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<p><span style="font-size:11pt"><span style="background:white"><span style="line-height:normal"><span style="font-family:Calibri,sans-serif"><span style="font-size:10.0pt"><span style="font-family:"Arial",sans-serif"><span style="color:black">Dear all,</span></span></span></span></span></span></span></p>
<p><span style="font-size:11pt"><span style="background:white"><span style="line-height:normal"><span style="font-family:Calibri,sans-serif"><span lang="EN-US" style="font-size:10.0pt"><span style="font-family:"Arial",sans-serif"><span style="color:black">I would like to have a help if possible on the pooling of continuous data, specifically Pk/Pd outcomes in the presence of means, medians, and geometric means.</span></span></span></span></span></span></span></p>
<p><span style="font-size:11pt"><span style="background:white"><span style="line-height:normal"><span style="font-family:Calibri,sans-serif"><span lang="EN-US" style="font-size:10.0pt"><span style="font-family:"Arial",sans-serif"><span style="color:black">The goal is to have a (standardized) mean difference between patients with two different conditions.</span></span></span></span></span></span></span></p>
<p><span style="font-size:11pt"><span style="background:white"><span style="line-height:normal"><span style="font-family:Calibri,sans-serif"><span lang="EN-US" style="font-size:10.0pt"><span style="font-family:"Arial",sans-serif"><span style="color:black">Medians under certain assumption can be converted to means and the conv.fivenum functions serves the purpose.</span></span></span></span></span></span></span></p>
<p><span style="font-size:11pt"><span style="background:white"><span style="line-height:normal"><span style="font-family:Calibri,sans-serif"><span lang="EN-US" style="font-size:10.0pt"><span style="font-family:"Arial",sans-serif"><span style="color:black">The problem is the pooling of means with geometric means, since the latter cannot be re-converted to arithmetic means without raw data.</span></span></span></span></span></span></span></p>
<p><span style="font-size:11pt"><span style="background:white"><span style="line-height:normal"><span style="font-family:Calibri,sans-serif"><span lang="EN-US" style="font-size:10.0pt"><span style="font-family:"Arial",sans-serif"><span style="color:black">So, as suggested by the Cochrane Handbook, “a meta-analysis may be then performed on the scale of the log-transformed data”; the Handbook describes how to derive the natural logs from the geometric means and how to compute s<span style="background:white">tandard deviations of the log-transformed data from confidence intervals.</span></span></span></span></span></span></span></span></p>
<p><span style="font-size:11pt"><span style="background:white"><span style="line-height:normal"><span style="font-family:Calibri,sans-serif"><span lang="EN-US" style="font-size:10.0pt"><span style="background:white"><span style="font-family:"Arial",sans-serif"><span style="color:black">Since “</span></span></span></span><span lang="EN-US" style="font-size:10.0pt"><span style="font-family:"Arial",sans-serif"><span style="color:black">log-transformed and untransformed data cannot be mixed in a meta-analysis”, the issue is to go from raw to transformed data for standard means, and the suggestion is to follow the formulas from the paper of Higgins 2008.</span></span></span></span></span></span></span></p>
<p><span style="font-size:11pt"><span style="background:white"><span style="line-height:normal"><span style="font-family:Calibri,sans-serif"><span lang="EN-US" style="font-size:10.0pt"><span style="font-family:"Arial",sans-serif"><span style="color:black">My question is: can I follow the chunks suggested in this page: <a href="https://wviechtb.github.io/metafor/reference/escalc.html" target="_blank"><span style="color:black"><span style="text-decoration:none"><span style="text-underline:none">https://wviechtb.github.io/metafor/reference/escalc.html</span></span></span></a>?</span></span></span></span></span></span></span></p>
<p><span style="font-size:11pt"><span style="background:white"><span style="line-height:normal"><span style="font-family:Calibri,sans-serif"><span lang="EN-US" style="font-size:10.0pt"><span style="font-family:"Arial",sans-serif"><span style="color:black">I mean the section 3a (“Measures for Quantitative Variables”), by resorting to "CVLN" for the log transformed coefficient of variation and to "SDLN" for the log transformed standard deviation in each group, in order to have log-transformed data to be pooled with the natural logs from the geometric means.</span></span></span></span></span></span></span></p>
<p><span style="font-size:11pt"><span style="background:white"><span style="line-height:normal"><span style="font-family:Calibri,sans-serif"><span lang="EN-US" style="font-size:10.0pt"><span style="font-family:"Arial",sans-serif"><span style="color:black">If there is some line of code also for faster handling of geometric means in order to derive natural logs it would be nice as well.</span></span></span></span></span></span></span></p>
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<div>Warm regards,</div>
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<div>Alberto</div>
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<p><span style="font-size: 10.0px;">Alberto Enrico Maraolo, MD, MSc (Antimicrobial Stewardship, Evidence Synthesis), FESCMID<br/>
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Infectious Diseases Specialist, Member of the Steering Committee of SIMIT (ID Italian Society)<br/>
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Cotugno Hospital, AORN dei Colli, Naples, Italy</span></p>
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<p><span style="font-size: 10.0px;">mail: <a href="mailto:albertomaraolo@mail.com" onclick="" target="_blank">albertomaraolo@mail.com</a></span></p>
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<p><span style="font-size: 10.0px;">Alberto Enrico Maraolo, MD, MSc (Antimicrobial Stewardship, Evidence Synthesis), FESCMID<br/>
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Specialista in Malattie Infettive, Consigliere Nazionale Direttivo SIMIT (Società Italiana di Malattie Infettive e Tropicali)<br/>
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Dirigente Medico, AORN dei Colli - Ospedale Cotugno, Napoli</span></p>
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