Environ Stat, 2014 Mar 1;19(1), p. 57-81.
Estimating Mixture of Gaussian Processes by Kernel Smoothing.
Authors: Huang M, Li R, Wang H, Yao W
Source: J Bus Econ Stat, 2014;32(2), p. 259-270.
CALIBRATING NON-CONVEX PENALIZED REGRESSION IN ULTRA-HIGH DIMENSION.
Authors: Wang L, Kim Y, Li R
Source: Ann Stat, 2013 Oct 1;41(5), p. 2505-2536.
New methods for advancing research on tobacco dependence using ecological momentary assessments.
Authors: Lanza ST, Piper ME, Shiffman S
Source: Nicotine Tob Res, 2014 May;16 Suppl 2, p. S71-2.
Time-varying processes involved in smoking lapse in a randomized trial of smoking cessation therapies.
Authors: Vasilenko SA, Piper ME, Lanza ST, Liu X, Yang J, Li R
Source: Nicotine Tob Res, 2014 May;16 Suppl 2, p. S135-43.
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.
Changes in substance use-related health risk behaviors on the timeline follow-back interview as a function of length of recall period.
Authors: Buu A, Li R, Walton MA, Yang H, Zimmerman MA, Cunningham RM
Source: Subst Use Misuse, 2014 Aug;49(10), p. 1259-69.
EPub date: 2014 Mar 6.
Dose-dependent incidence of hepatic tumors in adult mice following perinatal exposure to bisphenol A.
Authors: Weinhouse C, Anderson OS, Bergin IL, Vandenbergh DJ, Gyekis JP, Dingman MA, Yang J, Dolinoy DC
Source: Environ Health Perspect, 2014 May;122(5), p. 485-91.
EPub date: 2014 Jan 17.
SEMIPARAMETRIC ESTIMATION OF CONDITIONAL HETEROSCEDASTICITY VIA SINGLE-INDEX MODELING.
Authors: Zhu L, Dong Y, Li R
Source: Stat Sin, 2013 Jul;23(3), p. 1215-1235.
Control Systems Engineering for Understanding and Optimizing Smoking Cessation Interventions.
Authors: Timms KP, Rivera DE, Collins LM, Piper ME
Source: Proc Am Control Conf, 2013;null, p. 1964-1969.
Anhedonia, depressed mood, and smoking cessation outcome.
Authors: Leventhal AM, Piper ME, Japuntich SJ, Baker TB, Cook JW
Source: J Consult Clin Psychol, 2014 Feb;82(1), p. 122-9.
EPub date: 2013 Nov 11.
Functional data analysis for dynamical system identification of behavioral processes.
Authors: Trail JB, Collins LM, Rivera DE, Li R, Piper ME, Baker TB
Source: Psychol Methods, 2014 Jun;19(2), p. 175-87.
EPub date: 2013 Sep 30.
A dynamical systems approach to understanding self-regulation in smoking cessation behavior change.
Authors: Timms KP, Rivera DE, Collins LM, Piper ME
Source: Nicotine Tob Res, 2014 May;16 Suppl 2, p. S159-68.
EPub date: 2013 Sep 24.
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.
Advancing the understanding of craving during smoking cessation attempts: a demonstration of the time-varying effect model.
Authors: Lanza ST, Vasilenko SA, Liu X, Li R, Piper ME
Source: Nicotine Tob Res, 2014 May;16 Suppl 2, p. S127-34.
EPub date: 2013 Aug 24.
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.
Modeling complexity of EMA data: time-varying lagged effects of negative affect on smoking urges for subgroups of nicotine addiction.
Authors: Shiyko M, Naab P, Shiffman S, Li R
Source: Nicotine Tob Res, 2014 May;16 Suppl 2, p. S144-50.
EPub date: 2013 Aug 3.
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.
Psychiatric diagnoses among quitters versus continuing smokers 3 years after their quit day.
Authors: Piper ME, Rodock M, Cook JW, Schlam TR, Fiore MC, Baker TB
Source: Drug Alcohol Depend, 2013 Feb 1;128(1-2), p. 148-54.
EPub date: 2012 Sep 17.