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Cusp catastrophe model: a nonlinear model for health outcomes in nursing research.
Nurs Res 2014 May-Jun; 63(3):211-20NR

Abstract

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.

Authors+Show Affiliations

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.No affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Comparative Study
Journal Article
Research Support, N.I.H., Extramural

Language

eng

PubMed ID

24785249

Citation

Chen, Ding-Geng Din, et al. "Cusp Catastrophe Model: a Nonlinear Model for Health Outcomes in Nursing Research." Nursing Research, vol. 63, no. 3, 2014, pp. 211-20.
Chen DG, Lin F, Chen XJ, et al. Cusp catastrophe model: a nonlinear model for health outcomes in nursing research. Nurs Res. 2014;63(3):211-20.
Chen, D. G., Lin, F., Chen, X. J., Tang, W., & Kitzman, H. (2014). Cusp catastrophe model: a nonlinear model for health outcomes in nursing research. Nursing Research, 63(3), pp. 211-20. doi:10.1097/NNR.0000000000000034.
Chen DG, et al. Cusp Catastrophe Model: a Nonlinear Model for Health Outcomes in Nursing Research. Nurs Res. 2014;63(3):211-20. PubMed PMID: 24785249.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Cusp catastrophe model: a nonlinear model for health outcomes in nursing research. AU - Chen,Ding-Geng Din, AU - Lin,Feng, AU - Chen,Xinguang Jim, AU - Tang,Wan, AU - Kitzman,Harriet, PY - 2014/5/3/entrez PY - 2014/5/3/pubmed PY - 2014/6/19/medline SP - 211 EP - 20 JF - Nursing research JO - Nurs Res VL - 63 IS - 3 N2 - 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. SN - 1538-9847 UR - https://www.unboundmedicine.com/medline/citation/24785249/Cusp_catastrophe_model:_a_nonlinear_model_for_health_outcomes_in_nursing_research_ L2 - http://dx.doi.org/10.1097/NNR.0000000000000034 DB - PRIME DP - Unbound Medicine ER -