Letter to the Editor
Inclusive use of effect size conversion and Bayes factor in intensive care medicine research
Uso inclusivo de la conversión del tamaño de efecto y del factor Bayes en la investigación de medicina intensiva
C. Ramos-Vera
Área de investigación, Facultad de Ciencias de la Salud, Universidad Cesar Vallejo, Lima, Peru
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Rodriguez-Ruiz, M. Campelo-Izquierdo, A. Estany-Gestal, A.B. Hortas, M.S. Rodríguez-Calvo, A. Rodríguez-Núñez" "autores" => array:6 [ 0 => array:2 [ "nombre" => "E." "apellidos" => "Rodriguez-Ruiz" ] 1 => array:2 [ "nombre" => "M." "apellidos" => "Campelo-Izquierdo" ] 2 => array:2 [ "nombre" => "A." "apellidos" => "Estany-Gestal" ] 3 => array:2 [ "nombre" => "A.B." "apellidos" => "Hortas" ] 4 => array:2 [ "nombre" => "M.S." "apellidos" => "Rodríguez-Calvo" ] 5 => array:2 [ "nombre" => "A." "apellidos" => "Rodríguez-Núñez" ] ] ] ] ] "idiomaDefecto" => "en" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S2173572722000170?idApp=WMIE" "url" => "/21735727/0000004600000003/v1_202202250743/S2173572722000170/v1_202202250743/en/main.assets" ] "en" => array:15 [ "idiomaDefecto" => true "cabecera" => "<span class="elsevierStyleTextfn">Letter to the Editor</span>" "titulo" => "Inclusive use of effect size conversion and Bayes factor in intensive care medicine research" "tieneTextoCompleto" => true "saludo" => "Dear Editor:" "paginas" => array:1 [ 0 => array:2 [ "paginaInicial" => "171" "paginaFinal" => "172" ] ] "autores" => array:1 [ 0 => array:3 [ "autoresLista" => "C. Ramos-Vera" "autores" => array:1 [ 0 => array:3 [ "nombre" => "C." "apellidos" => "Ramos-Vera" "email" => array:1 [ 0 => "cristony_777@hotmail.com" ] ] ] "afiliaciones" => array:1 [ 0 => array:2 [ "entidad" => "Área de investigación, Facultad de Ciencias de la Salud, Universidad Cesar Vallejo, Lima, Peru" "identificador" => "aff0005" ] ] ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Uso inclusivo de la conversión del tamaño de efecto y del factor Bayes en la investigación de medicina intensiva" ] ] "textoCompleto" => "<span class="elsevierStyleSections"><p id="par0005" class="elsevierStylePara elsevierViewall">On 26 September 2020, this journal published an important article that reported on the existence of a series of statistically significant associations (<span class="elsevierStyleItalic">P</span> < .05) between different genetic polymorphisms of the human leukocyte antigen (HLA) system and mortality due to COVID-19 in 72 patients,<a class="elsevierStyleCrossRef" href="#bib0005"><span class="elsevierStyleSup">1</span></a> one of which included the HLA-A*11 gene, based on measurement of the odds ratio (OR = 7.693).</p><p id="par0010" class="elsevierStylePara elsevierViewall">The replication of clinical investigations based on significance tests is advised. This can be done through Bayesian inference, since it allows us to reanalyze the significant finding reported by Lorente et al.,<a class="elsevierStyleCrossRef" href="#bib0005"><span class="elsevierStyleSup">1</span></a> where the Bayes factor (BF) method is referred to as the probability of the data under one hypothesis in relation to the other hypothesis (null hypothesis versus alternative hypothesis).<a class="elsevierStyleCrossRefs" href="#bib0010"><span class="elsevierStyleSup">2,3</span></a> In other words, the BF estimates quantification of the strength of proof on which the data support both hypotheses in contrasting them, beyond the dichotomic interpretation or rejection or acceptance of the null hypothesis.<a class="elsevierStyleCrossRefs" href="#bib0010"><span class="elsevierStyleSup">2,3</span></a> The statistical replication of significant findings with the BF allows us to strengthen the practical credibility of articles in the field of Intensive Care Medicine (experimental research, clinical trials, interventions and treatments). This is required when Bayesian inference reports conclusive or greater level evidence (BF<span class="elsevierStyleInf">10</span> > 10), based on Jeffrey’s classification scale<a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">4</span></a> for BF: weak, moderate, strong, very strong and extreme (<a class="elsevierStyleCrossRef" href="#tbl0005">Table 1</a>).</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia><p id="par0015" class="elsevierStylePara elsevierViewall">The aim of the present letter is to report an example of Bayesian reanalysis to define the level of evidence of statistical hypotheses. Accordingly, consideration was made of the sample size and conversion of OR to correlation effect size (r) through an online calculator,<a class="elsevierStyleCrossRef" href="#bib0025"><span class="elsevierStyleSup">5</span></a> where the conversion value was r = 0.49. This method considers two interpretations: BF<span class="elsevierStyleInf">10</span> (in favor of the alternative hypothesis) and BF<span class="elsevierStyleInf">01</span> (in favor of the null hypothesis), with a credibility interval of 95%. The results obtained for BF are: BF<span class="elsevierStyleInf">10</span> = 1.690 and BF<span class="elsevierStyleInf">01</span> = 0.0006, with 95%CI: 0.284−0.64, which warrants the significant finding reported by Lorente et al.,<a class="elsevierStyleCrossRef" href="#bib0005"><span class="elsevierStyleSup">1</span></a> with extreme evidence in favor of the alternative hypothesis (correlation). This allows us to infer that the rest of statistical associations of greater magnitude between the HLA genes and mortality also exhibit extreme evidence – thus contributing greater credibility to the clinical conclusions drawn in the mentioned study.