{"id":568,"date":"2018-06-23T19:02:04","date_gmt":"2018-06-23T10:02:04","guid":{"rendered":"https:\/\/best-biostatistics.com\/toukei-er\/entry\/meta-analysis-sensitivity-specificity-diagnosis\/"},"modified":"2025-02-20T23:48:29","modified_gmt":"2025-02-20T14:48:29","slug":"meta-analysis-sensitivity-specificity-diagnosis","status":"publish","type":"post","link":"https:\/\/best-biostatistics.com\/toukei-er\/entry\/meta-analysis-sensitivity-specificity-diagnosis\/","title":{"rendered":"R \u3067\u8a3a\u65ad\u691c\u67fb\u306e\u611f\u5ea6\u30fb\u7279\u7570\u5ea6\u3092\u7d71\u5408\u3059\u308b\u30e1\u30bf\u30a2\u30ca\u30ea\u30b7\u30b9"},"content":{"rendered":"\n

\u8a3a\u65ad\u691c\u67fb\u3092\u7d71\u5408\u3059\u308b\u65b9\u6cd5<\/p>\n\n\n\n

\u611f\u5ea6\u30fb\u7279\u7570\u5ea6\u306e\u30e1\u30bf\u30a2\u30ca\u30ea\u30b7\u30b9<\/p>\n\n\n\n\n\n\n\n

\u611f\u5ea6\u30fb\u7279\u7570\u5ea6\u306e\u30e1\u30bf\u30a2\u30ca\u30ea\u30b7\u30b9\u3068\u306f<\/h2>\n\n\n\n

\u8a3a\u65ad\u691c\u67fb\u306e\u6027\u80fd\u3092\u898b\u308b\u306e\u306b\u3001\u611f\u5ea6\u30fb\u7279\u7570\u5ea6\u306f\u6b20\u304b\u305b\u306a\u3044\u3002<\/p>\n\n\n\n

\u75c5\u6c17\u3042\u308a\u3092\u304d\u3061\u3093\u3068\u8a3a\u65ad\u3067\u304d\u308b\u5272\u5408\u304c\u611f\u5ea6\u3002<\/p>\n\n\n\n

\u75c5\u6c17\u306a\u3057\u3092\u304d\u3061\u3093\u3068\u75c5\u6c17\u306a\u3057\u3068\u9664\u5916\u3067\u304d\u308b\u5272\u5408\u304c\u7279\u7570\u5ea6\u3002<\/p>\n\n\n\n

\u5fc5\u8981\u306a\u6570\u5024\u306f\u3001\u691c\u67fb\u967d\u6027\u30fb\u9670\u6027\u3068\u3001\u75c5\u6c17\u3042\u308a\u30fb\u306a\u3057\u3092\u30af\u30ed\u30b9\u3055\u305b\u305f\u3001\uff12\uff58\uff12\u8868\u306e\u5404\u30bb\u30eb\u306e\u4eba\u6570\u3060\u3002<\/p>\n\n\n\n

\u3044\u308d\u3044\u308d\u306a\u7814\u7a76\u7d50\u679c\u3092\u7d71\u5408\u3059\u308b\u306e\u304c\u30e1\u30bf\u30a2\u30ca\u30ea\u30b7\u30b9\u3002<\/p>\n\n\n\n

\u611f\u5ea6\u30fb\u7279\u7570\u5ea6\u306e\u7814\u7a76\u7d50\u679c\u306e\u30e1\u30bf\u30a2\u30ca\u30ea\u30b7\u30b9\u306f\u3069\u3046\u3084\u308b\u304b\uff1f<\/p>\n\n\n\n

\u7d71\u5408ROC\u66f2\u7dda\u3092\u63cf\u304d\u305f\u3044\u5834\u5408\u306f\u3069\u3046\u3059\u308c\u3070\u3044\u3044\u304b\uff1f<\/p>\n\n\n\n

\u8907\u6570\u306e\u7814\u7a76\u7d50\u679c\u3092\u30d7\u30ed\u30c3\u30c8\u3057\u3066\u53ef\u8996\u5316\u3059\u308b<\/h2>\n\n\n\n

2\u00d72\u306e\u5206\u5272\u8868\u306e4\u3064\u306e\u30de\u30b9\u76ee\u3092a, b, c, d\u3068\u8868\u73fe\u3059\u308b \u3053\u3068\u304c\u591a\u3044\u3002<\/p>\n\n\n\n

