Grant Number:	1R01CA168676-01 Interpret this number
Primary Investigator:	Lanza, Stephanie
Organization:	Pennsylvania State University, The
Project Title:	Advancing Tobacco Research By Integrating Systems Science and Mixture Models
Fiscal Year:	2012

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.




Affect Variability in Relation to Alcohol Use Frequency, Intensity, and Concurrent Cannabis Use Among College Students during the COVID-19 Pandemic.
Authors: Rahal D. , Linden-Carmichael A.N. , Lanza S.T. .
Source: Addiction Research & Theory, 2024; 32(5), p. 375-383.
EPub date: 2023-11-24 00:00:00.0.
PMID: 39444433
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Evaluating the effect of smoking cessation treatment on a complex dynamical system.
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EPub date: 2017-08-25 00:00:00.0.
PMID: 28922651
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Emergence Of Electronic Cigarette Use In Us Adolescents And The Link To Traditional Cigarette Use
Authors: Lanza S.T. , Russell M.A. , Braymiller J.L. .
Source: Addictive Behaviors, 2017 Apr; 67, p. 38-43.
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What a difference a day makes: differences in initial abstinence response during a smoking cessation attempt.
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Source: Addiction (abingdon, England), 2017 Feb; 112(2), p. 330-339.
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Time-varying Effect Modeling To Address New Questions In Behavioral Research: Examples In Marijuana Use
Authors: Lanza S.T. , Vasilenko S.A. , Russell M.A. .
Source: Psychology Of Addictive Behaviors : Journal Of The Society Of Psychologists In Addictive Behaviors, 2016 Dec; 30(8), p. 939-954.
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A Semivarying Joint Model For Longitudinal Binary And Continuous Outcomes
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Latent Class Analysis for Developmental Research.
Authors: Lanza S.T. .
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Heterogeneity of alcohol, tobacco, and other substance use behaviors in U.S. college students: A latent class analysis.
Authors: Evans-Polce R. , Lanza S. , Maggs J. .
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Regularized Quantile Regression and Robust Feature Screening for Single Index Models.
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Age-varying associations between substance use behaviors and depressive symptoms during adolescence and young adulthood.
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New methods shed light on age of onset as a risk factor for nicotine dependence.
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A time-varying effect model for studying gender differences in health behavior.
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Source: The Journal Of Adolescent Health : Official Publication Of The Society For Adolescent Medicine, 2015 Aug; 57(2), p. 198-204.
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Source: Addictive Behaviors, 2015 Feb; 41, p. 65-71.
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Changes in gender and racial/ethnic disparities in rates of cigarette use, regular heavy episodic drinking, and marijuana use: ages 14 to 32.
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Designing adaptive intensive interventions using methods from engineering.
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