{"id":4111,"date":"2025-07-05T18:36:57","date_gmt":"2025-07-05T09:36:57","guid":{"rendered":"https:\/\/best-biostatistics.com\/toukei-er\/?p=4111"},"modified":"2025-07-05T18:36:59","modified_gmt":"2025-07-05T09:36:59","slug":"multiple-imputation-cox-regression-wald-test-anova-chi-square-value-integration","status":"publish","type":"post","link":"https:\/\/best-biostatistics.com\/toukei-er\/entry\/multiple-imputation-cox-regression-wald-test-anova-chi-square-value-integration\/","title":{"rendered":"\u591a\u91cd\u4ee3\u5165\u6cd5\u5f8c\u306eCox\u56de\u5e30\uff1aWald\u691c\u5b9a\uff08ANOVA\uff09\u30ab\u30a4\u4e8c\u4e57\u5024\u306e\u7d71\u5408"},"content":{"rendered":"\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p>\u591a\u91cd\u4ee3\u5165\u6cd5 (Multiple Imputation, Mice) \u306f\u3001\u6b20\u640d\u5024\u306b\u5bfe\u51e6\u3059\u308b\u305f\u3081\u306e\u5f37\u529b\u306a\u7d71\u8a08\u7684\u624b\u6cd5\u3067\u3042\u308b\u3002\u3057\u304b\u3057\u3001\u591a\u91cd\u4ee3\u5165\u6cd5\u306b\u3088\u3063\u3066\u4f5c\u6210\u3055\u308c\u305f\u8907\u6570\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u305d\u308c\u305e\u308c\u306b\u5bfe\u3057\u3066\u7d71\u8a08\u89e3\u6790\u3092\u884c\u3063\u305f\u5f8c\u3001\u305d\u308c\u3089\u306e\u7d50\u679c\u3092\u3069\u306e\u3088\u3046\u306b\u7d71\u5408\u3059\u308c\u3070\u3088\u3044\u304b\u8ff7\u3046\u3053\u3068\u304c\u3042\u308b\u3002\u672c\u8a18\u4e8b\u3067\u306f\u3001\u7279\u306bCox\u6bd4\u4f8b\u30cf\u30b6\u30fc\u30c9\u56de\u5e30\u30e2\u30c7\u30eb\u306b\u304a\u3051\u308bWald\u691c\u5b9a\uff08ANOVA\uff09\u306e\u30ab\u30a4\u4e8c\u4e57\u5024\u3092\u3001<code>rms<\/code>\u30d1\u30c3\u30b1\u30fc\u30b8\u306e<code>cph<\/code>\u95a2\u6570\u3068<code>miceadds<\/code>\u30d1\u30c3\u30b1\u30fc\u30b8\u306e<code>micombine.chisquare<\/code>\u95a2\u6570\u3092\u7528\u3044\u3066\u7d71\u5408\u3059\u308b\u65b9\u6cd5\u306b\u3064\u3044\u3066\u89e3\u8aac\u3059\u308b\u3002<\/p>\n\n\n\n<!--more-->\n\n\n\n<h2 class=\"wp-block-heading\">\u3069\u3093\u306a\u3068\u304d\u306b\u4f7f\u3046\u65b9\u6cd5\u304b<\/h2>\n\n\n\n<p>\u3053\u306e\u65b9\u6cd5\u306f\u3001\u4ee5\u4e0b\u306e\u3088\u3046\u306a\u72b6\u6cc1\u3067\u7279\u306b\u5f79\u7acb\u3064\u3002<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>\u30c7\u30fc\u30bf\u306b\u6b20\u640d\u5024\u304c\u3042\u308a\u3001\u591a\u91cd\u4ee3\u5165\u6cd5\u3092\u7528\u3044\u3066\u6b20\u640d\u5024\u3092\u88dc\u5b8c\u3057\u305f\u30b1\u30fc\u30b9\u3002<\/strong><\/li>\n\n\n\n<li><strong>\u30a4\u30d9\u30f3\u30c8\u767a\u751f\u307e\u3067\u306e\u6642\u9593\uff08\u751f\u5b58\u6642\u9593\uff09\u3092\u30a2\u30a6\u30c8\u30ab\u30e0\u3068\u3059\u308bCox\u6bd4\u4f8b\u30cf\u30b6\u30fc\u30c9\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3057\u305f\u3044\u5834\u5408\u3002<\/strong><\/li>\n\n\n\n<li><strong>\u30e2\u30c7\u30eb\u306b\u542b\u307e\u308c\u308