Applied Research Topics | The Methodology Center

Applied Research Topics

Alcohol, Tobacco, and Other Drug Use

Tobacco UseMethodology Center researchers conducted several empirical analyses of drug abuse data using the time-varying effect model for intensive longitudinal data. Methodology Center researchers have modeled, for example, changes over time in the relationships between negative affect, smoking self-efficacy, and urge to smoke.

 

Bekiroglu, K., Lagoa, C., Murphy, S., & Lanza, S.T. (in press). A robust MPC approach to the design of treatments. Proceedings of the 52nd IEEE Conference on Decision and Control.

Selya, A. S., Updegrove, N., Rose, J. S., Dierker, L. D., Tan, X., Hedeker, D., et al. (2015). Nicotine-dependence-varying effects of smoking events on momentary mood changes among adolescents. Addictive Behaviors,41, 65-71.

Buu, A., Li, R., Walton, M., Yang, H., Zimmerman, M.A., & Cunningham, R.M. (2014). Changes in substance use-related health risk behaviors on timeline follow-back interview as a function of the length of recall period. Substance Use and Misuse, 49(10), 1259-1269.

Dziak, J., Li, R., Zimmerman, M., & Buu, A. (2014). Time-varying effect models for ordinal responses with applications in substance abuse research. Statistics in Medicine, 33(29), 5126-37.

Lagoa, C., Bekiroglu, K., Lanza, S.T., & Murphy, S. (2014). Designing adaptive intensive interventions using methods from engineering. Journal of Consulting and Clinical Psychology, 82(5), 868-878.

Shiyko, M., Burkhalter, J. E., Li, R., & Park, B. J. (2014). Modeling nonlinear time-dependent treatment effects: An application of the generalized time-varying effect model (TVEM). Journal of Consulting and Clinical Psychology, 82 (760-772). PMCID: PMC4067470

Timms, K. P., Rivera, D. E., Collins, L. M., & Piper, M. E. (2014).  A dynamical systems approach to understand self-regulation in smoking cessation behavior change. Nicotine and Tobacco Research, 16, S159-S168. PMCID: PMC3977628

Trail, J., Collins, L.M., Rivera, D.E., Li, R., Piper, M.E., & Baker, T.B. (2014). Functional data analysis for dynamical system identification of behavioral processes. Psychological Methods, 19, 175-187.

Vasilenko, S. A., Piper, M. E., Lanza, S. T. Liu, X., Yang, J. & Li, R. (2014). Time-varying processes involved in smoking lapse in a randomized trial of smoking cessation therapies. Nicotine and Tobacco Research, 16, (Suppl. 2), 135-143. PMCID: PMC4056442

Yang, H., Cranford, J. A., Li, R., & Buu, A. (2014). Two‐stage model for time‐varying effects of discrete longitudinal covariates with applications in analysis of daily process data. Statistics in Medicine. Advance online publication. doi: 10.1002/sim.6368

Selya, A.S., Dierker, L. C., Rose, J. S., Hedeker, D., Tan, X., Li, R., & Mermelstein, R.J. (2013). Time-varying effects of smoking quantity and nicotine dependence on adolescent smoking regularity. Drug and Alcohol Dependence. 128, 230-237. PMCID: PMC3538104

Shiyko, M., Naab, P., Shiffman, S., & Li, R. (2013). Modeling complexity of EMA data: time-varying lagged effects of negative affect on smoking urges for subgroups of nicotine addiction. Nicotine & Tobacco Research, 16, S144-150

Lanza, S. T., Vasilenko, S., Liu, X., Li, R., & Piper, M. (2013). Advancing the understanding of craving during smoking cessation attempts: A demonstration of the time-varying effect model. Nicotine and Tobacco Research, 16, S127-134.

Liu, X., Li, R., Lanza, S.T., Vasilenko, S., & Piper, M. (2013). Understanding the role of cessation fatigue in the smoking cessation process. Drug and Alcohol Dependence. 133, 548 - 555. PMC Journal- In Process

Shiyko, M. P., Lanza, S. T., Tan, X., Li, R. and Shiffman, S. (2012). Using the time-varying effect model (TVEM) to examine dynamic associations between negative affect and self confidence on smoking urges: differences between successful quitters and relapsers. Prevention Science. PMCID: PMC3372905 doi: 10.1007/s11121-011-0264-z

Tan, X., Dierker, L., Li, R., Rose, J., and The Tobacco Etiology Research Network (TERN). (2011). How spacing of data collection may impact estimates of substance use trajectories. Substance Use and Misuse. 46, 758-768. PMCID: PMC3107528

Dierker, L., Rose, J., Tan, X., Li, R. and The Tobacco Etiology Research Network (TERN). (2010). Uncovering multiple pathways to substance use: A comparison of methods for identifying population subgroups. The Journal of Primary Prevention. 31, 333-348. PMCID: PMC3107529

 


 

 


 

Genetics

Scientists in the Methodology Center have applied their proposed statistical procedures for longitudinal data to a genome-wide association study using the longitudinal genetic data collected in Framingham Heart Study and other longitudinal genetics data in the following papers related to the genome-wide association study.

 

Liu, J., Li, R., & Wu, R. (2014). Feature selection for varying coefficient models with ultrahigh-dimensional covariates. Journal of the American Statistical Association, 109, 266-274.

Das, K., Li, J., Fu, G.,Wang, Z., Li, R. & Wu, R. (2013). Dynamic semiparametric Bayesian models for genetic mapping of complex trait with irregular longitudinal data. Statistics in Medicine, 32, 509 - 523. PMCID: PMC3770845

Das, K., Li, R., Sengupta, S., & Wu, R. (2013). A Bayesian semiparametric model for bivariate sparse longitudinal data. Statistics in Medicine, 32, 3899-3910.  doi: 10.1002/sim.5790 PMCID: PMC3740051

Wang, Y., Huang, C., Fang, Y., Yang, Q., & Li, R. (2012). Flexible semiparametric analysis of longitudinal genetic studies by reduced rank smoothing. Journal of the Royal Statistical Society, Series C. 61, 1-24. PMCID:PMC3348702

Fu, G., Luo, J., Berg, A., Wang, Z., Li, J., Das, K., Li, R., & Wu, L. (2011). A dynamic model for functional mapping of biological rhythms. Journal of Biological Dynamics. 5, 84-101. PMCID: PMC3027063

Das, K., Li, J., Wang, Z., Gu, G., Tong, C. Li, Y., Xu, M., Ahn, K., Mauger, D.T. Li, R., & Wu, R. (2011). A dynamic model for genome-wide association studies. Human Genetics. 129, 629-639. PMCID: PMC3103104

 


 

Neuroimaging

Neuroimaging

Functional MRI and neuroimaging produce types of intensive longitudinal data. Researchers in the Methodology Center have applied multivariate TVEMs for analysis of neuroimaging data.

 

Zhu, H., Li, R., & Kong, L. (2012). Multivariate varying coefficient model for functional responses. Annals of Statistics, 40, 2634 - 2666. PMCID: PMC3464104

Zhu, H., Kong, L., Li, R., Styner, M., Gerig, G., Lin, W., & Gilmore, J. H. (2011). FADTTS: Functional analysis of diffusion tensor tract statistics. Neuroimage, 56, 1412–1425. PMCID: PMC3085665

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