Evidence map›Paper›PMID 24785249›Full record

ArticleNursing research

Cusp catastrophe model: a nonlinear model for health outcomes in nursing research.

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

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Nursing research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Tipping point realized in cod fishery.Scientific reports · 2021
    Article
  4. Article
  5. Article
  6. Cusp Catastrophe Regression and Its Application in Public Health and Behavioral Research.International journal of environmental research and public health · 2017
    Article
  7. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Ding-Geng Din ChenDing-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.
Feng Lin
Xinguang Jim Chen
Wan Tang
Harriet Kitzman

Funding

STATISTICS COREP01AG020166 · NIA · UNIVERSITY OF WISCONSIN-MADISON · PI RYFF, CAROL D. · 2002 to 2015
$50.6M
The University of Rochester Clinical and Translational Science InstituteKL2TR000095 · NCATS · UNIVERSITY OF ROCHESTER · PI BENNETT, NANCY M, KIEBURTZ, KARL D. · 2012 to 2015
$1.7M
Modeling quantum change in adolescent sexual initiation and condom useR01HD075635 · NICHD · WAYNE STATE UNIVERSITY · PI CHEN, XINGUANG · 2013 to 2017
$1.5M
Measuring Cigarette Smoking Behavior Progression with Cross-Sectional DataR01DA022730 · NIDA · WAYNE STATE UNIVERSITY · PI CHEN, XINGUANG · 2008 to 2011
$847k
NCATS NIH HHS KL2 TR000095NIA NIH HHS P01 AG020166NICHD NIH HHS R01 HD075635NICHD NIH HHS R01HD075635NIDA NIH HHS R01 DA022730
6 · The paper itself

Abstract

backgroundAlthough 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.

objectiveThe 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.

methodsData 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.

resultsGrip 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.

conclusionThe 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.

Indexed as

Models, StatisticalModels, TheoreticalNursing ResearchAdultAgedAged, 80 and overBiomarkersExecutive FunctionHand StrengthHumansInterleukin-6Linear ModelsMiddle AgedBiomarkersInterleukin-6

Identifiers

PMID24785249
PMCPMC4066972

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.