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Cusp Catastrophe Model: A Nonlinear Model for Health Outcomes in Nursing Research

Chen, Ding-Geng (Din); Lin, Feng; Chen, Xinguang (Jim); Tang, Wan; Kitzman, Harriet

doi: 10.1097/NNR.0000000000000034
Methods

Background: Although health outcomes may have fundamentally nonlinear relationships with relevant behavioral, psychological, cognitively, or biological predictors, most analytical models assume a linear relationship. Furthermore, some health outcomes may have multimodal distributions, but most statistical models in common use assume a unimodal, normal distribution. Suitable nonlinear models should be developed to explain health outcomes.

Objective: The aim of this study is to provide an overview of a cusp catastrophe model for examining health outcomes and to present an example using grip strength as an indicator of a physical functioning outcome to illustrate how the technique may be used. Results using linear regression, nonlinear logistic model, and the cusp catastrophe model were compared.

Methods: Data from 935 participants from the Survey of Midlife Development in the United States (MIDUS) were analyzed. The outcome was grip strength; executive function and the inflammatory cytokine interleukin-6 were predictor variables.

Results: Grip strength was bimodally distributed. On the basis of fit and model selection criteria, the cusp model was superior to the linear model and the nonlinear logistic regression model. The cusp catastrophe model identified interleukin-6 as a significant asymmetry factor and executive function as a significant bifurcation factor.

Conclusion: The cusp catastrophe model is a useful alternative for explaining the nonlinear relationships commonly seen between health outcome and its predictors. Considerations for the use of cusp catastrophe model in nursing research are discussed and recommended.

Ding-Geng (Din) Chen, PhD, is Professor, School of Nursing and Department of Biostatistics and Computational Biology, University of Rochester Medical Center, New York, and Tianjin International Joint Academy of Biotechnology and Medicine, China.

Feng Lin, PhD, RN, is Assistant Professor, School of Nursing and Department of Psychiatry, School of Medicine and Dentistry, University of Rochester Medical Center, New York.

Xinguang (Jim) Chen, PhD, is Professor, Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville.

Wan Tang, PhD, is Associate Professor, Department of Biostatistics and Computational Biology, University of Rochester Medical Center, New York.

Harriet Kitzman, PhD, RN, FAAN, is Professor, School of Nursing, University of Rochester Medical Center, New York.

Accepted for publication December 26, 2014.

This research was supported in part by three NIH grants from the National Institute on Drug Abuse (NIDA, R01 DA022730, PI: X. Chen), the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD, R01HD075635, PIs: X. Chen and D. Chen), and University of Rochester CTSA Award Number KL2 TR000095 from the National Center for Advancing Translational Sciences (PI: Lin). We thank the two anonymous reviewers for their comments and suggestions, which significantly improved this manuscript.

Ding-Geng Chen and Feng Lin contributed equally to this article.

The authors have no conflicts of interest to report.

Corresponding author: Ding-Geng (Din) Chen, PhD, School of Nursing and Department of Biostatistics and Computational Biology, University of Rochester Medical Center, 601 Elmwood Ave, Box SON, Rochester, NY 14642 (e-mail: DrDG.Chen@gmail.com).

© 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins.