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Cusp Catastrophe Modeling in Medical and Health Research

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Innovative Statistical Methods for Public Health Data

Part of the book series: ICSA Book Series in Statistics ((ICSABSS))

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.

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Authors and Affiliations

  1. Department of Epidemiology, University of Florida, 2004 Mowry Road, CTRB #4228, Gainesville, FL, 32610, USA

    Xinguang (Jim) Chen

  2. School of Social Work, University of North Carolina, Chapel Hill, NC, 27599, USA

    Ding-Geng Chen

Authors
  1. Xinguang (Jim) Chen
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  2. Ding-Geng Chen
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Corresponding author

Correspondence to Xinguang (Jim) Chen .

Editor information

Editors and Affiliations

  1. Wallace H. Kuralt Distinguished Professor, Director of Statistical Development and Consultation, School of Social Work, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA

    Ding-Geng (Din) Chen

  2. Arizona State University, Tempe, Arizona, USA

    Jeffrey Wilson

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Chen, X., Chen, DG. (2015). Cusp Catastrophe Modeling in Medical and Health Research. In: Chen, DG., Wilson, J. (eds) Innovative Statistical Methods for Public Health Data. ICSA Book Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-18536-1_12

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