b\u7279\u5b9a\u306e\u5909\u6570\uff08\u30ab\u30c6\u30b4\u30ea\u30ab\u30eb\u5909\u6570\u306a\u3069\uff09\u306e\u5168\u4f53\u7684\u306a\u52b9\u679c\u3092\u8a55\u4fa1\u3059\u308b\u305f\u3081\u3001Wald\u691c\u5b9a\uff08ANOVA\uff09\u306e\u30ab\u30a4\u4e8c\u4e57\u5024\u3092\u5229\u7528\u3057\u305f\u3044\u5834\u5408\u3002<\/strong><\/li>\n\n\n\n<li><strong>\u591a\u91cd\u4ee3\u5165\u6cd5\u306b\u3088\u3063\u3066\u5f97\u3089\u308c\u305f\u8907\u6570\u306e\u30ab\u30a4\u4e8c\u4e57\u5024\u3092\u9069\u5207\u306b\u7d71\u5408\u3057\u3001\u5358\u4e00\u306e\u4fe1\u983c\u3067\u304d\u308bp\u5024\u3092\u5c0e\u304d\u51fa\u3057\u305f\u3044\u5834\u5408\u3002<\/strong><\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">\u3069\u306e\u3088\u3046\u306a\u65b9\u6cd5\u304b<\/h2>\n\n\n\n<p>\u591a\u91cd\u4ee3\u5165\u6cd5\u3067\u306f\u3001m \u500b\u306e\u4ee3\u5165\u6e08\u307f\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304c\u751f\u6210\u3055\u308c\u308b\u3002\u305d\u308c\u305e\u308c\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306b\u5bfe\u3057\u3066Cox\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u5f53\u3066\u306f\u3081\u3001Wald\u691c\u5b9a\uff08ANOVA\uff09\u306e\u30ab\u30a4\u4e8c\u4e57\u5024\u3092\u7b97\u51fa\u3059\u308b\u3002\u3053\u308c\u3089\u306e m \u500b\u306e\u30ab\u30a4\u4e8c\u4e57\u5024\u306f\u3001\u76f4\u63a5\u5e73\u5747\u3092\u3068\u308b\u3060\u3051\u3067\u306f\u9069\u5207\u306b\u7d71\u5408\u3067\u304d\u306a\u3044\u3002<\/p>\n\n\n\n<p>\u3053\u3053\u3067\u767b\u5834\u3059\u308b\u306e\u304c\u3001<strong>Rubin\u306e\u7d71\u5408\u30eb\u30fc\u30eb<\/strong>\u306e\u8003\u3048\u65b9\u306b\u57fa\u3065\u3044\u305f\u30ab\u30a4\u4e8c\u4e57\u5024\u306e\u7d71\u5408\u3067\u3042\u308b\u3002<code>miceadds<\/code>\u30d1\u30c3\u30b1\u30fc\u30b8\u306e<code>micombine.chisquare<\/code>\u95a2\u6570\u306f\u3001\u8907\u6570\u306e\u30ab\u30a4\u4e8c\u4e57\u5024\u3068\u305d\u308c\u305e\u308c\u306e\u81ea\u7531\u5ea6\u3092\u7528\u3044\u3066\u3001\u30d7\u30fc\u30eb\u3055\u308c\u305f\u30ab\u30a4\u4e8c\u4e57\u5024\u3068\u30d7\u30fc\u30eb\u3055\u308c\u305f\u81ea\u7531\u5ea6\u3001\u305d\u3057\u3066p\u5024\u3092\u7b97\u51fa\u3059\u308b\u3002\u3053\u306e\u95a2\u6570\u306f\u3001\u7279\u306bANOVA\u30bf\u30a4\u30d7\u306e\u30ab\u30a4\u4e8c\u4e57\u691c\u5b9a\u306e\u7d71\u5408\u306b\u9069\u3057\u3066\u3044\u308b\u3002<\/p>\n\n\n\n<p><code>rms<\/code>\u30d1\u30c3\u30b1\u30fc\u30b8\u306e<code>cph<\/code>\u95a2\u6570\u306f\u3001Cox\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u67d4\u8edf\u306b\u6307\u5b9a\u3067\u304d\u308b\u95a2\u6570\u3067\u3042\u308a\u3001\u30e2\u30c7\u30eb\u306e\u5f53\u3066\u306f\u3081\u5f8c\u306b<code>anova()<\/code>\u95a2\u6570\u3092\u7528\u3044\u308b\u3053\u3068\u3067\u3001\u5404\u5909\u6570\u306eWald\u691c\u5b9a\uff08ANOVA\uff09\u306e\u30ab\u30a4\u4e8c\u4e57\u5024\u3068\u81ea\u7531\u5ea6\u3092\u5f97\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u3002<\/p>\n\n\n\n<div