{"id":432,"date":"2019-08-06T22:13:20","date_gmt":"2019-08-06T13:13:20","guid":{"rendered":"https:\/\/best-biostatistics.com\/toukei-er\/entry\/survival-analysis-in-r\/"},"modified":"2024-10-07T12:37:44","modified_gmt":"2024-10-07T03:37:44","slug":"survival-analysis-in-r","status":"publish","type":"post","link":"https:\/\/best-biostatistics.com\/toukei-er\/entry\/survival-analysis-in-r\/","title":{"rendered":"R \u3067\u751f\u5b58\u6642\u9593\u30c7\u30fc\u30bf\u3092\u5206\u6790\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"\n

Cox\u306e\u6bd4\u4f8b\u30cf\u30b6\u30fc\u30c9\u30e2\u30c7\u30eb\uff08\u30b3\u30c3\u30af\u30b9\u306e\u6bd4\u4f8b\u30cf\u30b6\u30fc\u30c9\u30e2\u30c7\u30eb\u3001Cox\u56de\u5e30\u3001\u30b3\u30c3\u30af\u30b9\u56de\u5e30\u306a\u3069\u8868\u793a\u30fb\u547c\u3073\u540d\u306f\u305f\u304f\u3055\u3093\u3042\u308b\u304c\u7686\u540c\u3058\u3082\u306e\u3092\u6307\u3057\u3066\u3044\u308b\uff09\u306f\u3001\u751f\u5b58\u6642\u9593\u3068\u30a4\u30d9\u30f3\u30c8\u30c7\u30fc\u30bf\u3092\u591a\u5909\u91cf\u89e3\u6790\u3067\u304d\u308b\u7d71\u8a08\u30e2\u30c7\u30eb\u3060\u3002<\/p>\n\n\n\n

\u6ce8\u76ee\u3057\u305f\u3044\u8981\u56e0\u304c\u3001\u4ed6\u306e\u8981\u56e0\u3068\u76f8\u95a2\u304c\u3042\u308a\u3001\u307e\u305f\u4ed6\u306e\u8981\u56e0\u304c\u30a4\u30d9\u30f3\u30c8\u306b\u3082\u5f71\u97ff\u3092\u4e0e\u3048\u3066\u3044\u308b\uff08\u4ea4\u7d61\u8981\u56e0\u3068\u547c\u3070\u308c\u308b\uff09\u3068\u304d\u306b\u7528\u3044\u3089\u308c\u308b\u65b9\u6cd5\u3060\u3002<\/p>\n\n\n\n

R \u3067 Cox \u56de\u5e30\u306e\u65b9\u6cd5\u306e\u89e3\u8aac\u3002<\/p>\n\n\n\n\n\n\n\n

R\u3067Cox\u6bd4\u4f8b\u30cf\u30b6\u30fc\u30c9\u30e2\u30c7\u30eb\u3092\u5b9f\u65bd\u3059\u308b\u305f\u3081\u306e\u30b5\u30f3\u30d7\u30eb\u30c7\u30fc\u30bf<\/h2>\n\n\n\n

survival \u30d1\u30c3\u30b1\u30fc\u30b8\u306e lung \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u4f7f\u3046\u3002<\/p>\n\n\n\n

\u3053\u308c\u306f\u3001\u5317\u90e8\u4e2d\u592e\u304c\u3093\u6cbb\u7642\u30b0\u30eb\u30fc\u30d7\uff08North Central Cancer Treatment Group, NCCTG\uff09\u63d0\u4f9b\u306e\u9032\u884c\u80ba\u304c\u3093\u60a3\u8005\u3055\u3093\u306e\u751f\u5b58\u30c7\u30fc\u30bf\u3002<\/p>\n\n\n\n

\u8a73\u3057\u304f\u306f\u3053\u3061\u3089\u3082\u53c2\u7167\u3002<\/p>\n\n\n

\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\u3042\u308f\u305b\u3066\u8aad\u307f\u305f\u3044<\/span>\n\t\t\t\t\t
\"\"<\/figure><\/div>\t\t\t\t\t
\n\t\t\t\t\t\tR \u3067\u30ab\u30d7\u30e9\u30f3\u30de\u30a4\u30e4\u30fc\u66f2\u7dda\u3092\u30b0\u30eb\u30fc\u30d7\u3054\u3068\u306b\u66f8\u304f\u65b9\u6cd5<\/a>\n\t\t\t\t\t\tR\u3067\u30ab\u30d7\u30e9\u30f3\u30de\u30a4\u30e4\u30fc\u66f2\u7dda\u3092\u66f8\u304f\u65b9\u6cd5\u306e\u7d39\u4ecb\u3002 survfit \u3092\u4f7f\u3063\u305f\u30b0\u30eb\u30fc\u30d7\u3054\u3068\u306e\u66f2\u7dda\u306e\u66f8\u304d\u65b9\u3002 R\u3067\u30ab\u30d7\u30e9\u30f3\u30de\u30a4\u30e4\u30fc\u66f2\u7dda\u3092\u66f8\u304f\u305f\u3081\u306e\u30b5\u30f3\u30d7\u30eb\u30c7\u30fc\u30bf \u30ab\u30d7\u30e9\u30f3\u30de\u30a4\u30e4\u30fc\u66f2\u7dda…<\/span>\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n\n\n

\u307e\u305a\u3001survival\u3092\u547c\u3073\u51fa\u3059\u3002<\/p>\n\n\n\n

library<\/span>(<\/span>survival)<\/span>\n<\/code><\/pre>\n\n\n\n
\u5168\u8eab\u72b6\u614b\u3092\u8868\u3059ECOG performance score\u3067\u898b\u308b\u3068\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u4e2d\u3067\u3082\u3063\u3068\u3082\u72b6\u614b\u304c\u60aa\u3044\uff13\u306e\u75c7\u4f8b\u304c\u4e00\u4f8b\u3057\u304b\u3044\u306a\u3044\u3002<\/p>\n\n\n\n
