Generalized Additive Mixed Models for correlated Data

DESCRIPTION (provided by applicant): The ultimate objective of our research is the development of S+ GAMM: a toolkit of generalized additive mixed model for correlated data. This research will make fundamental contributions to the conduct of public health studies by developing robust semi-parametric methodologies and user-friendly software for handling correlated data. Currently, there is no commercial software that does this. The aim of the proposed research is to overcome this deficiency and extend the benefits of using smoothing splines to model the covariate effects when correlation is present. The methodology is an integration of mixed-effects modeling for variance components and non-parametric additive models for the mean functions of covariate effects. We will incorporate diagnostic techniques and graphical methods into the package. The S+ GAMM module will be an object-oriented software module in the S-Plus language. We will also develop a comprehensive case study guidebook using real problems, such as longitudinal data and spatial data. PROPOSED COMMERCIAL APPLICATION: S+GAMM will be a module in the S-Plus software system. This module will be attractive both to the existing S-Plus user base, as well as to a much broader community of biomedical researchers and data analysts. This research will also lead to the development of short courses, books, and other educational materials.