id=\"biost-34099350\" class=\"biost- biost-entity-placement\"><p style=\"text-align: center;\"><span style=\"font-size: 20px;\"><strong><a href=\"https:\/\/best-biostatistics.com\/kmhl\">\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 href=\"https:\/\/best-biostatistics.com\/kmhl\"><img class=\"aligncenter wp-image-2794 size-full\" src=\"https:\/\/best-biostatistics.com\/wp\/wp-content\/uploads\/2023\/11\/bn_r_03.png\" alt=\"\" width=\"500\" height=\"327\" \/><\/a>\r\n<p style=\"text-align: center;\"><span style=\"color: #ff0000; font-size: 20px;\"><strong><span class=\"marker2\">\u21911\u4e07\u4eba\u4ee5\u4e0a\u306e\u533b\u7642\u5f93\u4e8b\u8005\u304c\u8cfc\u8aad\u4e2d<\/span><\/strong><\/span><\/p><\/div><h2 class=\"wp-block-heading\">\u5177\u4f53\u4f8b<\/h2>\n\n\n\n<p>\u3053\u3053\u3067\u306f\u3001\u67b6\u7a7a\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u7528\u3044\u3066\u5177\u4f53\u7684\u306a\u624b\u9806\u3092\u8aac\u660e\u3059\u308b\u3002<\/p>\n\n\n\n<p><strong>\u30b7\u30ca\u30ea\u30aa:<\/strong> \u3042\u308b\u75be\u60a3\u306e\u60a3\u8005\u306e\u751f\u5b58\u6642\u9593\u3092\u4e88\u6e2c\u3059\u308b\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3057\u305f\u3044\u3068\u8003\u3048\u3066\u3044\u308b\u3002\u60a3\u8005\u306e\u5e74\u9f62\u3001\u6027\u5225\u3001\u6cbb\u7642\u6cd5\uff08A, B, C\uff09\u304c\u751f\u5b58\u6642\u9593\u306b\u5f71\u97ff\u3092\u4e0e\u3048\u308b\u304b\u3092\u691c\u8a0e\u3059\u308b\u3002\u305f\u3060\u3057\u3001\u30c7\u30fc\u30bf\u306b\u306f\u6b20\u640d\u5024\u304c\u542b\u307e\u308c\u3066\u3044\u308b\u3068\u3059\u308b\u3002<\/p>\n\n\n\n<p><strong>\u76ee\u7684:<\/strong> \u6cbb\u7642\u6cd5\u304c\u751f\u5b58\u6642\u9593\u306b\u4e0e\u3048\u308b\u5168\u4f53\u7684\u306a\u52b9\u679c\u3092\u3001\u591a\u91cd\u4ee3\u5165\u6cd5\u3092\u9069\u7528\u3057\u305f\u30c7\u30fc\u30bf\u3067Cox\u56de\u5e30\u3092\u884c\u3044\u3001Wald\u691c\u5b9a\uff08ANOVA\uff09\u306e\u30ab\u30a4\u4e8c\u4e57\u5024\u3092\u7d71\u5408\u3057\u3066\u8a55\u4fa1\u3059\u308b\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\u5177\u4f53\u4f8b\u306eR\u3067\u306e\u8a08\u7b97<\/h2>\n\n\n\n<p>R \u8a08\u7b97\u4f8b\uff1a<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code># \u5fc5\u8981\u306a\u30d1\u30c3\u30b1\u30fc\u30b8\u306e\u30a4\u30f3\u30b9\u30c8\u30fc\u30eb (\u521d\u56de\u306e\u307f)\n# install.packages(\"mice\")\n# install.packages(\"rms\")\n# install.packages(\"miceadds\")\n# install.packages(\"survival\") # cph\u306e\u5185\u90e8\u3067\u4f7f\u7528\u3055\u308c\u308b\u305f\u3081\n\n# \u30d1\u30c3\u30b1\u30fc\u30b8\u306e\u30ed\u30fc\u30c9\nlibrary(mice)\nlibrary(rms)\nlibrary(miceadds)\nlibrary(survival)\n\n# \u30c7\u30fc\u30bf\u306e\u4f5c\u6210 (\u4f8b\u3068\u3057\u3066\u6b20\u640d\u5024\u3092\u542b\u3080\u30c7\u30fc\u30bf\u3092\u4f5c\u6210)\nset.seed(123)\nn &lt;- 100\ndf_orig &lt;- data.frame(\n  time = rexp(n, rate = 0.01),\n  status = sample(0:1, n, replace = TRUE, prob = c(0.3, 0.7)),\n  age = rnorm(n, 60, 10),\n  sex = factor(sample(c(\"Male\", \"Female\"), n, replace = TRUE)),\n  treatment = factor(sample(c(\"A\", \"B\", \"C\"), n, replace = TRUE, prob = c(0.4, 0.3, 0.3)))\n)\n\n# \u6b20\u640d\u5024\u3092\u30e9\u30f3\u30c0\u30e0\u306b\u5c0e\u5165\ndf_orig$age&#91;sample(1:n, 10)] &lt;- NA\ndf_orig$sex&#91;sample(1:n, 5)] &lt;- NA\ndf_orig$treatment&#91;sample(1:n, 8)] &lt;- NA\n\n# \u591a\u91cd\u4ee3\u5165\u6cd5\u306e\u5b9f\u884c\n# method = \"pmm\" (Predictive Mean Matching) \u306f\u9023\u7d9a\u5909\u6570\u3001\"polyreg\" \u306f\u30ab\u30c6\u30b4\u30ea\u30ab\u30eb\u5909\u6570\u306b\u9069\u7528\nimp &lt;- mice(df_orig, m = 5, seed = 456, printFlag = FALSE)\n\n# \u5404\u4ee3\u5165\u6e08\u307f\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3067Cox\u56de\u5e30\u3092\u5b9f\u884c\u3057\u3001Wald\u691c\u5b9a\uff08ANOVA\uff09\u306e\u30ab\u30a4\u4e8c\u4e57\u5024\u3068\u81ea\u7531\u5ea6\u3092\u62bd\u51fa\nchi_squares &lt;- list()\ndfs &lt;- list()\n\nfor (i in 1:imp$m) {\n  # i\u756a\u76ee\u306e\u4ee3\u5165\u6e08\u307f\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u53d6\u5f97\n  df_complete &lt;- complete(imp, i)\n\n  # Cox\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u69cb\u7bc9 (rms::cph\u3092\u4f7f\u7528)\n  # surv\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u3092\u4f5c\u6210\n  surv_obj &lt;- Surv(df_complete$time, df_complete$status)\n  \n  # cph\u30e2\u30c7\u30eb\u306e\u5f53\u3066\u306f\u3081\n  # x=TRUE, y=TRUE \u306f\u5f8c\u3067anova()\u3092\u4f7f\u3046\u305f\u3081\u306b\u5fc5\u8981\n  fit_cph &lt;- cph(surv_obj ~ age + sex + treatment, data = df_complete, x=TRUE, y=TRUE)\n\n  # treatment\u5909\u6570\u306eANOVA\u691c\u5b9a\u7d50\u679c\u3092\u53d6\u5f97\n  # anova(fit_cph)\u306f\u30c7\u30fc\u30bf\u30d5\u30ec\u30fc\u30e0\u3092\u8fd4\u3059\n  anova_result &lt;- anova(fit_cph)\n  \n  # treatment\u306e\u884c\u3092\u53d6\u5f97 (\u5909\u6570\u540d 'treatment' \u304c\u542b\u307e\u308c\u308b\u884c)\n  # anova_result\u306e\u884c\u540d\u306f\u5909\u6570\u540d\u306b\u306a\u3063\u3066\u3044\u308b\u3053\u3068\u304c\u591a\u3044\n  # exact\u306a\u30de\u30c3\u30c1\u30f3\u30b0 'treatment' \u3092\u884c\u3046\n  treatment_row &lt;- anova_result&#91;rownames(anova_result) == \"treatment\", ]\n\n  # \u30ab\u30a4\u4e8c\u4e57\u5024\u3068\u81ea\u7531\u5ea6\u3092\u62bd\u51fa\n  # \u30ab\u30a4\u4e8c\u4e57\u5024\u306f 'Chi-Square' \u5217\u3001\u81ea\u7531\u5ea6\u306f 'd.f.' \u5217\u306b\u683c\u7d0d\u3055\u308c\u3066\u3044\u308b\n  chi_squares&#91;&#91;i]] &lt;- treatment_row&#91;\"Chi-Square\"]\n  dfs&#91;&#91;i]] &lt;- treatment_row&#91;\"d.f.