\u4eca\u56de\u306f\u8a08\u7b97\u3084\u4f5c\u56f3\u306e\u308f\u304b\u308a\u3084\u3059\u3055\u306e\u95a2\u4fc2\u4e0a\u3001\u305d\u306e\u4e00\u4f8b\u3092\u9664\u5916\u3057\u3066\u9032\u3081\u308b\u3002<\/p>\n\n\n\n
\u73fe\u5b9f\u306e\u30c7\u30fc\u30bf\u89e3\u6790\u3067\u306f\u3001\u3053\u3046\u3044\u3046\u4e00\u4f8b\u3092\u9664\u5916\u3059\u308b\u304b\u3069\u3046\u304b\u3001\u9664\u5916\u3059\u308b\u306a\u3089\u9069\u5207\u306a\u7406\u7531\u304c\u3064\u304f\u304b\u3069\u3046\u304b\u3001\u5927\u5909\u614e\u91cd\u306b\u9032\u3081\u308b\u5fc5\u8981\u304c\u3042\u308b\u306e\u306f\u8a00\u3046\u307e\u3067\u3082\u306a\u3044\u3002<\/p>\n\n\n\n
lung1 <-<\/span> lung[<\/span>lung$<\/span>ph.ecog<<\/span>3<\/span>,]<\/span>\n<\/code><\/pre>\n\n\n\n
R\u3067Cox\u6bd4\u4f8b\u30cf\u30b6\u30fc\u30c9\u30e2\u30c7\u30eb\u3092\u5b9f\u65bd\u3059\u308b\u6e96\u5099<\/h2>\n\n\n\n
\u30ab\u30d7\u30e9\u30f3\u30de\u30a4\u30e4\u30fc\u66f2\u7dda\u3068\u30ed\u30b0\u30e9\u30f3\u30af\u691c\u5b9a\u3067\u30a8\u30f3\u30c9\u30dd\u30a4\u30f3\u30c8\u3068\u306e\u95a2\u9023\u3092\u78ba\u8a8d\u3057\u3066\u304a\u304f<\/h3>\n\n\n\n
\u4eca\u56de\u3001ECOG performance score\u3067\u751f\u5b58\u6642\u9593\u306b\u9055\u3044\u304c\u3042\u308b\u304b\u306e\u89e3\u6790\u3092\u884c\u3046\u3002<\/p>\n\n\n\n
\u6027\u5225\u3084\u5e74\u9f62\u306f\u751f\u5b58\u30fb\u6b7b\u4ea1\u306b\u95a2\u9023\u3042\u308b\u306e\u306f\u8a00\u3046\u307e\u3067\u3082\u306a\u3044\u304c\u3001ECOG performance score\u306b\u3082\u95a2\u9023\u3042\u308b\u3068\u4eee\u5b9a\u3059\u308b\u3002<\/p>\n\n\n\n
\u307e\u305a\u3001ECOG performance score\u5225\u306b\u751f\u5b58\u671f\u9593\u304c\u7570\u306a\u308b\u304b\u691c\u8a0e\u3059\u308b\u3002<\/p>\n\n\n\n
survfit1 <-<\/span> survfit<\/span>(<\/span>Surv<\/span>(<\/span>time,<\/span>status)<\/span>~<\/span>factor<\/span>(<\/span>ph.ecog),<\/span>data=<\/span>lung1)<\/span>\nplot<\/span>(<\/span>survfit1,<\/span> col=<\/span>2<\/span>:<\/span>4<\/span>,<\/span> las=<\/span>1<\/span>,<\/span> xlab=<\/span>\"Survival Time (days)\"<\/span>,<\/span> ylab=<\/span>\"Overall Survival\"<\/span>)<\/span>\nlegend<\/span>(<\/span>\"topright\"<\/span>,<\/span> paste<\/span>(<\/span>\"ECOG PS=\"<\/span>,<\/span>0<\/span>:<\/span>2<\/span>,<\/span> sep=<\/span>\"\"<\/span>),<\/span> lty=<\/span>1<\/span>,<\/span> col=<\/span>2<\/span>:<\/span>4<\/span>)<\/span>\nsurvfit1\nsurvdiff<\/span>(<\/span>Surv<\/span>(<\/span>time,<\/span>status)<\/span>~<\/span>factor<\/span>(<\/span>ph.ecog),<\/span>data=<\/span>lung1)<\/span>\n<\/code><\/pre>\n\n\n\n
ECOG performance score\u9806\u306b\u3060\u3093\u3060\u3093\u4e88\u5f8c\u304c\u60aa\u304f\u306a\u308b\u306e\u304c\u308f\u304b\u308b\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
<\/span><\/p>\n\n\n\n
survfit()\u306e\u7d50\u679c\u306f\u3001ECOG performance score\u5225\u306b\u3001n\u3001\u30a4\u30d9\u30f3\u30c8\u6570\u3001\u751f\u5b58\u65e5\u6570\u4e2d\u592e\u5024\u300195%\u4fe1\u983c\u533a\u9593\u4e0b\u9650\u3001\u4e0a\u9650\u304c\u8868\u793a\u3055\u308c\u308b\u3002<\/p>\n\n\n\n
survdiff()\u306e\u7d50\u679c\u306f\u3001\u30ed\u30b0\u30e9\u30f3\u30af\u691c\u5b9a\u306e\u7d50\u679c\u3067\u3001ECOG performance score\u9593\u3067\u7d71\u8a08\u5b66\u7684\u306b\u6709\u610f\u306a\u5dee\u304c\u3042\u308b\u3053\u3068\u304c\u308f\u304b\u308b\uff08 p=1e-04 = $ 10^4 $ = 0.001 \uff09\u3002<\/p>\n\n\n\n
