<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0864-0300</journal-id>
<journal-title><![CDATA[Revista Cubana de Investigaciones Biomédicas]]></journal-title>
<abbrev-journal-title><![CDATA[Rev Cubana Invest Bioméd]]></abbrev-journal-title>
<issn>0864-0300</issn>
<publisher>
<publisher-name><![CDATA[ECIMED]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0864-03002024000100004</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[El análisis estadístico implicativo y su uso en investigaciones de salud]]></article-title>
<article-title xml:lang="en"><![CDATA[Implicative statistical analysis and its use in health research]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Carballo Reina]]></surname>
<given-names><![CDATA[Onelia]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Barcas Trovajo]]></surname>
<given-names><![CDATA[Beatriz]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Abierta para Adultos  ]]></institution>
<addr-line><![CDATA[ Santiago de los Caballeros]]></addr-line>
<country>República Dominicana</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Hospital Clínico Quirúrgico &#8220;Juan Bruno Zayas&#8221;  ]]></institution>
<addr-line><![CDATA[ Santiago de Cuba]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2024</year>
</pub-date>
<volume>43</volume>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S0864-03002024000100004&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S0864-03002024000100004&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S0864-03002024000100004&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN  Introducción: El análisis estadístico implicativo es un método basado en las técnicas estadísticas multivariadas, la teoría de la cuasi-implicación, la inteligencia artificial y el álgebra booleana. Se utiliza para modelar interrelaciones entre sujetos y variables que permiten la estructuración del conocimiento en forma de normas y reglas generalizadas.  Objetivo: Caracterizar el análisis estadístico implicativo como herramienta del tratamiento de la información estadística en ciencias de la salud.  Métodos: Se realizó una búsqueda de fuentes bibliográficas para caracterizar el método, y el uso en factores pronósticos y perfiles de organización funcional visual en patologías extrapolables a distintos tamaños de muestras.  Desarrollo: El análisis estadístico implicativo organiza la información, favorece el tratamiento estadístico adecuado en el análisis de los datos y permite graficar los resultados. Igualmente, las reglas obtenidas conllevan a hipótesis de causalidad sin restringir el número de variables y el tamaño de la muestra. Su uso ha contribuido a estudios de factores pronósticos en patologías como el cáncer y de perfiles en el procesamiento visual en disléxicos.  Conclusiones:  El análisis estadístico implicativo crea hipótesis de causalidad a través de reglas metodológicas de relación entre las variables de estudio. Además, permite estructurar, analizar y comprender vínculos entre sujetos y variables de la investigación en salud.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT  Introduction:  Implicative statistical analysis is a method based on multivariate statistical techniques, quasi-implication theory, artificial intelligence, and Boolean algebra. It is used to model interrelationships between subjects and variables that allow the structuring of knowledge in the form of generalized norms and rules.  Objective:  To characterize implicative statistical analysis as a tool for the processing of statistical information in health sciences.  Methods:  A search of bibliographic sources was carried out to characterize the method, and its use in prognostic factors and profiles of visual functional organization in pathologies that can be extrapolated to different sample sizes.  Development:  Implicative statistical analysis organizes information, favors the appropriate statistical treatment in the analysis of the data, and allows the results to be graphed. Likewise, the rules obtained lead to hypotheses of causality without restricting the number of variables and the size of the sample. Its use has contributed to studies of prognostic factors in pathologies such as cancer and profiles in visual processing in dyslexics.  Conclusions:  Implicative statistical analysis creates hypotheses of causality through methodological rules of relationship between study variables. In addition, it makes it possible to structure, analyze and understand links between subjects and variables of health research.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[análisis estadístico implicativo]]></kwd>
<kwd lng="es"><![CDATA[teoría de la cuasi implicación]]></kwd>
<kwd lng="es"><![CDATA[CHIC]]></kwd>
<kwd lng="en"><![CDATA[implicative statistical analysis]]></kwd>
<kwd lng="en"><![CDATA[quasi-implication theory]]></kwd>
<kwd lng="en"><![CDATA[CHIC]]></kwd>
</kwd-group>
</article-meta>
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