Longitudinal and Correlated Data
Correlated data are common in many health sciences studies, where clustered, hierarchical, non-independent, and spatial data are frequently observed. A common feature of such data is that observations are correlated, and statistical analysis requires taking such correlation into account. Examples of clustered data include longitudinal data, familial data, genetic data, genomic data, and analysis of multiple outcomes or recurrent events. Hierarchical data are common in multi-center clinical trials and community/school-based intervention studies, where correlation is due to several levels of clustering, such as schools and classes. Spatial data arise in disease mapping, ecology, environmental health and brain imaging, where data are correlated due to spatial proximity. Correlated data are also observed in various genetic and genomic studies. Faculty in Biostatistics are engaged in the design and the development of statistical methodology for such correlated data. Examples of research areas include random effects models, estimating equations, missing data, multiple outcomes, nonparametric/semiparametric regression, measurement error models, and joint modeling survival and longitudinal outcomes.
Faculty: V. Baladandayuthapani, M. Banerjee, T. Braun, M. Elliott, P. Han, N. Kaciroti, J. Kalbfleisch, R. Little, P. Song, J. Taylor, L. Wang, Z. Wu, W. Ye, M. Zhang, X. Zhou