<\/th>\u75c5\u6c17\u3042\u308a<\/th>\u75c5\u6c17\u306a\u3057<\/th><\/tr><\/thead>
\u691c\u67fb\u967d\u6027<\/td>a<\/td>b<\/td><\/tr>
\u691c\u67fb\u9670\u6027<\/td>c<\/td>d<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

\u611f\u5ea6\u30fb\u7279\u7570\u5ea6\u306e\u30e1\u30bf\u30a2\u30ca\u30ea\u30b7\u30b9\u3092\u884c\u3063\u3066\u307f\u308b\u30b5\u30f3\u30d7\u30eb\u30c7\u30fc\u30bf\u306f\u3001\u4ee5\u4e0b\u306e\u901a\u308a\u3002<\/p>\n\n\n\n

a <-<\/span> c<\/span>(<\/span>26<\/span>,<\/span>11<\/span>,<\/span>68<\/span>,<\/span>74<\/span>+<\/span>0.5<\/span>,<\/span>84<\/span>,<\/span>40<\/span>,<\/span>16<\/span>,<\/span>96<\/span>,<\/span>11<\/span>,<\/span>91<\/span>,<\/span>46<\/span>,<\/span>15<\/span>,<\/span>58<\/span>,<\/span>26<\/span>)<\/span>\nb <-<\/span> c<\/span>(<\/span>2<\/span>,<\/span>2<\/span>,<\/span>8<\/span>,<\/span>0<\/span>+<\/span>0.5<\/span>,<\/span>13<\/span>,<\/span>7<\/span>,<\/span>9<\/span>,<\/span>15<\/span>,<\/span>2<\/span>,<\/span>5<\/span>,<\/span>3<\/span>,<\/span>2<\/span>,<\/span>16<\/span>,<\/span>1<\/span>)<\/span>\nc <-<\/span> c<\/span>(<\/span>4<\/span>,<\/span>1<\/span>,<\/span>3<\/span>,<\/span>12<\/span>+<\/span>0.5<\/span>,<\/span>20<\/span>,<\/span>3<\/span>,<\/span>1<\/span>,<\/span>20<\/span>,<\/span>2<\/span>,<\/span>5<\/span>,<\/span>9<\/span>,<\/span>1<\/span>,<\/span>10<\/span>,<\/span>4<\/span>)<\/span>\nd <-<\/span> c<\/span>(<\/span>83<\/span>,<\/span>5<\/span>,<\/span>34<\/span>,<\/span>111<\/span>+<\/span>0.5<\/span>,<\/span>99<\/span>,<\/span>41<\/span>,<\/span>109<\/span>,<\/span>206<\/span>,<\/span>57<\/span>,<\/span>57<\/span>,<\/span>42<\/span>,<\/span>93<\/span>,<\/span>121<\/span>,<\/span>74<\/span>)<\/span>\n<\/code><\/pre>\n\n\n\n
\u611f\u5ea6\u306f\u3001\u75c5\u6c17\u3042\u308a\u306e\u4eba\u306e\u3046\u3061\u3001\u691c\u67fb\u304c\u5f53\u305f\u3063\u305f\u4eba\u3002<\/p>\n\n\n\n
\u3053\u308c\u304c\u3001\u691c\u67fb\u967d\u6027\u306e\u5272\u5408\u3002<\/p>\n\n\n\n
\u75c5\u6c17\u3042\u308a\u306e\u4eba\u304c\u672c\u5f53\u306b\u75c5\u6c17\u3042\u308a\u3068\u5224\u65ad\u3055\u308c\u308b\u5272\u5408\u304c\u611f\u5ea6\uff08Sensitivity, Se\uff09\u3002<\/p>\n\n\n\n
\u7279\u7570\u5ea6\u306f\u3001\u75c5\u6c17\u306a\u3057\u306e\u4eba\u306e\u3046\u3061\u3001\u691c\u67fb\u304c\u9670\u6027\u306e\u4eba\u3002<\/p>\n\n\n\n
\u3053\u308c\u304c\u3001\u691c\u67fb\u9670\u6027\u306e\u5272\u5408\u3002<\/p>\n\n\n\n
\u75c5\u6c17\u306a\u3057\u306e\u4eba\u304c\u672c\u5f53\u306b\u75c5\u6c17\u306a\u3057\u3068\u5224\u65ad\u3055\u308c\u308b\u5272\u5408\u304c\u7279\u7570\u5ea6\uff08Specificity, Sp\uff09\u3002<\/p>\n\n\n\n
\u3053\u308c\u3089\u3092\u8a08\u7b97\u3059\u308b\u3068\u4ee5\u4e0b\u306e\u3068\u304a\u308a\u3067\u3042\u308b\u3002<\/p>\n\n\n\n
