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</o:shapelayout></xml><![endif]--></head><body lang=DE link="#0563C1" vlink="#954F72" style='word-wrap:break-word'><div class=WordSection1><p class=MsoNormal>Hi all,<o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p><p class=MsoNormal><span lang=EN-US>I have the following scenario: 2x2 table, binary data<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>Multiple studies are comparing the SLN detection in breast cancer patients of two different detection methods (ICG vs RI).<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US>The literature review is providing 13 studies, table is attached. <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>Aim is to calculate the risk difference. Since the detection rate is a binary outcome, the inverse variance method might not be a good choice for calculating the weights. Therefore, I wanted to use the Mantel-Haenszel method. <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>When I am using the metabin function from [meta], I can calculate the pooled effect as follows:<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal>m.sln<- metabin(event.e = ICG_SLN, <o:p></o:p></p><p class=MsoNormal> <span lang=EN-US>n.e = ICG_total,<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US> event.c = RI_SLN,<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US> n.c = RI_total,<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US> studlab = Study,<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US> data = df,<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US> sm = "RD",<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US> method = "MH",<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US> MH.exact = TRUE,<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US> fixed = FALSE,<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US> random = TRUE,<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US> method.tau = "PM",<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US> hakn = TRUE,<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US> title = "SLN detection rate")<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>My problem: I am not able to run this utilizing the rma.mh function in [metafor] for RD. The outcomes are different:<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal style='background:#1C1C1C;word-break:break-all'><span lang=EN-US style='font-size:14.0pt;font-family:"Courier New";color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm;mso-fareast-language:DE'>[META]<br><br><o:p></o:p></span></p><p class=MsoNormal style='background:#1C1C1C;word-break:break-all'><span lang=EN-US style='font-size:14.0pt;font-family:"Courier New";color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm;mso-fareast-language:DE'> RD 95%-CI t p-value<o:p></o:p></span></p><p class=MsoNormal style='background:#1C1C1C;word-break:break-all'><span lang=EN-US style='font-size:14.0pt;font-family:"Courier New";color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm;mso-fareast-language:DE'>Random effects model 0.0112 [-0.0224; 0.0447] 0.73 0.4821<o:p></o:p></span></p><p class=MsoNormal style='background:#1C1C1C;word-break:break-all'><span lang=EN-US style='font-size:14.0pt;font-family:"Courier New";color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm;mso-fareast-language:DE'><o:p> </o:p></span></p><p class=MsoNormal style='background:#1C1C1C;word-break:break-all'><span lang=EN-US style='font-size:14.0pt;font-family:"Courier New";color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm;mso-fareast-language:DE'>Quantifying heterogeneity:<o:p></o:p></span></p><p class=MsoNormal style='background:#1C1C1C;word-break:break-all'><span lang=EN-US style='font-size:14.0pt;font-family:"Courier New";color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm;mso-fareast-language:DE'> tau^2 = 0.0021 [0.0004; 0.0092]; tau = 0.0455 [0.0197; 0.0960]<o:p></o:p></span></p><p class=MsoNormal style='background:#1C1C1C;word-break:break-all'><span lang=EN-US style='font-size:14.0pt;font-family:"Courier New";color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm;mso-fareast-language:DE'> I^2 = 63.2% [33.0%; 79.7%]; H = 1.65 [1.22; 2.22]<o:p></o:p></span></p><p class=MsoNormal style='background:#1C1C1C;word-break:break-all'><span lang=EN-US