Tobacco Use

Methodology Center researchers conducted several empirical analyses of smoking data using the time-varying effect model for ILD. These appear in Dierker et al. (2010), Tan et al. (2011) and Shiyko et al. (2012). Methodology Center researchers have modeled changes over time in the relationships between negative affect, smoking self-efficacy, and urge to smoke.

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

AIDS and HIV

Methodology Center researchers have developed time-varying effect models and semi-time-varying effect models that are useful in analyzing data collected from AIDS and HIV studies. A subset of data has been analyzed in Fan and Li (2004), Qu and Li (2006) and Fan, Huang and Li (2007) as demonstrations of the proposed methods.

 

Fan, J. Huang, T., & Li, R. (2007). Analysis of longitudinal data with semiparametric estimation of covariance function. Journal of the American Statistical Association. 102, 632-641. PMCID: PMC2730591

Qu, A. P., & Li, R. (2006). Quadratic inference functions for varying-coefficient models with longitudinal data. Biometrics. 62, 379-391. PMCID: PMC2680010

Fan, J., & Li, R. (2004). New estimation and model selection procedures for semiparametric modeling in longitudinal data analysis. Journal of the American Statistical Association, 99, 710-723.

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.

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

Functional MRI and neuroimaging produce types of intensive longitudinal data. Researchers in the Methodology Center applied multivariate TVEMs for analysis of neuroimaging data in Zhu et al. (2011).

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