><\/span> survfit1\nCall:<\/span> survfit<\/span>(<\/span>formula =<\/span> Surv<\/span>(<\/span>time,<\/span> status)<\/span> ~<\/span> factor<\/span>(<\/span>ph.ecog),<\/span> data =<\/span> lung1)<\/span>\n1<\/span> observation deleted due to missingness\nn events median 0.95<\/span>LCL 0.95<\/span>UCL\nfactor<\/span>(<\/span>ph.ecog)<\/span>=<\/span>0<\/span>  63<\/span>     37<\/span>    394<\/span>     348<\/span>     574<\/span>\nfactor<\/span>(<\/span>ph.ecog)<\/span>=<\/span>1<\/span> 113<\/span>     82<\/span>    306<\/span>     268<\/span>     429<\/span>\nfactor<\/span>(<\/span>ph.ecog)<\/span>=<\/span>2<\/span>  50<\/span>     44<\/span>    199<\/span>     156<\/span>     288<\/span>\n><\/span> survdiff<\/span>(<\/span>Surv<\/span>(<\/span>time,<\/span>status)<\/span>~<\/span>factor<\/span>(<\/span>ph.ecog),<\/span>data=<\/span>lung1)<\/span>\nCall:<\/span>\nsurvdiff<\/span>(<\/span>formula =<\/span> Surv<\/span>(<\/span>time,<\/span> status)<\/span> ~<\/span> factor<\/span>(<\/span>ph.ecog),<\/span> data =<\/span> lung1)<\/span>\nn=<\/span>226<\/span>,<\/span> 1<\/span> observation deleted due to missingness.\nN Observed Expected <\/span>(<\/span>O-<\/span>E)<\/span>^<\/span>2<\/span>\/<\/span>E <\/span>(<\/span>O-<\/span>E)<\/span>^<\/span>2<\/span>\/<\/span>V\nfactor<\/span>(<\/span>ph.ecog)<\/span>=<\/span>0<\/span>  63<\/span>       37<\/span>     53.9<\/span>    5.3014<\/span>    8.0181<\/span>\nfactor<\/span>(<\/span>ph.ecog)<\/span>=<\/span>1<\/span> 113<\/span>       82<\/span>     83.1<\/span>    0.0144<\/span>    0.0295<\/span>\nfactor<\/span>(<\/span>ph.ecog)<\/span>=<\/span>2<\/span>  50<\/span>       44<\/span>     26.0<\/span>   12.4571<\/span>   14.9754<\/span>\nChisq=<\/span> 18<\/span>  on 2<\/span> degrees of freedom,<\/span> p=<\/span> 1e-04<\/span>\n<\/code><\/pre>\n\n\n\n
\u6b21\u306b\u3001\u6027\u5225\u3067\u751f\u5b58\u671f\u9593\u304c\u7570\u306a\u308b\u304b\u78ba\u8a8d\u3059\u308b\u3002<\/p>\n\n\n\n
survfit2 <-<\/span> survfit<\/span>(<\/span>Surv<\/span>(<\/span>time,<\/span>status)<\/span>~<\/span>factor<\/span>(<\/span>sex),<\/span>data=<\/span>lung1)<\/span>\nplot<\/span>(<\/span>survfit2,<\/span>col=<\/span>1<\/span>:<\/span>2<\/span>,<\/span> las=<\/span>1<\/span>,<\/span> xlab=<\/span>\"Survival Time (days)\"<\/span>,<\/span> ylab=<\/span>\"Overall Survival\"<\/span>)<\/span>\nlegend<\/span>(<\/span>\"topright\"<\/span>,<\/span> c<\/span>(<\/span>\"Male\"<\/span>,<\/span>\"Female\"<\/span>),<\/span> lty=<\/span>1<\/span>,<\/span> col=<\/span>1<\/span>:<\/span>2<\/span>)<\/span>\nsurvfit2\nsurvdiff<\/span>(<\/span>Surv<\/span>(<\/span>time,<\/span>status)<\/span>~<\/span>factor<\/span>(<\/span>sex),<\/span>data=<\/span>lung1)<\/span>\n<\/code><\/pre>\n\n\n\n
\u30b0\u30e9\u30d5\u3092\u898b\u308b\u3068\u5973\u6027\u306e\u307b\u3046\u304c\u751f\u5b58\u671f\u9593\u304c\u9577\u304f\u898b\u3048\u308b\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
<\/span><\/p>\n\n\n\n
\u7d50\u679c\u306f\u3001\u5973\u6027\uff08sex=2\uff09\u306e\u307b\u3046\u304c\u7537\u6027\uff08sex=1\uff09\u306b\u6bd4\u3079\u3001\u751f\u5b58\u671f\u9593\u4e2d\u592e\u5024\u304c\u9577\u304f\u3001\u30ed\u30b0\u30e9\u30f3\u30af\u691c\u5b9a\u3067\u3082\u7d71\u8a08\u5b66\u7684\u306b\u6709\u610f\u306a\u5dee\u304c\u3042\u3063\u305f\uff08 p = 0.002 \uff09\u3002<\/p>\n\n\n\n
