Methodological and Technical Research Topics | The Methodology Center

Methodological and Technical Research Topics

Joint Modeling of Longitudinal Data and Time-to-Event (Survival) Data

Researchers are interested in two primary responses in the analysis of longitudinal data: longitudinal process response and time-to-event response. It is challenging to model these two responses jointly due to the censoring of the time-to-event response. Methodology Center researchers have developed new statistical procedures for joint modeling and applied them to empirical analyses in Das et al. (2013) and Shiyko et al. (2014).

 

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

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


Time-Varying Effect Models (TVEM)

Graph of an irregular curve with confidence bandsThe time-varying effect model developed by Methodology Center researchers allows researchers to estimate the time-varying effects of coefficients. A natural extension of linear regression models, TVEM allows coefficients to vary over time. This flexible approach allows the mean trajectory and effects of covariates to vary with time without assuming parametric (e.g., linear or quadratic) functions. Tan, Shiyko, Li, Li, & Dierker (2012) provides a detailed introduction to time-varying effect models for audiences in the psychological sciences. An empirical demonstration of the %TVEM macro appears in Shiyko, Lanza, Tan, Li, & Shiffman (2012). Both articles make use of the %TVEM SAS macro, developed by Center scientists to enable applied researchers to employ TVEM.

 

Chen, Z., Li, R., & Li, Y. (in press). Varying-coefficient models for data with auto-correlated error process. <em">Statistica Sinica.

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 & Tobacco Research, 16, S135-143.

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(5), 760-772.

Shiyko, M., Naab, P., Shiffman, S., & Li, R. (2014). 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. (2014). Advancing the understanding of craving during smoking cessation attempts: A demonstration of the time-varying effect model. Nicotine and Tobacco Research, 16, S127-134. 

Huang, M., Li, R., & Wang, S. (2013). Nonparametric mixture of regression models. Journal of the American Statistical Association, 108, 929-941. PMCID: PMC3865811

Yao, W., & LI, R. (2013). New local estimation procedure for nonparametric regression function of longitudinal data. Journal of the Royal Statistical Society, Series B, 75, 123-138. PMCID: PMC3607647

Zhu, L., Dong, Y., & LI, R. (2013). Semiparametric estimation of conditional heteroscedasticity through single index modeling. Statistica Sinica, 24, 1235-1256. PMCID: PMC3901164

Das, K., Li, R., Sengupta, S., & 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.

Zhu, L., Li, R., & Cui, H. (2013). Robust estimation for partially linear models with large-dimensional covariates. Science China: Mathematics, 56, 1247-1262. 

Tan, X., Shiyko, M. P., Li, R., Li, Y., & Dierker, L. (2012). A time-varying effect model for intensive longitudinal data. Psychological Methods, 17, 61-77. PMCID: PMC3288551 

Shiyko, M. P., Lanza, S. T., Tan, X., Li, R., & 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, 13, 288-299. PMCID: PMC3171604

Tan, X., Dierker, L., Rose, J., Li, R., & Tobacco Etiology Research Network. (2011). How spacing of data collection may impact estimates of substance use trajectories. Substance Use & Misuse, 46(6), 758-68. doi: 10.1007/s11121-011-0217-6

Kai, B., Li, R., & Zou, H. (2011). New efficient estimation and variable selection methods for semiparametric varying-coefficient partially linear models. Annals of Statistics, 39, 305-332. PMCID: PMC3109949

Fan, J. Huang, T., & Li, R. (2007). Analysis of longitudinal data with semiparametric estimation of covariance function. Journal of 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.

 


Functional Hierarchical Linear Model (FHLM)

Functional hierarchical linear models (FHLMs) extend mixed effects models, hierarchical linear models, and multilevel models by allowing both fixed effects and random effects (i.e. individual effects) in these models to vary over time. Li, Root and Shiffman (2006) proposed FHLMs for analysis of ecological momentary assessment (EMA) data collected in smoking cession study. This article makes use of the %FHLMLLR SAS macro (an acronym for functional hierarchical linear model using local linear regression), developed by center scientists in 2009. This macro allows prevention scientists to graphically represent these complex relationships.

 

Li, R., Root, T. L., & Shiffman, S. (2006). A local linear estimation procedure for functional multilevel modeling. In T. A. Walls & J. L. Schafer (Eds.), Models for intensive longitudinal data (pp. 63-83). New York, NY: Oxford University Press 


Software

We distribute free software to researchers so they can use our methods accurately and easily.

 

The Methodology Center first released %TVEM SAS macro for SAS in 2011, and continues to improve and expand the software. There are currently four versions of the %TVEM macro, all of which are available in a single download.

  • %TVEM_normal for data with a normal distribution
  • %TVEM_poisson for data with a poisson distribution
  • %TVEM_zip for data with a zero-inflated poisson distribution
  • %TVEM_logistic for binary data with a logistic distribution
 

We developed the SAS macro for Estimation of Functional Hierarchical Linear Models (FHLM) Using Local Linear Regression Estimation Procedure in 2009.

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