In this renewal, the Data Management, Measurement, and Stafistics (DMMS) Core will confinue to focus on four primary aims: (1) to provide expert data entry, management, and stafistical support across projects;(2) to develop common, psychometricahly sound measures (notably measures of smoking behaviors and outcomes) to use across projects;(3) to implement cross project, integrated analyses;and (4) to propose and develop stafisfical approaches to address key issues related to the analysis of adolescent smoking data. (1) SUPPORT. As in the original project, we will develop and maintain a computerized database for data entry and management, and we will develop detailed documentation ofthe database. We will continue to ufilize rigorous methods for data entry, edifing, and updafing to ensure that the data are clean, consistent, and secure. We will also provide stafistical support and collaboration for all projects. (2) MEASURES. We will assess smoking at mulfiple fimes and with mulfiple measures.
A specific aim of the DMMS Core is to conceptualize and develop measures that will (a) aid in the identificafion of longitudinal smoking patterns and (b) aid in the interpretafion of contexts in which these patterns develop. (3) INTEGRATION. A key feature of the program project is that data will be available from the full cohort through questionnaires administered in Project 1 (four annual assessments) in addifion to data collected from subsets of the cohort in each of the other three projects. A major priority within the DMMS Core is to develop stafisfical models that allow integrated analyses of data from many sources. (4) DEVELOPMENT. Methodological research within the DMMS core will seek to advance the development of statisfical approaches for the analysis of smoking data. These efforts will be focused around three primary issues: the conceptualization/combinafion of mulfiple indices of """"""""dependence,"""""""" individual heterogeneity in smoking development across fime, and characterizing influences of heterogeneity.
Cigarette smoking remains a major public health problem. The DMMS core will provide support for a program of research that examines factors that influence smoking during adolescence and young adult hood. Our overall goal is to increase understanding of the longitudinal pattems of young adult smoking and the emofional and social contexts in which these take place.
|Selya, Arielle S; Dierker, Lisa; Rose, Jennifer S et al. (2018) The Role of Nicotine Dependence in E-Cigarettes' Potential for Smoking Reduction. Nicotine Tob Res 20:1272-1277|
|Selya, Arielle S; Rose, Jennifer S; Dierker, Lisa et al. (2018) Evaluating the mutual pathways among electronic cigarette use, conventional smoking and nicotine dependence. Addiction 113:325-333|
|Selya, Arielle S; Cannon, Dale S; Weiss, Robert B et al. (2018) The role of nicotinic receptor genes (CHRN) in the pathways of prenatal tobacco exposure on smoking behavior among young adult light smokers. Addict Behav 84:231-237|
|Xie, Hui; Gao, Weihua; Xing, Baodong et al. (2018) Measuring the Impact of Nonignorable Missingness Using the R Package isni. Comput Methods Programs Biomed 164:207-220|
|Lin, Xiaolei; Mermelstein, Robin J; Hedeker, Donald (2018) A 3-level Bayesian mixed effects location scale model with an application to ecological momentary assessment data. Stat Med 37:2108-2119|
|Dierker, Lisa; Mendoza, William; Goodwin, Renee et al. (2017) Marijuana use disorder symptoms among recent onset marijuana users. Addict Behav 68:6-13|
|Piasecki, Thomas M; Trela, Constantine J; Mermelstein, Robin J (2017) Hangover Symptoms, Heavy Episodic Drinking, and Depression in Young Adults: A Cross-Lagged Analysis. J Stud Alcohol Drugs 78:580-587|
|Gorka, Stephanie M; Hedeker, Donald; Piasecki, Thomas M et al. (2017) Impact of alcohol use motives and internalizing symptoms on mood changes in response to drinking: An ecological momentary assessment investigation. Drug Alcohol Depend 173:31-38|
|Pugach, Oksana; Cannon, Dale S; Weiss, Robert B et al. (2017) Classification Tree Analysis as a Method for Uncovering Relations Between CHRNA5A3B4 and CHRNB3A6 in Predicting Smoking Progression in Adolescent Smokers. Nicotine Tob Res 19:410-416|
|Hedeker, Donald; Mermelstein, Robin J; Demirtas, Hakan et al. (2016) A Mixed-effects Location-Scale Model for Ordinal Questionnaire Data. Health Serv Outcomes Res Methodol 16:117-131|
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