<?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>1561-3194</journal-id>
<journal-title><![CDATA[Revista de Ciencias Médicas de Pinar del Río]]></journal-title>
<abbrev-journal-title><![CDATA[Rev Ciencias Médicas]]></abbrev-journal-title>
<issn>1561-3194</issn>
<publisher>
<publisher-name><![CDATA[Editorial Ciencias Médicas]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1561-31942020000200287</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Técnicas estadísticas multivariadas para el estudio de la causalidad en Medicina]]></article-title>
<article-title xml:lang="en"><![CDATA[Multivariate statistical techniques for the study of causality in medicine]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Sagaró del Campo]]></surname>
<given-names><![CDATA[Nelsa María]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Zamora Matamoros]]></surname>
<given-names><![CDATA[Larisa]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Ciencias Médicas  ]]></institution>
<addr-line><![CDATA[Santiago de Cuba ]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad de Oriente  ]]></institution>
<addr-line><![CDATA[Santiago de Cuba ]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2020</year>
</pub-date>
<volume>24</volume>
<numero>2</numero>
<fpage>287</fpage>
<lpage>300</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S1561-31942020000200287&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S1561-31942020000200287&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S1561-31942020000200287&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN  Introducción:  las técnicas estadísticas multivariadas son aquellas que analizan múltiples características medidas en un mismo individuo, que por estar interrelacionadas no tiene sentido medir su efecto de manera aislada. Las mismas son más frecuentemente empleadas en los estudios observacionales acerca de la etiología y el pronóstico de una enfermedad, ya que permiten explicar, predecir, controlar variables confusoras y obtener mayor consistencia en la inferencia estadística.  Objetivo:  exponer las técnicas multivariadas que pueden ser empleadas para el estudio de la causalidad en las ciencias biomédicas.  Métodos:  se llevó a cabo una revisión de la literatura sobre el tema en bases de datos bibliográficas como Pubmed/Medline, SciELO, SCOPUS, Web of Science, EBSCOhost, Google, entre otras. Se emplearon en la estrategia de búsqueda como descriptores los consignados en las palabras claves del artículo, los mismos fueron utilizados en idiomas inglés, francés, portugués y español, combinados con los operadores lógicos. De los más de 200 documentos digitales encontrados, se escogieron los de mayor actualidad, elaborando un informe donde se agrupan las técnicas según su objetivo y tipos de variables empleadas en el análisis.  Resultados:  se presentan las técnicas descriptivas, explicativas y mixtas, considerando los diferentes tipos de regresión y las ecuaciones estructurales como las técnicas más empleadas en el estudio de la causalidad.  Conclusiones:  se propone el empleo del análisis estadístico implicativo en la determinación de factores de riesgo y pronósticos.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT  Introduction:  multivariate statistical techniques are those that examine multiple characteristics considered in the same individual, and that because they are interrelated do not make sense to determine their effect in an isolated way. They are more frequently applied in observational studies in relation to the etiology and prognosis of a disease, since they permit to explain, predict and control confusing variables to obtain greater consistency in statistical inference.  Objective:  this paper is aimed at describing the multivariate techniques that can be applied for the study of causality in biomedical sciences.  Methods:  a review of the literature on the subject was carried out, searching in bibliographic databases such as Pubmed/Medline, SciELO, SCOPUS, Web of Science, EBSCOhost, Google, among others. The descriptors used in the search strategy were those provided in the key words of the article, which were used in English, French, Portuguese and Spanish, combined with the logical operators. Out of more than 200 digital documents found, the most current ones were chosen, and a report was prepared where the techniques were grouped according to their objective and types of variables applied in the analysis.  Results:  descriptive, explanatory and mixed techniques were presented, considering the different types of regression and structural equations as the most applied techniques in the study of causality.  Conclusions: The use of implicit statistical analysis in the determination of risk factors and prognosis was proposed.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[ESTADÍSTICAS]]></kwd>
<kwd lng="es"><![CDATA[ANÁLISIS MULTIVARIANTE]]></kwd>
<kwd lng="es"><![CDATA[ANÁLISIS DE REGRESIÓN]]></kwd>
<kwd lng="es"><![CDATA[ANÁLISIS ESTADÍSTICO.]]></kwd>
<kwd lng="en"><![CDATA[STATISTICS]]></kwd>
<kwd lng="en"><![CDATA[MULTIVARIATE ANALYSIS]]></kwd>
<kwd lng="en"><![CDATA[REGRESSION ANALYSIS]]></kwd>
<kwd lng="en"><![CDATA[STATISTICAL ANALYSIS]]></kwd>
</kwd-group>
</article-meta>
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