><\/span> survfit2\nCall:<\/span> survfit<\/span>(<\/span>formula =<\/span> Surv<\/span>(<\/span>time,<\/span> status)<\/span> ~<\/span> factor<\/span>(<\/span>sex),<\/span> data =<\/span> lung1)<\/span>\n1<\/span> observation deleted due to missingness\nn events median 0.95<\/span>LCL 0.95<\/span>UCL\nfactor<\/span>(<\/span>sex)<\/span>=<\/span>1<\/span> 136<\/span>    110<\/span>    283<\/span>     218<\/span>     320<\/span>\nfactor<\/span>(<\/span>sex)<\/span>=<\/span>2<\/span>  90<\/span>     53<\/span>    426<\/span>     348<\/span>     550<\/span>\n><\/span> survdiff<\/span>(<\/span>Surv<\/span>(<\/span>time,<\/span>status)<\/span>~<\/span>factor<\/span>(<\/span>sex),<\/span>data=<\/span>lung1)<\/span>\nCall:<\/span>\nsurvdiff<\/span>(<\/span>formula =<\/span> Surv<\/span>(<\/span>time,<\/span> status)<\/span> ~<\/span> factor<\/span>(<\/span>sex),<\/span> data =<\/span> lung1)<\/span>\nn=<\/span>226<\/span>,<\/span> 1<\/span> observation deleted due to missingness.\nN Observed Expected <\/span>(<\/span>O-<\/span>E)<\/span>^<\/span>2<\/span>\/<\/span>E <\/span>(<\/span>O-<\/span>E)<\/span>^<\/span>2<\/span>\/<\/span>V\nfactor<\/span>(<\/span>sex)<\/span>=<\/span>1<\/span> 136<\/span>      110<\/span>     90.3<\/span>      4.29<\/span>      9.72<\/span>\nfactor<\/span>(<\/span>sex)<\/span>=<\/span>2<\/span>  90<\/span>       53<\/span>     72.7<\/span>      5.33<\/span>      9.72<\/span>\nChisq=<\/span> 9.7<\/span>  on 1<\/span> degrees of freedom,<\/span> p=<\/span> 0.002<\/span>\n<\/code><\/pre>\n\n\n\n
\u305d\u308c\u3067\u306f\u5e74\u9f62\u306f\u3069\u3046\u304b\uff1f<\/p>\n\n\n\n
10\u6b73\u306e\u533a\u5207\u308a\u3092\u4e2d\u5fc3\u306b\u3001\u7d0410\u5e74\u9593\u9694\u3067\u30b0\u30eb\u30fc\u30d7\u3092\u4f5c\u3063\u3066\u3001\u4e88\u5099\u7684\u306b\u691c\u8a0e\u3057\u3066\u307f\u308b\u3002<\/p>\n\n\n\n
lung1$<\/span>agec <-<\/span> round<\/span>(<\/span>lung1$<\/span>age\/<\/span>10<\/span>)<\/span>\ntable<\/span>(<\/span>lung1$<\/span>age,<\/span> lung1$<\/span>agec)<\/span> # \u52a0\u5de5\u3057\u305f\u5909\u6570\u306e\u78ba\u8a8d<\/span>\n<\/code><\/pre>\n\n\n\n
\u30b0\u30e9\u30d5\u3092\u63cf\u3044\u3066\u3001\u30ed\u30b0\u30e9\u30f3\u30af\u691c\u5b9a\u3092\u3057\u3066\u307f\u308b\u3002<\/p>\n\n\n\n
survfit3 <-<\/span> survfit<\/span>(<\/span>Surv<\/span>(<\/span>time,<\/span>status)<\/span>~<\/span>agec,<\/span> data=<\/span>lung1)<\/span>\nplot<\/span>(<\/span>survfit3,<\/span>col=<\/span>1<\/span>:<\/span>5<\/span>,<\/span> las=<\/span>1<\/span>,<\/span> xlab=<\/span>\"Survival Time (days)\"<\/span>,<\/span> ylab=<\/span>\"Overall Survival\"<\/span>)<\/span>\nlegend<\/span>(<\/span>\"topright\"<\/span>,<\/span> paste<\/span>(<\/span>\"Around\"<\/span>,<\/span> (<\/span>4<\/span>:<\/span>8<\/span>)<\/span>*<\/span>10<\/span>),<\/span> lty=<\/span>1<\/span>,<\/span> col=<\/span>1<\/span>:<\/span>5<\/span>)<\/span>\nsurvfit3\nsurvdiff<\/span>(<\/span>Surv<\/span>(<\/span>time,<\/span>status)<\/span>~<\/span>agec,<\/span>data=<\/span>lung1)<\/span>\n<\/code><\/pre>\n\n\n\n
\u30b0\u30e9\u30d5\u304b\u3089\u306f\u3001\u5e74\u9f62\u30b0\u30eb\u30fc\u30d7\u3067\u5927\u304d\u306a\u5dee\u306f\u898b\u3089\u308c\u306a\u3044\u3002<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
<\/span><\/p>\n\n\n\n
\u751f\u5b58\u65e5\u6570\u4e2d\u592e\u5024\u3092\u898b\u3066\u3082\u3001\u30ed\u30b0\u30e9\u30f3\u30af\u691c\u5b9a\u306e\u7d50\u679c\u3092\u898b\u3066\u3082\u3001\u9855\u8457\u306a\u5dee\u306f\u898b\u3089\u308c\u305a\u3001\u6b7b\u4ea1\u3068\u5f37\u3044\u95a2\u9023\u306f\u306a\u3044\u3068\u8a00\u3048\u308b\u3002<\/p>\n\n\n\n