Se <-<\/span> a\/<\/span>(<\/span>a+<\/span>c)<\/span>\nSp <-<\/span> d\/<\/span>(<\/span>b+<\/span>d)<\/span>\nplot<\/span>(<\/span>1<\/span>-<\/span>Sp,<\/span> Se,<\/span> xlim=<\/span>c<\/span>(<\/span>0<\/span>,<\/span>1<\/span>),<\/span> ylim=<\/span>c<\/span>(<\/span>0<\/span>,<\/span>1<\/span>))<\/span>\n<\/code><\/pre>\n\n\n\n
\u56f3\u306b\u3059\u308b\u3068\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u306a\u308b\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
<\/span><\/p>\n\n\n\n
\u5de6\u4e0a\u306b\u304b\u305f\u307e\u3063\u3066\u3044\u308b\u306e\u304c\u3001\u5404\u7814\u7a76\u3092\u8868\u3057\u3066\u3044\u308b\u70b9\u3067\u3042\u308b\u3002<\/p>\n\n\n\n
\uff1e\uff1e\u3082\u3046\u7d71\u8a08\u3067\u60a9\u3080\u306e\u306f\u7d42\u308f\u308a\u306b\u3057\u307e\u305b\u3093\u304b\uff1f\u00a0<\/a><\/strong><\/span><\/p>\r\n\"\"<\/a>\r\n
\u21911\u4e07\u4eba\u4ee5\u4e0a\u306e\u533b\u7642\u5f93\u4e8b\u8005\u304c\u8cfc\u8aad\u4e2d<\/span><\/strong><\/span><\/p><\/div>
\u611f\u5ea6\u3068\u507d\u967d\u6027\u304b\u3089\u8a08\u7b97\u3055\u308c\u308b\u30aa\u30c3\u30ba\u6bd4\u3092\u91cd\u307f\u3065\u3051\u7dda\u5f62\u56de\u5e30\u3067\u30c1\u30a7\u30c3\u30af\u3059\u308b<\/h2>\n\n\n\n
\u611f\u5ea6 Se \u3068\u507d\u967d\u6027 1-Sp \u306e\u30aa\u30c3\u30ba\u6bd4\uff08OR\uff09\u3092\u8a08\u7b97\u3059\u308b\u3002<\/p>\n\n\n\n
\u3053\u308c\u304c\u8a3a\u65ad\u306e\u30d1\u30ef\u30fc\u306e\u6307\u6a19\u3060\u3002<\/p>\n\n\n\n
\u611f\u5ea6 Se \u3068\u7279\u7570\u5ea6 Sp \u306e\u30aa\u30c3\u30ba\u6bd4\uff08S\uff09\u3082\u8a08\u7b97\u3059\u308b\u3002<\/p>\n\n\n\n
\u3053\u308c\u306f\u30ab\u30c3\u30c8\u30aa\u30d5\u5024\u306e\u5f71\u97ff\u3092\u8868\u3059\u3002<\/p>\n\n\n\n
\u305d\u308c\u305e\u308c\u306e\u8a66\u9a13\u7d50\u679c\u306e\u6a19\u6e96\u8aa4\u5dee se \u3092\u8a08\u7b97\u3059\u308b\u3002<\/p>\n\n\n\n
\u6a19\u6e96\u8aa4\u5dee\u304b\u3089\u305d\u308c\u305e\u308c\u306e\u8a66\u9a13\u306e\u91cd\u307f w \u3092\u8a08\u7b97\u3059\u308b\u3002<\/p>\n\n\n\n
OR \u3068 S \u306e\u5bfe\u6570\u3092\u4f7f\u3063\u3066\u3001\u91cd\u307f\u4ed8\u3051\u7dda\u5f62\u56de\u5e30\u5206\u6790\u3092\u884c\u3046\u3002<\/p>\n\n\n\n
x <-<\/span> 1<\/span>-<\/span>Sp\ny <-<\/span> Se\nOR <-<\/span> (<\/span>y\/<\/span>(<\/span>1<\/span>-<\/span>y))<\/span>\/<\/span>(<\/span>x\/<\/span>(<\/span>1<\/span>-<\/span>x))<\/span>\nS <-<\/span> (<\/span>y\/<\/span>(<\/span>1<\/span>-<\/span>y))<\/span>\/<\/span>((<\/span>1<\/span>-<\/span>x)<\/span>\/<\/span>x)<\/span>\nse <-<\/span> sqrt<\/span>(<\/span>1<\/span>\/<\/span>a+<\/span>1<\/span>\/<\/span>b+<\/span>1<\/span>\/<\/span>c+<\/span>1<\/span>\/<\/span>d)<\/span>\nw <-<\/span> 1<\/span>\/<\/span>se\/<\/span>se\nlm.res <-<\/span> lm<\/span>(<\/span>log<\/span>(<\/span>OR)<\/span> ~<\/span> log<\/span>(<\/span>S),<\/span> weights=<\/span>w)<\/span>\nsummary<\/span>(<\/span>lm.res)<\/span>\n<\/code><\/pre>\n\n\n\n
