| Summary: | <p style="margin-bottom: 0cm; line-height: 100%;" align="justify"><span style="font-family: Courier New, monospace;"><span style="font-size: small;"><span style="font-family: Verdana, sans-serif;"><span style="font-size: small;"><span lang="en-US"><strong>Background</strong></span></span></span><span style="font-family: Verdana, sans-serif;"><span style="font-size: small;"><span lang="en-US">: </span></span></span><span style="font-family: Verdana, sans-serif;"><span style="font-size: small;"><span lang="en-US">implicative statistical analysis has been used successfully in the diagnosis and solution of problems, typical of the teaching of mathematics, which was its initial aim. In the national environment it has also been used to identify prognostic and risk factors in medicine.</span></span></span></span></span></p><p style="margin-bottom: 0cm; line-height: 100%;" align="justify"><span style="font-family: Courier New, monospace;"><span style="font-size: small;"><span style="font-family: Verdana, sans-serif;"><span style="font-size: small;"><span lang="en-US"><strong>Objective</strong></span></span></span><span style="font-family: Verdana, sans-serif;"><span style="font-size: small;"><span lang="en-US">: </span></span></span><span style="font-family: Verdana, sans-serif;"><span style="font-size: small;"><span lang="en-US">to describe new procedures for the optimum application of implicative statistical analysis in the setting of medical causality studies.</span></span></span></span></span></p><p style="margin-bottom: 0cm; line-height: 100%;" align="justify"><span style="font-family: Courier New, monospace;"><span style="font-size: small;"><span style="font-family: Verdana, sans-serif;"><span style="font-size: small;"><span lang="en-US"><strong>Methods</strong></span></span></span><span style="font-family: Verdana, sans-serif;"><span style="font-size: small;"><span lang="en-US">: </span></span></span><span style="font-family: Verdana, sans-serif;"><span style="font-size: small;"><span lang="en-US">a bibliographic review was carried out using specialized services available on the Internet and a critical analysis of the studies carried out at the University of Medical Sciences of Santiago de Cuba, with the application of this technique for the identification of prognostic and risk factors, which helped to define the correct way to apply it.</span></span></span></span></span></p><p style="margin-bottom: 0cm; line-height: 100%;" align="justify"><span style="font-family: Courier New, monospace;"><span style="font-size: small;"><span style="font-family: Verdana, sans-serif;"><span style="font-size: small;"><span lang="en-US"><strong>Results</strong></span></span></span><span style="font-family: Verdana, sans-serif;"><span style="font-size: small;"><span lang="en-US">: </span></span></span><span style="font-family: Verdana, sans-serif;"><span style="font-size: small;"><span lang="en-US">among the most important particularities for the application of this analysis are: within the transformations the duplication of the dependent variable, and within the main analysis the use of the new dependent variables in the implicative graph cone, as the way to recognize the alleged causal factors, either prognostic or risk factors.</span></span></span></span></span></p><p style="margin-bottom: 0.14cm; line-height: 100%;" align="justify"><span style="font-family: Courier New, monospace;"><span style="font-size: small;"><span style="font-family: Verdana, sans-serif;"><span style="font-size: small;"><span lang="en-US"><strong>Conclusions</strong></span></span></span><span style="font-family: Verdana, sans-serif;"><span style="font-size: small;"><span lang="en-US">: </span></span></span><span style="font-family: Verdana, sans-serif;"><span style="font-size: small;"><span lang="en-US">the presented procedures are a first approach to the design of a methodology proposed by the authors for the efficient use of the implicative statistical analysis, which together with an appropriate interpretation of the results must be an important mainstay to complement the multivariate techniques commonly used in the clinical-epidemiological studies of causality.</span></span></span></span></span></p>
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