><\/span> survfit3\nCall:<\/span> survfit<\/span>(<\/span>formula =<\/span> Surv<\/span>(<\/span>time,<\/span> status)<\/span> ~<\/span> agec,<\/span> data =<\/span> lung1)<\/span>\n1<\/span> observation deleted due to missingness\nn events median 0.95<\/span>LCL 0.95<\/span>UCL\nagec=<\/span>4<\/span> 12<\/span>      4<\/span>     NA<\/span>     268<\/span>      NA<\/span>\nagec=<\/span>5<\/span> 31<\/span>     24<\/span>    305<\/span>     223<\/span>     533<\/span>\nagec=<\/span>6<\/span> 92<\/span>     63<\/span>    348<\/span>     285<\/span>     477<\/span>\nagec=<\/span>7<\/span> 75<\/span>     59<\/span>    306<\/span>     269<\/span>     426<\/span>\nagec=<\/span>8<\/span> 16<\/span>     13<\/span>    222<\/span>     116<\/span>      NA<\/span>\n><\/span> survdiff<\/span>(<\/span>Surv<\/span>(<\/span>time,<\/span>status)<\/span>~<\/span>agec,<\/span>data=<\/span>lung1)<\/span>\nCall:<\/span>\nsurvdiff<\/span>(<\/span>formula =<\/span> Surv<\/span>(<\/span>time,<\/span> status)<\/span> ~<\/span> agec,<\/span> data =<\/span> lung1)<\/span>\nn=<\/span>226<\/span>,<\/span> 1<\/span> observation deleted due to missingness.\nN Observed Expected <\/span>(<\/span>O-<\/span>E)<\/span>^<\/span>2<\/span>\/<\/span>E <\/span>(<\/span>O-<\/span>E)<\/span>^<\/span>2<\/span>\/<\/span>V\nagec=<\/span>4<\/span> 12<\/span>        4<\/span>     9.33<\/span>    3.0490<\/span>    3.2724<\/span>\nagec=<\/span>5<\/span> 31<\/span>       24<\/span>    23.13<\/span>    0.0328<\/span>    0.0386<\/span>\nagec=<\/span>6<\/span> 92<\/span>       63<\/span>    68.84<\/span>    0.4956<\/span>    0.8695<\/span>\nagec=<\/span>7<\/span> 75<\/span>       59<\/span>    53.91<\/span>    0.4816<\/span>    0.7249<\/span>\nagec=<\/span>8<\/span> 16<\/span>       13<\/span>     7.79<\/span>    3.4844<\/span>    3.7060<\/span>\nChisq=<\/span> 7.7<\/span>  on 4<\/span> degrees of freedom,<\/span> p=<\/span> 0.1<\/span>\n<\/code><\/pre>\n\n\n\n
\u3068\u306f\u3044\u3046\u3082\u306e\u306e\u3001\u751f\u6b7b\u306b\u304b\u304b\u308f\u308b\u4e00\u756a\u306e\u8981\u56e0\u306f\u5e74\u9f62\u306a\u306e\u3067\u3001\u5e74\u9f62\u306f\u5171\u5909\u91cf\u304b\u3089\u5916\u3055\u305a\u306b\u9032\u3080\u3053\u3068\u306b\u3059\u308b\u3002<\/p>\n\n\n\n
\u591a\u5909\u91cf\u30e2\u30c7\u30eb\u306e\u5909\u6570\u306f\u3001\u6700\u7d42\u7684\u306b\u306f\u7814\u7a76\u8005\u304c\u6c7a\u3081\u308b\u3082\u306e\u3002<\/p>\n\n\n\n
\u9014\u4e2d\u306e\u8a08\u7b97\u306f\u53c2\u8003\u3068\u3057\u3066\u8003\u3048\u3001\u6700\u7d42\u30e2\u30c7\u30eb\u306f\u7814\u7a76\u8005\u304c\u6c7a\u3081\u308b\u3002<\/p>\n\n\n\n
\u8208\u5473\u306e\u3042\u308b\u5909\u6570\u3068\u5171\u5909\u91cf\u306e\u95a2\u9023\u3092\u307f\u3066\u304a\u304f<\/h3>\n\n\n\n
\u4eca\u56de\u8208\u5473\u304c\u3042\u308b\u5909\u6570\u306fECOG performance score\u3067\u3042\u308b\u3002<\/p>\n\n\n\n
\u3053\u306e\u30b9\u30b3\u30a2\u304c\u6027\u5225\u3084\u5e74\u9f62\u3067\u7570\u306a\u308b\u304b\u3069\u3046\u304b\u898b\u3066\u304a\u304f\u5fc5\u8981\u304c\u3042\u308b\u3002<\/p>\n\n\n\n
\u307b\u307c\u76f8\u95a2\u304c\u306a\u3051\u308c\u3070\u3001\u3044\u304f\u3089\u30a8\u30f3\u30c9\u30dd\u30a4\u30f3\u30c8\u306b\u95a2\u4fc2\u3057\u3066\u3044\u308b\u4e88\u5f8c\u56e0\u5b50\u3067\u3082\u3001\u8abf\u6574\u3057\u306a\u304f\u3066\u3088\u3044\u3002<\/p>\n\n\n\n