\u91cd\u307f\u4ed8\u3051\u7dda\u5f62\u56de\u5e30\u5206\u6790\u306e\u7d50\u679c\u306f\u3053\u3061\u3089\u3002<\/p>\n\n\n\n
> summary(lm.res)\n\nCall:\nlm(formula = log(OR) ~ log(S), weights = w)\n\nWeighted Residuals:\n    Min      1Q  Median      3Q     Max \n-2.2693 -0.3663  0.7553  1.1976  1.8928 \n\nCoefficients:\n            Estimate Std. Error t value Pr(>|t|)    \n(Intercept)   4.2038     0.2566  16.383 1.41e-09 ***\nlog(S)       -0.2306     0.2336  -0.987    0.343    \n---\nSignif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1\n\nResidual standard error: 1.322 on 12 degrees of freedom\nMultiple R-squared:  0.07509,\tAdjusted R-squared:  -0.001985 \nF-statistic: 0.9742 on 1 and 12 DF,  p-value: 0.3431\n<\/code><\/pre>\n\n\n\n
\u7d50\u679c\u306f log(S) \u306e\u4fc2\u6570\u306f\u7d71\u8a08\u5b66\u7684\u306b\u6709\u610f\u3067\u306a\u304f\u3001OR \u306f S \u306e\u95a2\u6570\u3068\u306f\u8a00\u3048\u306a\u3044\u3002<\/p>\n\n\n\n
\u3064\u307e\u308a\u3001\u30ab\u30c3\u30c8\u30aa\u30d5\u5024\u306e\u9055\u3044\u306b\u5f71\u97ff\u306f\u306a\u304f\u3001\u8a3a\u65ad\u30aa\u30c3\u30ba\u6bd4\u306e\u7d71\u5408\u304c\u53ef\u80fd\u3068\u5224\u65ad\u3067\u304d\u308b\u3002<\/p>\n\n\n\n
\u30d7\u30ed\u30c3\u30c8\u306b\u56de\u5e30\u76f4\u7dda\u3092\u4e57\u305b\u3066\u30c1\u30a7\u30c3\u30af\u3057\u305f\u56f3\u304c\u3053\u3061\u3089\u3002<\/p>\n\n\n\n
plot<\/span>(<\/span>log<\/span>(<\/span>S),<\/span>log<\/span>(<\/span>OR),<\/span>pch=<\/span>\"\"<\/span>,<\/span>ylim=<\/span>c<\/span>(<\/span>2<\/span>,<\/span>8<\/span>))<\/span>\nsymbols<\/span>(<\/span>log<\/span>(<\/span>S),<\/span>log<\/span>(<\/span>OR),<\/span>squares=<\/span>w,<\/span>add=<\/span>TRUE<\/span>)<\/span>\nabline<\/span>(<\/span>a=<\/span>lm.res$<\/span>coeff[<\/span>1<\/span>],<\/span>b=<\/span>lm.res$<\/span>coeff[<\/span>2<\/span>])<\/span>\n<\/code><\/pre>\n\n\n\n
\"\"<\/figure>\n\n\n\n
<\/span><\/p>\n\n\n\n
\u591a\u5c11\u306f\u50be\u3044\u3066\u3044\u308b\u304c\u3001\u50be\u304d\u304c 0 \u3067\u306a\u3044\u3068\u306f\u8a00\u3048\u306a\u3044\u3068\u3044\u3046\u7d50\u679c\u3067\u3042\u3063\u305f\u3002<\/p>\n\n\n\n
\u8981\u7d04ROC\u66f2\u7dda\u3092\u66f8\u304f<\/h2>\n\n\n\n
\u3055\u307e\u3056\u307e\u306a\u611f\u5ea6\u30fb\u7279\u7570\u5ea6\u5206\u6790\u7814\u7a76\u306e\u7d50\u679c\u3092\u8981\u7d04\u3057\u305f\u8981\u7d04ROC\u66f2\u7dda\u3092\u63cf\u304f\u306b\u306f\u3069\u3046\u3057\u305f\u3089\u3044\u3044\u304b\uff1f<\/p>\n\n\n\n
\u7dda\u5f62\u56de\u5e30\u5206\u6790\u306e\u7d50\u679c\u3092\u4f7f\u3063\u3066\u8981\u7d04ROC\u66f2\u7dda\u3092\u63cf\u304f\u3002<\/p>\n\n\n\n