</p><p id="par0020" class="elsevierStylePara elsevierViewall">In conclusion, the inclusive use of effect size conversion and the BF is a great methodological contribution that has practical implications for medical decision making, based on the confirmation of results that are effectively conclusive and of greater relevance in the context of COVID-19.</p><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0005">Financial support</span><p id="par0025" class="elsevierStylePara elsevierViewall">This study has received no specific funding from the public, commercial or non-profit sectors.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:2 [ 0 => array:2 [ "identificador" => "sec0005" "titulo" => "Financial support" ] 1 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "NotaPie" => array:1 [ 0 => array:2 [ "etiqueta" => "☆" "nota" => "<p class="elsevierStyleNotepara" id="npar0005">Please cite this article as: Ramos-Vera C. Uso inclusivo de la conversión del tamaño de efecto y del factor Bayes en la investigación de medicina intensiva. Med Intensiva. 2022;46:171–172.</p>" ] ] "multimedia" => array:1 [ 0 => array:8 [ "identificador" => "tbl0005" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "at0005" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "<p id="spar0010" class="elsevierStyleSimplePara elsevierViewall">Note: Proprietary table based on Jeffrey’s classification scale.<a class="elsevierStyleCrossRef" href="#bib0020"><span class="elsevierStyleSup">4</span></a></p>" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><tbody title="tbody"><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">>100 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Extreme \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Alternative hypothesis \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">30 + 100 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Very strong \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Alternative hypothesis \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">10 + 30 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Strong \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Alternative hypothesis \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">3.1−10 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Moderate \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Alternative hypothesis \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1.1−3 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Weak \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Alternative hypothesis \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">No evidence \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.3−0.99 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Weak \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Null hypothesis \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.29−0.1 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Moderate \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Null hypothesis \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.09−0.03 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Strong \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Null hypothesis \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">0.03−0.01 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Very strong \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Null hypothesis \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t"><0.01 \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Extreme \t\t\t\t\t\t\n \t\t\t\t</td><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="\n \t\t\t\t\ttop\n \t\t\t\t">Null hypothesis \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab2844649.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spar0005" class="elsevierStyleSimplePara elsevierViewall">Bayes factor quantifiable interpretation values.</p>" ] ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0005" "bibliografiaReferencia" => array:5 [ 0 => array:3 [ "identificador" => "bib0005" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Members of the BIOMEPOC group. HLA genetic polymorphisms and prognosis of patients with COVID-19" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "L. Lorente" 1 => "M.M. Martín" 2 => "A. Franco" 3 => "Y. Barrios" 4 => "J.J. Cáceres" 5 => "J. Solé-Violán" ] ] ] ] ] "host" => array:1 [ 0 => array:1 [ "Revista" => array:5 [ "tituloSerie" => "Med Intensiva." "fecha" => "2021" "volumen" => "45" "paginaInicial" => "96" "paginaFinal" => "103" ] ] ] ] ] ] 1 => array:3 [ "identificador" => "bib0010" "etiqueta" => "2" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Bayesian reanalyses from summary statistics: a guide for academic consumers" "autores" => array:1 [ 0 => array:2 [ "etal" => false "autores" => array:5 [ 0 => "A. Ly" 1 => "A. Raj" 2 => "A. Etz" 3 => "Q.F. Gronau" 4 => "E.-J. 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| Year/Month | Html | Total | |
|---|---|---|---|
| 2024 November | 2 | 4 | 6 |
| 2024 October | 52 | 51 | 103 |
| 2024 September | 62 | 31 | 93 |
| 2024 August | 80 | 35 | 115 |
| 2024 July | 67 | 32 | 99 |
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| 2024 May | 50 | 26 | 76 |
| 2024 April | 47 | 35 | 82 |
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| 2024 February | 31 | 35 | 66 |
| 2024 January | 43 | 43 | 86 |
| 2023 December | 34 | 45 | 79 |
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| 2023 October | 30 | 30 | 60 |
| 2023 September | 27 | 38 | 65 |
| 2023 August | 22 | 11 | 33 |
| 2023 July | 31 | 27 | 58 |
| 2023 June | 25 | 20 | 45 |
| 2023 May | 31 | 29 | 60 |
| 2023 April | 21 | 14 | 35 |
| 2023 March | 31 | 27 | 58 |
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| 2022 December | 52 | 42 | 94 |
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| 2022 October | 60 | 33 | 93 |
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