\u4ea4\u7d61\u56e0\u5b50\u306e\u6761\u4ef6\u3092\u6e80\u305f\u3055\u306a\u3044\u305f\u3081\u3060\u3002<\/p>\n\n\n\n
\u4ea4\u7d61\u56e0\u5b50\u306e\u6761\u4ef6\u306f\u4ee5\u4e0b\u306e 3 \u3064<\/p>\n\n\n\n
    \n
  1. \u4e88\u5f8c\u56e0\u5b50\u3067\u3042\u308b<\/li>\n\n\n\n
  2. \u76f8\u95a2\u304c\u3042\u308b<\/li>\n\n\n\n
  3. \u4e2d\u9593\u56e0\u5b50\u3067\u306f\u306a\u3044<\/li>\n<\/ol>\n\n\n\n
    \u9006\u306b\u5c11\u3057\u3067\u3082\u95a2\u9023\u6027\u3092\u611f\u3058\u308c\u3070\u3001\u8abf\u6574\u3057\u3066\u304a\u304f\u307b\u3046\u304c\u7121\u96e3\u3060\u3002<\/p>\n\n\n\n
    \u307e\u305a\u3001ECOG performance score\u3068\u6027\u5225\u306e\u95a2\u9023\u3092\u898b\u308b\u3002<\/p>\n\n\n\n
    ECOG performance score\u306f3\u6bb5\u968e\u3001\u6027\u5225\u306f\u7537\u5973\u30672\u3064\u306a\u306e\u3067\u30013×2\u306e\u5206\u5272\u8868\u3067\u8003\u3048\u308b\u3002<\/p>\n\n\n\n
    ECOG\u306b\u306f\u9806\u756a\u304c\u3042\u308b\u304c\u3001\u305d\u306e\u30c8\u30ec\u30f3\u30c9\u304c\u3069\u3046\u3068\u3044\u3046\u3088\u308a\u3082\u30013\u30b0\u30eb\u30fc\u30d7\u3067\u6027\u5225\u306e\u5272\u5408\u304c\u9055\u3046\u304b\u3060\u3051\u3092\u898b\u308b\u3053\u3068\u306b\u3059\u308b\u3002<\/p>\n\n\n\n
    \u3053\u306e\u5834\u5408Fisher Exact Test\u3092\u4f7f\u3046\u3002<\/p>\n\n\n\n
    with<\/span>(<\/span>lung1,<\/span> fisher.test<\/span>(<\/span>ph.ecog,<\/span> sex))<\/span>\n<\/code><\/pre>\n\n\n\n
    \u7d50\u679c\u306f\u3001p = 0.7059 \u3067\u7d71\u8a08\u5b66\u7684\u6709\u610f\u3067\u306f\u306a\u3044\u3002<\/p>\n\n\n\n
    \u95a2\u9023\u3042\u308b\u3068\u306f\u8a00\u3048\u306a\u3044\u3002<\/p>\n\n\n\n
    ><\/span> with<\/span>(<\/span>lung1,<\/span> fisher.test<\/span>(<\/span>ph.ecog,<\/span> sex))<\/span>\nFisher's Exact Test for Count Data<\/span>\ndata:  ph.ecog and sex<\/span>\np-value = 0.7059<\/span>\nalternative hypothesis: two.sided<\/span>\n<\/code><\/pre>\n\n\n\n
    \u6b21\u306b\u3001\u5e74\u9f62\u3068\u306e\u95a2\u9023\u3082\u898b\u3066\u304a\u304f\u3002<\/p>\n\n\n\n
    \u5e74\u9f62\u306f\u9023\u7d9a\u91cf\u3068\u8003\u3048\u3066\u3001\u4e00\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3067\u691c\u8a0e\u3059\u308b\u3053\u3068\u306b\u3059\u308b\u3002<\/p>\n\n\n\n
    lm()\u3068anova()\u3092\u4f7f\u3046\u3002<\/p>\n\n\n\n
    \u5206\u6563\u5206\u6790\u306b\u3064\u3044\u3066\u8a73\u3057\u304f\u306f\u3053\u3061\u3089\u3092\u53c2\u7167\u3002<\/p>\n\n\n
    \t\t\t
    \n\t\t\t\t
    \n\t\t\t\t\t\u3042\u308f\u305b\u3066\u8aad\u307f\u305f\u3044<\/span>\n\t\t\t\t\t
    \"\"<\/figure><\/div>\t\t\t\t\t
    \n\t\t\t\t\t\tR \u3067\u4e00\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092\u884c\u3046\u65b9\u6cd5<\/a>\n\t\t\t\t\t\tR\u3067\u3001\u4e00\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\u3092 step by step \u3067\u8a08\u7b97\u3057\u3066\u307f\u305f\u3002 lm() \u3068 Anova() \u3092\u4f7f\u3048\u3070\u3042\u3063\u3068\u3044\u3046\u9593\u3060\u304c\u3001\u5177\u4f53\u7684\u306a\u4e00\u3064\u4e00\u3064\u306e\u8a08\u7b97\u3092\u81ea\u5206\u3067\u7d44\u307f\u7acb\u3066\u3066\u307f\u308b\u3068\u3069\u3046\u304b\uff1f \u6559\u79d1\u66f8…<\/span>\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n\n\n
    lm1 <-<\/span> lm<\/span>(<\/span>age ~<\/span> factor<\/span>(<\/span>ph.ecog),<\/span> data=<\/span>lung1)<\/span>\nanova<\/span>(<\/span>lm1)<\/span>\n<\/code><\/pre>\n\n\n\n
