{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T09:35:41Z","timestamp":1782380141435,"version":"3.54.5"},"reference-count":29,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2020,10,28]],"date-time":"2020-10-28T00:00:00Z","timestamp":1603843200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100014440","name":"Ministerio de Ciencia, Innovaci\u00f3n y Universidades","doi-asserted-by":"publisher","award":["RTI2018\u2010095518\u2010B\u2010C21"],"award-info":[{"award-number":["RTI2018\u2010095518\u2010B\u2010C21"]}],"id":[{"id":"10.13039\/100014440","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Statistics in Medicine"],"published-print":{"date-parts":[[2021,1,30]]},"abstract":"<jats:p>Two\u2010way contingency tables arise in many fields, such as in medical studies, where the relation between two discrete random variables or responses is to be assessed. We propose to analyze and visualize a sample of 2\u2009\u00d7\u20092 tables in the context of single\u2010subject repeated measurements design by means of compositional data (CoDa) methods. First, we propose to visualize the tables in a quaternary diagram. Second, we show how to represent these tables by means of logratios indicating the relationship between the two variables as well as their strength and direction of dependency. Finally, we describe a technique to model those tables with a simplicial regression model. Data from a real\u2010world study of self\u2010prediction of migraine attack onset is used to illustrate this methodology. For each individual, the 2\u2009\u00d7\u20092 table of their migraine expectation vs next day migraine occurrence is computed, generating a sample of tables. Then we visualize and interpret the prediction ability of individuals both in the simplex and in terms of logratios of components. Finally, we model the self\u2010prediction ability with respect to demographic variables, days tracked and disease characteristics. Our application demonstrates that CoDa can be a useful tool for visualizing, modeling, and interpreting the components of 2\u2009\u00d7\u20092 tables.<\/jats:p>","DOI":"10.1002\/sim.8769","type":"journal-article","created":{"date-parts":[[2020,10,28]],"date-time":"2020-10-28T22:49:50Z","timestamp":1603925390000},"page":"213-225","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Modeling and visualizing two\u2010way contingency tables using compositional data analysis: A case\u2010study on individual self\u2010prediction of migraine days"],"prefix":"10.1002","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8304-257X","authenticated-orcid":false,"given":"Marina","family":"Vives\u2010Mestres","sequence":"first","affiliation":[{"name":"Department of Computer Science, Applied Mathematics and Statistics Universitat de Girona Girona Spain"},{"name":"Clinical Statistics Curelator Inc. Cambridge Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2507-0286","authenticated-orcid":false,"given":"Amparo","family":"Casanova","sequence":"additional","affiliation":[{"name":"Clinical Statistics Curelator Inc. Cambridge Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2020,10,28]]},"reference":[{"key":"e_1_2_8_2_1","first-page":"346","article-title":"The mean and variance of \u03c72, when used as a test of homogeneity, when expectations are small","volume":"31","author":"Haldane JBS","year":"1940","journal-title":"Biometrika"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.2397-2335.1922.tb00768.x"},{"key":"e_1_2_8_4_1","volume-title":"Psychological Statistics","author":"McNemar Q","year":"1949"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/37.3-4.256"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11749-019-00670-6"},{"key":"e_1_2_8_7_1","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1002\/9781119976462","volume-title":"Compositional Data Analysis: Theory and Applications","author":"Pawlowsky\u2010Glahn V","year":"2011"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1080\/00224065.2014.11917958"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.adiac.2017.10.003"},{"key":"e_1_2_8_10_1","volume-title":"Handbook of Research Methods for Marketing Management","author":"Ferrer\u2010Rosell B","year":"2020"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12966-018-0685-1"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.annepidem.2016.03.003"},{"issue":"2","key":"e_1_2_8_13_1","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1111\/j.2517-6161.1982.tb01195.x","article-title":"The statistical analysis of compositional data (with discussion)","volume":"44","author":"Aitchison J","year":"1982","journal-title":"J R Stat Soc B Methodol"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/9781119976462.ch3"},{"key":"e_1_2_8_15_1","unstructured":"N1\u2010Headache.Curelator.https:\/\/n1\u2010headache.com. 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