Persistent confusion in nutrition and obesity research about the validity of classic nonparametric tests in the presence of heteroscedasticity: evidence of the problem and valid alternatives.

Autor:	Kroeger CM; Charles Perkins Centre, Central Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.; Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, IN, USA., Ejima K; Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, IN, USA., Hannon BA; Division of Nutritional Sciences, University of Illinois, Urbana-Champaign, Urbana, IL, USA., Halliday TM; Department of Health, Kinesiology, and Recreation, University of Utah, Salt Lake City, UT, USA., McComb B; Division of Biostatistics, New York University School of Medicine, New York, NY, USA., Teran-Garcia M; Division of Nutritional Sciences, University of Illinois, Urbana-Champaign, Urbana, IL, USA., Dawson JA; Department of Nutritional Sciences, Texas Tech University, Lubbock, TX, USA., King DB; Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, IN, USA., Brown AW; Department of Applied Health Science, Indiana University School of Public Health-Bloomington, Bloomington, IN, USA., Allison DB; Department of Epidemiology and Biostatistics, Indiana University School of Public Health-Bloomington, Bloomington, IN, USA.
Jazyk:	angličtina
Zdroj:	The American journal of clinical nutrition [Am J Clin Nutr] 2021 Mar 11; Vol. 113 (3), pp. 517-524.
DOI:	10.1093/ajcn/nqaa357
Abstrakt:	The use of classic nonparametric tests (cNPTs), such as the Kruskal-Wallis and Mann-Whitney U tests, in the presence of unequal variance for between-group comparisons of means and medians may lead to marked increases in the rate of falsely rejecting null hypotheses and decreases in statistical power. Yet, this practice remains prevalent in the scientific literature, including nutrition and obesity literature. Some nutrition and obesity studies use a cNPT in the presence of unequal variance (i.e., heteroscedasticity), sometimes because of the mistaken rationale that the test corrects for heteroscedasticity. Herein, we discuss misconceptions of using cNPTs in the presence of heteroscedasticity. We then discuss assumptions, purposes, and limitations of 3 common tests used to test for mean differences between multiple groups, including 2 parametric tests: Fisher's ANOVA and Welch's ANOVA; and 1 cNPT: the Kruskal-Wallis test. To document the impact of heteroscedasticity on the validity of these tests under conditions similar to those used in nutrition and obesity research, we conducted simple simulations and assessed type I error rates (i.e., false positives, defined as incorrectly rejecting the null hypothesis). We demonstrate that type I error rates for Fisher's ANOVA, which does not account for heteroscedasticity, and Kruskal-Wallis, which tests for differences in distributions rather than means, deviated from the expected significance level. Greater deviation from the expected type I error rate was observed as the heterogeneity increased, especially in the presence of an imbalanced sample size. We provide brief tutorial guidance for authors, editors, and reviewers to identify appropriate statistical tests when test assumptions are violated, with a particular focus on cNPTs. (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Society for Nutrition.)
Databáze:	MEDLINE
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