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    "textoCompleto" => "<span class="elsevierStyleSections"><p id="par0005" class="elsevierStylePara elsevierViewall">I have read with great interest the letter by Modesto and Medina-Villanueva<a class="elsevierStyleCrossRef" href="#bib0030"><span class="elsevierStyleSup">1</span></a> on the proposed use of weights of evidence and decibans measures in intensive care medicine research&#44; and I agree with such methodological reflection where such probability estimates allow better intuition in the confirmation of clinical results&#44; similar to Bayes factor &#40;BF&#41; values&#46;<a class="elsevierStyleCrossRef" href="#bib0035"><span class="elsevierStyleSup">2</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">The BF is ideally suited to strengthen various statistical conclusions from clinical data&#46; For example&#44; it is useful for diagnostic prediction investigations such as the one referred by Modesto and Medina-Villanueva<a class="elsevierStyleCrossRef" href="#bib0030"><span class="elsevierStyleSup">1</span></a> where area under the ROC curve &#40;AUROC&#41; 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for example&#44; if a study with binary ES &#40;OR&#41; does not present adequate heterogeneity&#44; it has the option of using another standardized ES &#40;correlation coefficient or standardized mean difference&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">3</span></a></p><p id="par0020" class="elsevierStylePara elsevierViewall">Another Bayesian model of interest is the Bayesian A&#47;B test that contrasts the difference between two proportions of two different groups considering the assignment of prior distributions and the control of such sample data&#46;<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">4</span></a> Simultaneously it evaluates how likely it is that such events occur according to the logarithmic likelihood ratio scale &#40;log<span class="elsevierStyleHsp" style=""></span>OR<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#44; log<span class="elsevierStyleHsp" style=""></span>OR<span class="elsevierStyleHsp" style=""></span>&#62;<span class="elsevierStyleHsp" style=""></span>0&#41;&#46; This measure is most useful because it fits a normal distribution for simultaneous assessment&#44; which starts with equal odds for both proportions &#40;equal prior distributions&#41;&#44; then proceeds to analyze the pooled data by contrasting the BF towards both events &#40;log<span class="elsevierStyleHsp" style=""></span>OR<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#44; log<span class="elsevierStyleHsp" style=""></span>OR<span class="elsevierStyleHsp" style=""></span>&#62;<span class="elsevierStyleHsp" style=""></span>0&#41;&#46; It is possible to determine the quantitative weight of evidence in a similar way as referred to in the commented article1 by multiplying 10 by the logarithmic scale of the BF to confirm decisive evidence &#40;values greater than 20&#41; of the higher frequency group &#40;log OR &#62; 0&#41;&#44; and even to reporte the negative decisive evidence of the other clinical event &#40;logOR&#60;0&#41;&#46; In addition&#44; it allows establishing the posterior probability &#40;this measure is stable beyond the reporting of more data&#41; given the transformation of the obtained ES&#58; exp &#40;log<span class="elsevierStyleHsp" style=""></span>OR&#41;<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>OR&#44; and OR to probability<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>OR&#47;&#40;OR<span class="elsevierStyleHsp" style=""></span>&#43;<span class="elsevierStyleHsp" style=""></span>1&#41; and their respective intervals to report what are realistic probabilities of participants having such medical outcomes beyond significance values&#46;<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">4</span></a> This is useful for estimating COVID-19 infection rates according to groups and comorbid clinical conditions&#44;<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">4</span></a> such as the study replicated by Ramos-Vera<a class="elsevierStyleCrossRef" href="#bib0035"><span class="elsevierStyleSup">2</span></a> where the comparison of the proportions of cases &#40;COVID-19 infected patients&#41; and controls &#40;healthy adults&#41; reporting measures of human leukocyte antigen &#40;HLA&#41; genetic polymorphisms was evaluated&#44; following the recommendation of the Bayesian Neurology Group-Texas &#40;BNG-TX&#41;4&#46; Bayesian models are also essential in the development of clinical trials in acute care research&#46;<a class="elsevierStyleCrossRef" href="#bib0050"><span class="elsevierStyleSup">5</span></a> Therefore&#44; the joint application with other measures such as weights of evidence and decibans are essential to methodologically strengthen greater practical credibility in intensive care medicine research and by COVID-19&#46;</p></span>"
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Letter to the Editor
Reply to “Decibans: it is time to weight the evidence about diagnostic accuracy”
Respuesta a «Decibanes: es hora de pesar la evidencia sobre exactitud diagnóstica»
C. Ramos-Vera
Área de investigación, Facultad de Ciencias de la Salud, Universidad Cesar Vallejo, Lima, Peru