style='font-size:14.0pt;font-family:"Courier New";color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm;mso-fareast-language:DE'><o:p> </o:p></span></p><p class=MsoNormal style='background:#1C1C1C;word-break:break-all'><span lang=EN-US style='font-size:14.0pt;font-family:"Courier New";color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm;mso-fareast-language:DE'>Test of heterogeneity:<o:p></o:p></span></p><p class=MsoNormal style='background:#1C1C1C;word-break:break-all'><span lang=EN-US style='font-size:14.0pt;font-family:"Courier New";color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm;mso-fareast-language:DE'> Q d.f. p-value<o:p></o:p></span></p><p class=MsoNormal style='background:#1C1C1C;word-break:break-all'><span lang=EN-US style='font-size:14.0pt;font-family:"Courier New";color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm;mso-fareast-language:DE'> 32.57 12 0.0011</span><span lang=EN-US style='font-size:14.0pt;font-family:"Courier New";color:#E6E1DC;mso-fareast-language:DE'><o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>dat <- escalc(measure = "RD", <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US> ai = ICG_pos, ci = RI_pos, <o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US> n1i = ICG_total, n2i = RI_total,<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US> slab = paste(Study, Year, sep = ", "),<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US> </span>)<o:p></o:p></p><p class=MsoNormal>res <- rma(dat, method="REML")<o:p></o:p></p><pre style='background:#1C1C1C;word-break:break-all'><span class=gnd-iwgdh3b><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'>[METAFOR] – rma, random effects<o:p></o:p></span></span></pre><pre style='background:#1C1C1C;word-break:break-all'><span class=gnd-iwgdh3b><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'>Random-Effects Model (k = 13; tau^2 estimator: REML)<o:p></o:p></span></span></pre><pre style='background:#1C1C1C;word-break:break-all'><span class=gnd-iwgdh3b><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'><o:p> </o:p></span></span></pre><pre style='background:#1C1C1C;word-break:break-all'><span class=gnd-iwgdh3b><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'> logLik deviance AIC BIC AICc <o:p></o:p></span></span></pre><pre style='background:#1C1C1C;word-break:break-all'><span class=gnd-iwgdh3b><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'> 17.5702 -35.1404 -31.1404 -30.1705 -29.8070 <o:p></o:p></span></span></pre><pre style='background:#1C1C1C;word-break:break-all'><span class=gnd-iwgdh3b><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'><o:p> </o:p></span></span></pre><pre style='background:#1C1C1C;word-break:break-all'><span class=gnd-iwgdh3b><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'>tau^2 (estimated amount of total heterogeneity): 0.0013 (SE = 0.0008)<o:p></o:p></span></span></pre><pre style='background:#1C1C1C;word-break:break-all'><span class=gnd-iwgdh3b><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'>tau (square root of estimated tau^2 value): 0.0355<o:p></o:p></span></span></pre><pre style='background:#1C1C1C;word-break:break-all'><span class=gnd-iwgdh3b><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'>I^2 (total heterogeneity / total variability): 73.23%<o:p></o:p></span></span></pre><pre style='background:#1C1C1C;word-break:break-all'><span class=gnd-iwgdh3b><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'>H^2 (total variability / sampling variability): 3.74<o:p></o:p></span></span></pre><pre style='background:#1C1C1C;word-break:break-all'><span class=gnd-iwgdh3b><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'><o:p> </o:p></span></span></pre><pre style='background:#1C1C1C;word-break:break-all'><span class=gnd-iwgdh3b><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'>Test for Heterogeneity:<o:p></o:p></span></span></pre><pre style='background:#1C1C1C;word-break:break-all'><span class=gnd-iwgdh3b><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'>Q(df = 12) = 31.9726, p-val = 0.0014<o:p></o:p></span></span></pre><pre style='background:#1C1C1C;word-break:break-all'><span class=gnd-iwgdh3b><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'><o:p> </o:p></span></span></pre><pre style='background:#1C1C1C;word-break:break-all'><span class=gnd-iwgdh3b><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'>Model