Functional data arise in many fields of medical research, with examples from studies of growth patterns, gait, melanoma incidence rates, CD4 counts and many other areas. Indeed, any set of measurements gathered over time (or space), including time dependent covariates in survival analysis, may be thought of as functional data. There are many advantages of viewing such data as functions rather than disconnected points, perhaps the most important being the ability to routinely including derivative information into the analysis. Historically, functional data has been analyzed using multi-variate or time series methods, but these methods do not work well for irregularly spaced data or data measured at different times for different subjects. Recent advances make it possible to analyze such data as functions Here we propose to implement an S-Plus module for functional data analysis. This module will e a commercial implementation of the exploratory methods developed by Ramsay and Silverman (1997), with many extensions, including new methods for generalized linear models, survival analysis, and non-linear least square models, and extension to functions with different bases. The new module will seamlessly integrate functional data analysis methods into S-Plus.
As computers become integrated into daily lie, the ability of researchers to collect functional data is becoming more common. There are currently no commercial products available for handling functional data. The proposed methods have significant advantages over existing techniques. A well designed and comprehensive method for implementing these models will find a ready market.
