This article discusses some of the types of relationships observed in healthcare research and depicts them in graphic form. The article begins by explaining two basic associations observed in chemistry and physics (Boyles' Law and Charles' Law), and illustrates how these associations are similar to curvilinear and linear associations, respectively, found in healthcare. Graphs of curvilinear associations include morbidity curves and survival and mortality curves. Several examples of linear relationships are given and methods of testing linear relationships with interval and ratio data are introduced (i.e., correlation and ordinary least-squares regression). In addition, 2 × 2 contingency tables for testing the association between categorical (or nominal) data are described. Finally, Sir Austin Bradford Hill's eight criteria for assessing causality from research on associations between variables are presented and explained. Three appendices provide interested readers with opportunities to practice interpreting selected curvilinear and linear relationships.
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| http://dx.doi.org/10.1080/08854726.2016.1194046 | DOI Listing |
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