Evidence map›Paper›PMID 27158562›Full record

ArticleOpen journal of statistics2014

Cusp Catastrophe Polynomial Model: Power and Sample Size Estimation.

Ding-Geng Din Chen, Xinguang Jim Chen, Feng Lin, Wan Tang, Y L Lio, Tammy Yuanyuan Guo

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Article in Open journal of statistics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Ding-Geng Din ChenCenter of Research, School of Nursing, University of Rochester Medical Center, Rochester, USA; Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, USA; Institute of Data Sciences, University of Rochester, Rochester, USA.
Xinguang Jim ChenDepartment of Epidemiology, University of Florida, Gainesville, FL, USA; Wuhan University School of Public Health, Wuhan, China.
Feng LinCenter of Research, School of Nursing, University of Rochester Medical Center, Rochester, USA; AD-CARE, Department of Psychiatry, University of Rochester Medical Center, Rochester, NY, USA.
Wan TangDepartment of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, USA.
Y L LioDepartment of Mathematical Sciences, University of South Dakota, Vermillion, SD, USA.
Tammy Yuanyuan GuoDepartment of Statistics, Central South University, Changsha, Hunan, P. R. China.

Funding

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
NICHD NIH HHS R01 HD075635NIDA NIH HHS R01 DA022730
6 · The paper itself

Abstract

Guastello's polynomial regression method for solving cusp catastrophe model has been widely applied to analyze nonlinear behavior outcomes. However, no statistical power analysis for this modeling approach has been reported probably due to the complex nature of the cusp catastrophe model. Since statistical power analysis is essential for research design, we propose a novel method in this paper to fill in the gap. The method is simulation-based and can be used to calculate statistical power and sample size when Guastello's polynomial regression method is used to cusp catastrophe modeling analysis. With this novel approach, a power curve is produced first to depict the relationship between statistical power and samples size under different model specifications. This power curve is then used to determine sample size required for specified statistical power. We verify the method first through four scenarios generated through Monte Carlo simulations, and followed by an application of the method with real published data in modeling early sexual initiation among young adolescents. Findings of our study suggest that this simulation-based power analysis method can be used to estimate sample size and statistical power for Guastello's polynomial regression method in cusp catastrophe modeling.

Indexed as

Cusp catastrophe modelPolynomial regression methodSample size determinationStatistical power analysis

Identifiers

PMID27158562
PMCPMC4855876

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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.