\"]\n}\n\n# \u30ea\u30b9\u30c8\u304b\u3089\u30d9\u30af\u30c8\u30eb\u306b\u5909\u63db\nchi_squares_vec &lt;- unlist(chi_squares)\ndfs_vec &lt;- unlist(dfs)\n\n# \u7d71\u5408\u3055\u308c\u305f\u30ab\u30a4\u4e8c\u4e57\u5024\u3092\u8a08\u7b97\n# micombine.chisquare\u95a2\u6570\u3092\u4f7f\u7528\ncombined_result &lt;- micombine.chisquare(\n    dk = chi_squares_vec,\n    df = dfs_vec&#91;1]\n)<\/code><\/pre>\n\n\n\n<h3 class=\"wp-block-heading\">\u5b9f\u884c\u7d50\u679c\u3068\u89e3\u91c8<\/h3>\n\n\n\n<p>\u4e0a\u8a18\u306eR\u30b3\u30fc\u30c9\u3092\u5b9f\u884c\u3059\u308b\u3068\u3001<code>combined_result<\/code>\u30aa\u30d6\u30b8\u30a7\u30af\u30c8\u306b\u7d71\u5408\u3055\u308c\u305f\u30ab\u30a4\u4e8c\u4e57\u691c\u5b9a\u306e\u7d50\u679c\u304c\u683c\u7d0d\u3055\u308c\u308b\u3002\u51fa\u529b\u306f\u4ee5\u4e0b\u306e\u3088\u3046\u306b\u306a\u308b\u3060\u308d\u3046\u3002<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>> combined_result &lt;- micombine.chisquare(\n+     dk = chi_squares_vec,\n+     df = dfs_vec&#91;1]\n+ )\nCombination of Chi Square Statistics for Multiply Imputed Data\nUsing 5 Imputed Data Sets\nF(2, 46.03)=1.969     p=0.1512 \n> <\/code><\/pre>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong><code>F(2, 46.03)<\/code><\/strong>: \u7d71\u5408\u3055\u308c\u305f\u30ab\u30a4\u4e8c\u4e57\u7d71\u8a08\u91cf\u3067\u3042\u308b\u3002\u30ab\u30c3\u30b3\u5185\u306e\u6570\u5b57\u306f\u81ea\u7531\u5ea6\u3067\u3042\u308b\u3002<\/li>\n\n\n\n<li><strong><code>p-value<\/code><\/strong>: \u7d71\u5408\u3055\u308c\u305f\u30ab\u30a4\u4e8c\u4e57\u7d71\u8a08\u91cf\u3068\u81ea\u7531\u5ea6\u306b\u57fa\u3065\u3044\u305fp\u5024\u3067\u3042\u308b\u3002\u3053\u306ep\u5024\u306f\u3001<code>treatment<\/code>\u3068\u3044\u3046\u5909\u6570\u5168\u4f53\u304c\u3001\u4ed6\u306e\u5909\u6570\u3092\u8abf\u6574\u3057\u305f\u5f8c\u3067\u3082\u751f\u5b58\u6642\u9593\u306b\u6709\u610f\u306a\u5f71\u97ff\u3092\u4e0e\u3048\u308b\u304b\u3069\u3046\u304b\u3092\u793a\u3059\u3002<\/li>\n<\/ul>\n\n\n\n<p>\u3053\u306e\u4f8b\u3067\u306f\u3001<code>p-value<\/code>\u304c0.05\u3092\u4e0b\u56de\u3063\u3066\u3044\u306a\u3044\u306e\u3067\u3001\u7d71\u8a08\u7684\u306b\u6709\u610f\u306a\u7d50\u679c\u3068\u306f\u8a00\u3048\u306a\u3044\u3002\u3053\u308c\u306f\u3001\u6cbb\u7642\u6cd5\u304c\u751f\u5b58\u6642\u9593\u306b\u6709\u610f\u306a\u5f71\u97ff\u3092\u4e0e\u3048\u308b\u3068\u306f\u8a00\u3048\u306a\u3044\u3053\u3068\u3092\u793a\u5506\u3057\u3066\u3044\u308b\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\u307e\u3068\u3081<\/h2>\n\n\n\n<p>\u591a\u91cd\u4ee3\u5165\u6cd5\u5f8c\u306eCox\u56de\u5e30\u306b\u304a\u3044\u3066\u3001\u7279\u5b9a\u306e\u5909\u6570\uff08\u7279\u306b\u30ab\u30c6\u30b4\u30ea\u30ab\u30eb\u5909\u6570\u306a\u3069\u8907\u6570\u306e\u81ea\u7531\u5ea6\u3092\u6301\u3064\u5909\u6570\uff09\u306e\u5168\u4f53\u7684\u306a\u52b9\u679