alpha <-<\/span> lm.res$<\/span>coeff[<\/span>1<\/span>]<\/span>\nbeta <-<\/span> lm.res$<\/span>coeff[<\/span>2<\/span>]<\/span>\nSROCC <-<\/span> function<\/span>(<\/span>x,<\/span>alpha=<\/span>lm.res$<\/span>coeff[<\/span>1<\/span>],<\/span>beta=<\/span>lm.res$<\/span>coeff[<\/span>2<\/span>]){<\/span>1<\/span>\/<\/span>(<\/span>1<\/span>+<\/span>exp<\/span>(<\/span>-<\/span>1<\/span>*<\/span>alpha\/<\/span>(<\/span>1<\/span>-<\/span>beta))<\/span>*<\/span>(<\/span>x\/<\/span>(<\/span>1<\/span>-<\/span>x))<\/span>^<\/span>(<\/span>-<\/span>1<\/span>*<\/span>(<\/span>1<\/span>+<\/span>beta)<\/span>\/<\/span>(<\/span>1<\/span>-<\/span>beta)))}<\/span>\nplot<\/span>(<\/span>1<\/span>-<\/span>Sp,<\/span> Se,<\/span> xlim=<\/span>c<\/span>(<\/span>0<\/span>,<\/span>1<\/span>),<\/span> ylim=<\/span>c<\/span>(<\/span>0<\/span>,<\/span>1<\/span>))<\/span>\ncurve<\/span>(<\/span>SROCC,<\/span> from=<\/span>0<\/span>,<\/span> to=<\/span>1<\/span>,<\/span> add=<\/span>TRUE<\/span>)<\/span>\n<\/code><\/pre>\n\n\n\n
\u8981\u7d04 ROC \u66f2\u7dda\u3068\u5404\u7814\u7a76\u306e\u30d7\u30ed\u30c3\u30c8\u3092\u91cd\u306d\u305f\u56f3\u304c\u3053\u3061\u3089\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
<\/span><\/p>\n\n\n\n
\u8a3a\u65ad\u691c\u67fb\u306e\u7d71\u5408\u30aa\u30c3\u30ba\u6bd4\u3092\u8a08\u7b97\u3059\u308b<\/h2>\n\n\n\n
\u7d71\u5408\u30aa\u30c3\u30ba\u6bd4\u3092\u8a08\u7b97\u3059\u308b\u306b\u306f\u3001metafor\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u4f7f\u3046\u3002<\/p>\n\n\n\n
metafor\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u6700\u521d\u306e\u4e00\u56de\u3060\u3051\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb\u3059\u308b\u3002<\/p>\n\n\n\n
install.packages<\/span>(<\/span>\"metafor\"<\/span>)<\/span>\n<\/code><\/pre>\n\n\n\n
metafor\u30d1\u30c3\u30b1\u30fc\u30b8\u3092\u547c\u3073\u51fa\u3057\u3001\u7d71\u5408\u30aa\u30c3\u30ba\u6bd4\u3092\u8a08\u7b97\u3059\u308b\u3002<\/p>\n\n\n\n
\u56fa\u5b9a\u52b9\u679c\u30e2\u30c7\u30eb\u3001Mantel-Haenszel\u306e\u65b9\u6cd5\u3001\u5909\u91cf\u52b9\u679c\u30e2\u30c7\u30eb\uff08DerSimonian-Laird\u306e\u65b9\u6cd5\uff09\u3067\u8a08\u7b97\u3057\u3066\u307f\u308b\u3002<\/p>\n\n\n\n
library<\/span>(<\/span>metafor)<\/span>\nescalc1 <-<\/span> escalc<\/span>(<\/span>measure=<\/span>\"OR\"<\/span>,<\/span> ai=<\/span>a,<\/span> bi=<\/span>b,<\/span> ci=<\/span>c,<\/span> di=<\/span>d)<\/span>\nrma.uni<\/span>(<\/span>yi,<\/span> vi,<\/span> method=<\/span>\"FE\"<\/span>,<\/span> dat=<\/span>escalc1)<\/span>\nrma.mh <\/span>(<\/span>ai=<\/span>a,<\/span> bi=<\/span>b,<\/span> ci=<\/span>c,<\/span> di=<\/span>d)<\/span>\nrma.uni<\/span>(<\/span>yi,<\/span> vi,<\/span> method=<\/span>\"DL\"<\/span>,<\/span> dat=<\/span>escalc1)<\/span>\n<\/code><\/pre>\n\n\n\n
