Cusp catastrophe modeling in medical and health research

Abstract

Further advancement in medical and health research calls for analytical paradigm shifting from linear and continuous approach to nonlinear and discrete approach. In response to this need, we introduced the cusp catastrophe modeling method, including the general principle and two analytical approaches to statistically solving the model for actual data analysis: (1) the polynomial regression method and (2) the likelihood estimation method, with the former for analyzing longitudinal data and the later for cross-sectional data. The polynomial regression method can be conducted using most software packages, including SAS, SPSS, and R. A special R-based package "cusp" is needed to run the likelihood method for data analysis. To assist researchers interested in using the method, two examples with empirical data analyses are included, including R codes for the "cusp" package.