    \u7d50\u679c\u306f\u3001ECOG performance score\u9593\u3067\u5e74\u9f62\u306e\u5206\u5e03\u306f\u7d71\u8a08\u5b66\u7684\u6709\u610f\u306b\u7570\u306a\u308b\u3053\u3068\u304c\u308f\u304b\u3063\u305f\u3002<\/p>\n\n\n\n
    \u3053\u308c\u306f\u3001\u591a\u5909\u91cf\u30e2\u30c7\u30eb\u3067\u8abf\u6574\u3059\u308b\u306e\u304c\u671b\u307e\u3057\u3044\u3002<\/p>\n\n\n\n
    \u305f\u3060\u3057\u3001\u4eca\u56de\u306e\u30c7\u30fc\u30bf\u306f\u5e74\u9f62\u304c\u30a8\u30f3\u30c9\u30dd\u30a4\u30f3\u30c8\u306b\u3042\u307e\u308a\u95a2\u9023\u3057\u3066\u3044\u306a\u3044\u306e\u3067\u3001\u305d\u308c\u307b\u3069\u5927\u304d\u306a\u610f\u5473\u5408\u3044\u306f\u306a\u3044\u304b\u3082\u3057\u308c\u306a\u3044\u3002<\/p>\n\n\n\n
    ><\/span> anova<\/span>(<\/span>lm1)<\/span>\nAnalysis of Variance Table\nResponse:<\/span> age\nDf  Sum Sq Mean Sq F<\/span> value   Pr<\/span>(<\/span>><\/span>F<\/span>)<\/span>\nfactor<\/span>(<\/span>ph.ecog)<\/span>   2<\/span>   928.2<\/span>  464.12<\/span>  5.8484<\/span> 0.003345<\/span> **<\/span>\nResiduals       223<\/span> 17697.0<\/span>   79.36<\/span>\n---<\/span>\nSignif. codes:<\/span>  0<\/span> \u2018***<\/span>\u2019 0.001<\/span> \u2018**<\/span>\u2019 0.01<\/span> \u2018*<\/span>\u2019 0.05<\/span> \u2018.\u2019 0.1<\/span> \u2018 \u2019\n<\/code><\/pre>\n\n\n\n
    ECOG performance score\u5225\u306e\u751f\u5b58\u6642\u9593\u3092\u691c\u8a0e\u3059\u308b\u306e\u306b\u3001\u6027\u5225\u3068\u5e74\u9f62\u306f\u5fc5\u305a\u3057\u3082\u5927\u304d\u306a\u5f71\u97ff\u3092\u6301\u3063\u3066\u3044\u306a\u3044\u304b\u3082\u3057\u308c\u306a\u3044\u3068\u3044\u3046\u7d50\u679c\u3067\u3042\u308b\u304c\u3001\u4eca\u56de\u306f\u3053\u306e\u307e\u307e\u591a\u5909\u91cf\u8abf\u6574\u3092\u3059\u308b\u65b9\u5411\u3067\u9032\u3093\u3067\u307f\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>
    R \u3067 coxph \u3092\u4f7f\u3063\u3066 Cox\u6bd4\u4f8b\u30cf\u30b6\u30fc\u30c9\u30e2\u30c7\u30eb\u3092\u5b9f\u969b\u306b\u9069\u7528\u3057\u3066\u307f\u308b<\/h2>\n\n\n\n
    R \u3067Cox\u306e\u6bd4\u4f8b\u30cf\u30b6\u30fc\u30c9\u30e2\u30c7\u30eb\u3092\u5b9f\u65bd\u3059\u308b\u306b\u306f survival \u30d1\u30c3\u30b1\u30fc\u30b8\u306e coxph() \u3068\u3044\u3046\u95a2\u6570\u3092\u4f7f\u3046\u3002<\/p>\n\n\n\n
    coxph() \u306e\u30ab\u30c3\u30b3\u5185\u306fsurvfit()\u3084survdiff()\u306b\u3088\u304f\u4f3c\u3066\u3044\u308b\u3002<\/p>\n\n\n\n
    \u307e\u305f\u3001~\uff08\u30c1\u30eb\u30c0\uff09\u3088\u308a\u3082\u53f3\u5074\u306f\u3001lm()\u3084glm()\u3068\u66f8\u304d\u65b9\u304c\u305d\u3063\u304f\u308a\u3060\u3002<\/p>\n\n\n\n
    ECOG performance score\u3060\u3051\u3067\u9069\u7528\u3057\u3066\u307f\u308b<\/h3>\n\n\n\n
    coxph1 <-<\/span> coxph<\/span>(<\/span>Surv<\/span>(<\/span>time,<\/span>status)<\/span>~<\/span>factor<\/span>(<\/span>ph.ecog),<\/span>data=<\/span>lung1)<\/span>\nsummary<\/span>(<\/span>coxph1)<\/span>\n<\/code><\/pre>\n\n\n\n
    summary() \u3067\u7d50\u679c\u3092\u898b\u3066\u307f\u308b\u3068\u3001ECOG performance score\u304c\uff12\u306e\u5834\u5408\u304c\u3001\uff10\u306b\u6bd4\u3079\u3066\u3001\u7d71\u8a08\u5b66\u7684\u6709\u610f\u306b\u30cf\u30b6\u30fc\u30c9\u6bd4\uff08Hazard ratio\uff09\u304c\u9ad8\u3044\u3053\u3068\u304c\u308f\u304b\u308b\u3002<\/p>\n\n\n\n
    exp(coef)\u306e\u6b04\u3092\u898b\u308b\u3002<\/p>\n\n\n\n
    \u63a8\u5b9a\u5024\u306895%\u4fe1\u983c\u533a\u9593\u306f\u30012.498 (1.6083-3.878)\u3067\u3042\u308b\u3002<\/p>\n\n\n\n