\u307e\u305a\u3001\u56fa\u5b9a\u52b9\u679c\u30e2\u30c7\u30eb\u306e\u7d50\u679c\u3002<\/p>\n\n\n\n
> rma.uni(yi, vi, method=\"FE\", dat=escalc1)\n\nFixed-Effects Model (k = 14)\n\nI^2 (total heterogeneity \/ total variability):   42.68%\nH^2 (total variability \/ sampling variability):  1.74\n\nTest for Heterogeneity:\nQ(df = 13) = 22.6789, p-val = 0.0457\n\nModel Results:\n\nestimate      se     zval    pval   ci.lb   ci.ub      \n  4.3179  0.1733  24.9220  <.0001  3.9784  4.6575  *** \n\n---\nSignif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1<\/code><\/pre>\n\n\n\n
Heterogeneity\u304c\u7d71\u8a08\u5b66\u7684\u306b\u6709\u610f\uff08 p-val = 0.0457 \uff09\u306a\u306e\u3067\u3001\u5909\u91cf\u52b9\u679c\u30e2\u30c7\u30eb\u306e\u307b\u3046\u304c\u9069\u5207\u3002<\/p>\n\n\n\n
\u6b21\u306b\u3001Mantel-Haenszel\u306e\u65b9\u6cd5\u306e\u7d50\u679c\u3002<\/p>\n\n\n\n
> rma.mh (ai=a, bi=b, ci=c, di=d)\n\nEqual-Effects Model (k = 14)\n\nI^2 (total heterogeneity \/ total variability):  43.30%\nH^2 (total variability \/ sampling variability): 1.76\n\nTest for Heterogeneity: \nQ(df = 13) = 22.9291, p-val = 0.0425\n\nModel Results (log scale):\n\nestimate      se     zval    pval   ci.lb   ci.ub \n  4.4046  0.1687  26.1014  <.0001  4.0738  4.7353 \n\nModel Results (OR scale):\n\nestimate    ci.lb     ci.ub \n 81.8255  58.7827  113.9012 \n\nCochran-Mantel-Haenszel Test:    CMH = 1180.3678, df = 1,  p-val < 0.0001\nTarone's Test for Heterogeneity: X^2 =   25.6687, df = 13, p-val = 0.0188<\/code><\/pre>\n\n\n\n
Heterogeneity\u306f\u3053\u3061\u3089\u3082\u540c\u69d8\u306b\u6709\u610f\uff08 p-val = 0.0425 \uff09<\/p>\n\n\n\n
\u6700\u5f8c\u306b\u3001DerSimonian-Laird\u306e\u65b9\u6cd5\u306e\u7d50\u679c\u3002<\/p>\n\n\n\n
\u56fa\u5b9a\u52b9\u679c\u30e2\u30c7\u30eb\u304a\u3088\u3073Mantel-Haenszel\u306e\u65b9\u6cd5\u3067\u3001Heterogeneity\u304c\u7d71\u8a08\u5b66\u7684\u6709\u610f\u3060\u3063\u305f\u306e\u3067\u3001DerSiminian-Laird\u306e\u65b9\u6cd5\u304c\u6700\u3082\u9069\u5207\u3002<\/p>\n\n\n\n
> rma.uni(yi, vi, method=\"DL\", dat=escalc1)\n\nRandom-Effects Model (k = 14; tau^2 estimator: DL)\n\ntau^2 (estimated amount of total heterogeneity): 0.3369 (SE = 0.3329)\ntau (square root of estimated tau^2 value):      0.5804\nI^2 (total heterogeneity \/ total variability):   42.68%\nH^2 (total variability \/ sampling variability):  1.74\n\nTest for Heterogeneity:\nQ(df = 13) = 22.6789, p-val = 0.0457\n\nModel Results:\n\nestimate      se     zval    pval   ci.lb   ci.ub      \n  4.5695  0.2565  17.8182  <.0001  4.0669  5.0722  *** \n\n---\nSignif. codes:  0 \u2018***\u2019 0.001 \u2018**\u2019 0.01 \u2018*\u2019 0.05 \u2018.\u2019 0.1 \u2018 \u2019 1<\/code><\/pre>\n\n\n\n