c\u3092\u8a55\u4fa1\u3059\u308b\u969b\u306b\u306f\u3001Wald\u691c\u5b9a\uff08ANOVA\uff09\u306e\u30ab\u30a4\u4e8c\u4e57\u5024\u3092\u7d71\u5408\u3059\u308b\u5fc5\u8981\u304c\u3042\u308b\u3002<\/p>\n\n\n\n<p>\u672c\u8a18\u4e8b\u3067\u7d39\u4ecb\u3057\u305f<code>rms<\/code>\u30d1\u30c3\u30b1\u30fc\u30b8\u306e<code>cph<\/code>\u95a2\u6570\u3068<code>miceadds<\/code>\u30d1\u30c3\u30b1\u30fc\u30b8\u306e<code>micombine.chisquare<\/code>\u95a2\u6570\u3092\u7d44\u307f\u5408\u308f\u305b\u308b\u3053\u3068\u3067\u3001\u3053\u306e\u30d7\u30ed\u30bb\u30b9\u3092\u52b9\u679c\u7684\u306b\u5b9f\u884c\u3067\u304d\u308b\u3002\u3053\u308c\u306b\u3088\u308a\u3001\u591a\u91cd\u4ee3\u5165\u6cd5\u306b\u3088\u308b\u6b20\u640d\u5024\u88dc\u5b8c\u306e\u6069\u6075\u3092\u53d7\u3051\u3064\u3064\u3001\u7d71\u8a08\u7684\u306b\u9069\u5207\u306a\u65b9\u6cd5\u3067\u30e2\u30c7\u30eb\u306e\u8a55\u4fa1\u3092\u884c\u3046\u3053\u3068\u304c\u53ef\u80fd\u3068\u306a\u308b\u3002<\/p>\n\n\n\n<p>\u3053\u306e\u65b9\u6cd5\u306f\u3001\u81e8\u5e8a\u7814\u7a76\u3084\u75ab\u5b66\u7814\u7a76\u306a\u3069\u3001\u6b20\u640d\u5024\u304c\u983b\u7e41\u306b\u767a\u751f\u3059\u308b\u5206\u91ce\u3067Cox\u56de\u5e30\u30e2\u30c7\u30eb\u3092\u6271\u3046\u7814\u7a76\u8005\u306b\u3068\u3063\u3066\u975e\u5e38\u306b\u6709\u7528\u3067\u3042\u308b\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u591a\u91cd\u4ee3\u5165\u6cd5 (Multiple Imputation, Mice) \u306f\u3001\u6b20\u640d\u5024\u306b\u5bfe\u51e6\u3059\u308b\u305f\u3081\u306e\u5f37\u529b\u306a\u7d71\u8a08\u7684\u624b\u6cd5\u3067\u3042\u308b\u3002\u3057\u304b\u3057\u3001\u591a\u91cd\u4ee3\u5165\u6cd5\u306b\u3088\u3063\u3066\u4f5c\u6210\u3055\u308c\u305f\u8907\u6570\u306e\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u305d\u308c\u305e\u308c\u306b\u5bfe\u3057\u3066\u7d71\u8a08\u89e3\u6790\u3092\u884c\u3063\u305f\u5f8c\u3001\u305d\u308c\u3089\u306e\u7d50\u679c [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"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":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[21,20],"tags":[],"class_list":["post-4111","post","type-post","status-publish","format-standard","hentry","category-21","category-20"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/posts\/4111","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=4111"}],"version-history":[{"count":4,"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/posts\/4111\/revisions"}],"predecessor-version":[{"id":4115,"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/posts\/4111\/revisions\/4115"}],"wp:attachment":[{"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/media?parent=4111"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/categories?post=4111"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/tags?post=4111"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}