Results:<o:p></o:p></span></span></pre><pre style='background:#1C1C1C;word-break:break-all'><span class=gnd-iwgdh3b><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'><o:p> </o:p></span></span></pre><pre style='background:#1C1C1C;word-break:break-all'><span class=gnd-iwgdh3b><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'>estimate se zval pval ci.lb ci.ub <o:p></o:p></span></span></pre><pre style='background:#1C1C1C;word-break:break-all'><span class=gnd-iwgdh3b><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'> 0.0111 0.0129 0.8631 0.3881 -0.0141 0.0364 </span></span><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC'><o:p></o:p></span></pre><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>res_1 <- rma.mh(measure = "RD",<o:p></o:p></span></p><p class=MsoNormal><span lang=EN-US> </span>ai = ICG_pos, ci = RI_pos, <o:p></o:p></p><p class=MsoNormal> <span lang=EN-US>n1i = ICG_total, n2i = RI_total, data=dat)<o:p></o:p></span></p><pre style='background:#1C1C1C;word-break:break-all'><span class=gnd-iwgdh3b><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'>[METAFOR] rma.mh<o:p></o:p></span></span></pre><pre style='background:#1C1C1C;word-break:break-all'><span class=gnd-iwgdh3b><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'>Equal-Effects Model (k = 13)<o:p></o:p></span></span></pre><pre style='background:#1C1C1C;word-break:break-all'><span class=gnd-iwgdh3b><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'><o:p> </o:p></span></span></pre><pre style='background:#1C1C1C;word-break:break-all'><span class=gnd-iwgdh3b><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'> logLik deviance AIC BIC AICc <o:p></o:p></span></span></pre><pre style='background:#1C1C1C;word-break:break-all'><span class=gnd-iwgdh3b><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'> 18.3474 32.0734 -34.6948 -34.1299 -34.3312 <o:p></o:p></span></span></pre><pre style='background:#1C1C1C;word-break:break-all'><span class=gnd-iwgdh3b><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'><o:p> </o:p></span></span></pre><pre style='background:#1C1C1C;word-break:break-all'><span class=gnd-iwgdh3b><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'>I^2 (total heterogeneity / total variability): 62.59%<o:p></o:p></span></span></pre><pre style='background:#1C1C1C;word-break:break-all'><span class=gnd-iwgdh3b><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'>H^2 (total variability / sampling variability): 2.67<o:p></o:p></span></span></pre><pre style='background:#1C1C1C;word-break:break-all'><span class=gnd-iwgdh3b><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'><o:p> </o:p></span></span></pre><pre style='background:#1C1C1C;word-break:break-all'><span class=gnd-iwgdh3b><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'>Test for Heterogeneity: <o:p></o:p></span></span></pre><pre style='background:#1C1C1C;word-break:break-all'><span class=gnd-iwgdh3b><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'>Q(df = 12) = 32.0734, p-val = 0.0013<o:p></o:p></span></span></pre><pre style='background:#1C1C1C;word-break:break-all'><span class=gnd-iwgdh3b><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'><o:p> </o:p></span></span></pre><pre style='background:#1C1C1C;word-break:break-all'><span class=gnd-iwgdh3b><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'>Model Results:<o:p></o:p></span></span></pre><pre style='background:#1C1C1C;word-break:break-all'><span class=gnd-iwgdh3b><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'><o:p> </o:p></span></span></pre><pre style='background:#1C1C1C;word-break:break-all'><span class=gnd-iwgdh3b><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'>estimate se zval pval ci.lb ci.ub <o:p></o:p></span></span></pre><pre style='background:#1C1C1C;word-break:break-all'><span class=gnd-iwgdh3b><span lang=EN-US style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'> </span></span><span class=gnd-iwgdh3b><span style='font-size:14.0pt;color:#E6E1DC;border:none windowtext 1.0pt;padding:0cm'>0.0119 0.0058 2.0411 0.0412 0.0005 0.0234</span></span><span style='font-size:14.0pt;color:#E6E1DC'><o:p></o:p></span></pre><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US><o:p> </o:p></span></p><p class=MsoNormal><span lang=EN-US>hanks, Marc<o:p></o:p></span></p></div></body></html>