\u7d71\u5408\u30aa\u30c3\u30ba\u6bd4\u306f\u30014.57\uff0895%\u4fe1\u983c\u533a\u9593\uff1a4.07-5.07\uff09\u3068\u8a08\u7b97\u3055\u308c\u305f\u3002<\/p>\n\n\n\n
\u307e\u3068\u3081<\/h2>\n\n\n\n
\u8a3a\u65ad\u691c\u67fb\u306e\u5404\u7814\u7a76\u7d50\u679c\u3092\u7d71\u5408\u3059\u308b\u30e1\u30bf\u30a2\u30ca\u30ea\u30b7\u30b9\u306e\u65b9\u6cd5\u3092\u7d39\u4ecb\u3057\u305f\u3002<\/p>\n\n\n\n
\u8907\u6570\u7814\u7a76\u306e\u30d7\u30ed\u30c3\u30c8\u3001\u5bfe\u6570\u30aa\u30c3\u30ba\u6bd4\u306e\u91cd\u307f\u3065\u3051\u7dda\u5f62\u56de\u5e30\u306b\u3088\u308b\u30c1\u30a7\u30c3\u30af\u3001\u8981\u7d04 ROC \u66f2\u7dda\u306e\u66f8\u304d\u65b9\u3001\u7d71\u5408\u30aa\u30c3\u30ba\u6bd4\u306e\u8a08\u7b97\u65b9\u6cd5\u3092\u89e3\u8aac\u3057\u305f\u3002<\/p>\n\n\n\n
\u53c2\u8003\u306b\u306a\u308c\u3070\u3002<\/p>\n\n\n\n
\u53c2\u8003\u66f8\u7c4d<\/h2>\n\n\n\n
\u3053\u306e\u8a18\u4e8b\u3067\u7d39\u4ecb\u3057\u305f\u30b5\u30f3\u30d7\u30eb\u30c7\u30fc\u30bf\u306f\u3001\u300c\u65b0\u7248 \u30e1\u30bf\u30fb\u30a2\u30ca\u30ea\u30b7\u30b9\u5165\u9580 \u2500\u30a8\u30d3\u30c7\u30f3\u30b9\u306e\u7d71\u5408\u3092\u3081\u3056\u3059\u7d71\u8a08\u624b\u6cd5\u2500 (\u533b\u5b66\u7d71\u8a08\u5b66\u30b7\u30ea\u30fc\u30ba)<\/a>\u300d\u304c\u51fa\u5178\u5143\u3067\u3042\u308b\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
\u8a3a\u65ad\u691c\u67fb\u3092\u7d71\u5408\u3059\u308b\u65b9\u6cd5 \u611f\u5ea6\u30fb\u7279\u7570\u5ea6\u306e\u30e1\u30bf\u30a2\u30ca\u30ea\u30b7\u30b9<\/p>\n","protected":false},"author":2,"featured_media":3083,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"swell_btn_cv_data":"","_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[5,10,46],"tags":[],"class_list":["post-568","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-r","category-roc","category-46"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"https:\/\/best-biostatistics.com\/toukei-er\/wp-content\/uploads\/2018\/06\/20200920211042.png","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/posts\/568","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/comments?post=568"}],"version-history":[{"count":3,"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/posts\/568\/revisions"}],"predecessor-version":[{"id":3537,"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/posts\/568\/revisions\/3537"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/media\/3083"}],"wp:attachment":[{"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/media?parent=568"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/categories?post=568"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/tags?post=568"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}