Longitudinal data comprise a response that is measured repeatedly over time, for a number of individual units, in a study. This might include measurement of serum proteins from nephrology patients over time, measurements of smoking behaviour over time in a smoking cessation trial or observational study, or measures of air particulates in geographical regions over time (e.g., Atmospheric Concentration of Chlorofluorocarbons: Addressing the Global Concern with the Longitudinal Bent-Cable Model; A Statistical Investigation to Monitor and Understand Atmospheric CFC Decline with the Spatial-longitudinal Bent-cable Model). When there exist two or more distinct responses each measured over time (e.g., three serum proteins such as albumin, C-reactive protein, and ceruloplasmin, or two measures of smoking activity, such as self-report and exhaled carbon monoxide, Multivariate longitudinal data analysis with mixed effects hidden Markov models), these can be defined as multivariate longitudinal data. Such data may be a collection of responses recorded at each of the same discrete set of time points (Dynamical Correlation for Multivariate Longitudinal Data), or, more generally, each response may be collected over time but at different time points from one another. In this latter case, a smoothing step is typically required to “connect” the different responses prior to modeling (A binning method for analyzing mixed longitudinal data measured at distinct time points).
We will be utilizing various longitudinal data analysis techniques for the Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) database, both for single longitudinal outcomes as well as for multivariate longitudinal outcomes. We are also developing new methodology for such analysis as needed.