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    "textoCompleto" => "<span class="elsevierStyleSections"><p id="par0005" class="elsevierStylePara elsevierViewall">I have read with great interest the letter by Modesto and Medina-Villanueva<a class="elsevierStyleCrossRef" href="#bib0030"><span class="elsevierStyleSup">1</span></a> on the proposed use of weights of evidence and decibans measures in intensive care medicine research&#44; and I agree with such methodological reflection where such probability estimates allow better intuition in the confirmation of clinical results&#44; similar to Bayes factor &#40;BF&#41; values&#46;<a class="elsevierStyleCrossRef" href="#bib0035"><span class="elsevierStyleSup">2</span></a></p><p id="par0010" class="elsevierStylePara elsevierViewall">The BF is ideally suited to strengthen various statistical conclusions from clinical data&#46; For example&#44; it is useful for diagnostic prediction investigations such as the one referred by Modesto and Medina-Villanueva<a class="elsevierStyleCrossRef" href="#bib0030"><span class="elsevierStyleSup">1</span></a> where area under the ROC curve &#40;AUROC&#41; findings are reported&#44; these values can be converted to effect size &#40;ES&#59; e&#46;g&#46;&#44; OR&#44; <span class="elsevierStyleItalic">d</span>&#44; <span class="elsevierStyleItalic">r</span>&#41;<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">3</span></a> to estimate the degree of probative strength of this diagnostic test given the data&#46;<a class="elsevierStyleCrossRef" href="#bib0035"><span class="elsevierStyleSup">2</span></a> Likewise&#44; it is possible to consider the AUROC cutoff point &#40;0&#46;5&#41; as an important inclusive value in the Bayesian intervals &#40;BI&#41; of the convertible effect &#40;<span class="elsevierStyleItalic">r</span>&#41; of the AUROC to provide greater certainty in the interpretation of the existence of the 95&#37; probability of finding such a measure of interest among such BIs given the data&#46; This is essential to enhance the credibility and replicability of diagnostic studies using the AUROC&#46;</p><p id="par0015" class="elsevierStylePara elsevierViewall">Convertible ES are essential for the integration of future diagnostic prediction meta-analyses with the alternative of establishing adequate heterogeneity &#40;variability of effects&#41;&#44; for example&#44; if a study with binary ES &#40;OR&#41; does not present adequate heterogeneity&#44; it has the option of using another standardized ES &#40;correlation coefficient or standardized mean difference&#41;&#46;<a class="elsevierStyleCrossRef" href="#bib0040"><span class="elsevierStyleSup">3</span></a></p><p id="par0020" class="elsevierStylePara elsevierViewall">Another Bayesian model of interest is the Bayesian A&#47;B test that contrasts the difference between two proportions of two different groups considering the assignment of prior distributions and the control of such sample data&#46;<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">4</span></a> Simultaneously it evaluates how likely it is that such events occur according to the logarithmic likelihood ratio scale &#40;log<span class="elsevierStyleHsp" style=""></span>OR<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#44; log<span class="elsevierStyleHsp" style=""></span>OR<span class="elsevierStyleHsp" style=""></span>&#62;<span class="elsevierStyleHsp" style=""></span>0&#41;&#46; This measure is most useful because it fits a normal distribution for simultaneous assessment&#44; which starts with equal odds for both proportions &#40;equal prior distributions&#41;&#44; then proceeds to analyze the pooled data by contrasting the BF towards both events &#40;log<span class="elsevierStyleHsp" style=""></span>OR<span class="elsevierStyleHsp" style=""></span>&#60;<span class="elsevierStyleHsp" style=""></span>0&#44; log<span class="elsevierStyleHsp" style=""></span>OR<span class="elsevierStyleHsp" style=""></span>&#62;<span class="elsevierStyleHsp" style=""></span>0&#41;&#46; It is possible to determine the quantitative weight of evidence in a similar way as referred to in the commented article1 by multiplying 10 by the logarithmic scale of the BF to confirm decisive evidence &#40;values greater than 20&#41; of the higher frequency group &#40;log OR &#62; 0&#41;&#44; and even to reporte the negative decisive evidence of the other clinical event &#40;logOR&#60;0&#41;&#46; In addition&#44; it allows establishing the posterior probability &#40;this measure is stable beyond the reporting of more data&#41; given the transformation of the obtained ES&#58; exp &#40;log<span class="elsevierStyleHsp" style=""></span>OR&#41;<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>OR&#44; and OR to probability<span class="elsevierStyleHsp" style=""></span>&#61;<span class="elsevierStyleHsp" style=""></span>OR&#47;&#40;OR<span class="elsevierStyleHsp" style=""></span>&#43;<span class="elsevierStyleHsp" style=""></span>1&#41; and their respective intervals to report what are realistic probabilities of participants having such medical outcomes beyond significance values&#46;<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">4</span></a> This is useful for estimating COVID-19 infection rates according to groups and comorbid clinical conditions&#44;<a class="elsevierStyleCrossRef" href="#bib0045"><span class="elsevierStyleSup">4</span></a> such as the study replicated by Ramos-Vera<a class="elsevierStyleCrossRef" href="#bib0035"><span class="elsevierStyleSup">2</span></a> where the comparison of the proportions of cases &#40;COVID-19 infected patients&#41; and controls &#40;healthy adults&#41; reporting measures of human leukocyte antigen &#40;HLA&#41; genetic polymorphisms was evaluated&#44; following the recommendation of the Bayesian Neurology Group-Texas &#40;BNG-TX&#41;4&#46; Bayesian models are also essential in the development of clinical trials in acute care research&#46;<a class="elsevierStyleCrossRef" href="#bib0050"><span class="elsevierStyleSup">5</span></a> Therefore&#44; the joint application with other measures such as weights of evidence and decibans are essential to methodologically strengthen greater practical credibility in intensive care medicine research and by COVID-19&#46;</p></span>"
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