    \uff10\u306b\u6bd4\u3079\u3066\u3001\uff12\u306e\u75c7\u4f8b\u306f2.5\u500d\u30a4\u30d9\u30f3\u30c8\u304c\u8d77\u304d\u3084\u3059\u304b\u3063\u305f\u3068\u8a00\u3048\u308b\u3002<\/p>\n\n\n\n
    ><\/span> summary<\/span>(<\/span>coxph1)<\/span>\nCall:<\/span>\ncoxph<\/span>(<\/span>formula =<\/span> Surv<\/span>(<\/span>time,<\/span> status)<\/span> ~<\/span> factor<\/span>(<\/span>ph.ecog),<\/span> data =<\/span> lung1)<\/span>\nn=<\/span> 226<\/span>,<\/span> number of events=<\/span> 163<\/span>\n(<\/span>1<\/span> observation deleted due to missingness)<\/span>\ncoef exp<\/span>(<\/span>coef)<\/span> se<\/span>(<\/span>coef)<\/span>     z Pr<\/span>(<\/span>>|<\/span>z|<\/span>)<\/span>\nfactor<\/span>(<\/span>ph.ecog)<\/span>1<\/span> 0.3686<\/span>    1.4458<\/span>   0.1987<\/span> 1.856<\/span>   0.0635<\/span> .\nfactor<\/span>(<\/span>ph.ecog)<\/span>2<\/span> 0.9153<\/span>    2.4975<\/span>   0.2246<\/span> 4.076<\/span> 4.58e-05<\/span> ***<\/span>\n---<\/span>\nSignif. codes:<\/span>  0<\/span> \u2018***<\/span>\u2019 0.001<\/span> \u2018**<\/span>\u2019 0.01<\/span> \u2018*<\/span>\u2019 0.05<\/span> \u2018.\u2019 0.1<\/span> \u2018 \u2019 1<\/span>\nexp<\/span>(<\/span>coef)<\/span> exp<\/span>(<\/span>-<\/span>coef)<\/span> lower .95<\/span> upper .95<\/span>\nfactor<\/span>(<\/span>ph.ecog)<\/span>1<\/span>     1.446<\/span>     0.6917<\/span>    0.9795<\/span>     2.134<\/span>\nfactor<\/span>(<\/span>ph.ecog)<\/span>2<\/span>     2.498<\/span>     0.4004<\/span>    1.6083<\/span>     3.878<\/span>\nConcordance=<\/span> 0.602  <\/span>(<\/span>se =<\/span> 0.024<\/span> )<\/span>\nLikelihood ratio test=<\/span> 16.53<\/span>  on 2<\/span> df,<\/span>   p=<\/span>3e-04<\/span>\nWald test            =<\/span> 17.28<\/span>  on 2<\/span> df,<\/span>   p=<\/span>2e-04<\/span>\nScore <\/span>(<\/span>logrank)<\/span> test =<\/span> 18.04<\/span>  on 2<\/span> df,<\/span>   p=<\/span>1e-04<\/span>\n<\/code><\/pre>\n\n\n\n
    ECOG performance score\u306b\u52a0\u3048\u3066\u6027\u5225\u3068\u5e74\u9f62\u3092\u6295\u5165\u3057\u3066\u307f\u308b<\/h3>\n\n\n\n
    coxph2 <-<\/span> coxph<\/span>(<\/span>Surv<\/span>(<\/span>time,<\/span>status)<\/span>~<\/span>factor<\/span>(<\/span>ph.ecog)<\/span>+<\/span>factor<\/span>(<\/span>sex)<\/span>+<\/span>age,<\/span>data=<\/span>lung1)<\/span>\nsummary<\/span>(<\/span>coxph2)<\/span>\n<\/code><\/pre>\n\n\n\n
    \u7d50\u679c\u3068\u3057\u3066\u3001ECOG performance score\u306f\u5148\u307b\u3069\u3068\u540c\u3058\u304f\uff10\u306b\u6bd4\u3079\u3066\u3001\uff12\u306e\u75c7\u4f8b\u306f\u7d042.5\u500d\u306e\u30cf\u30b6\u30fc\u30c9\u3067\u3042\u308b\u3068\u8a08\u7b97\u3055\u308c\u305f\u3002<\/p>\n\n\n\n
    ECOG performance score\u304c\uff11\u3067\u3042\u3063\u3066\u3082\u3001\uff10\u306b\u6bd4\u3079\u3066\u3001\u6709\u610f\u306b\u30ea\u30b9\u30af\u304c\u9ad8\u304b\u3063\u305f\uff08\u7d041.5\u500d\uff09\u3002<\/p>\n\n\n\n
    \u307e\u305f\u3001\u7537\u6027\u306b\u6bd4\u3079\u3001\u5973\u6027\u306e\u307b\u3046\u304c\u7d040.58\u500d\u306e\u30cf\u30b6\u30fc\u30c9\u3067\u3042\u308a\u3001\u7d0442%\u307b\u3069\u30ea\u30b9\u30af\u304c\u4f4e\u3044\u3053\u3068\u304c\u308f\u304b\u308b\u3002<\/p>\n\n\n\n
    ><\/span> summary<\/span>(<\/span>coxph2)<\/span>\nCall:<\/span>\ncoxph<\/span>(<\/span>formula =<\/span> Surv<\/span>(<\/span>time,<\/span> status)<\/span> ~<\/span> factor<\/span>(<\/span>ph.ecog)<\/span> +<\/span> factor<\/span>(<\/span>sex)<\/span> +<\/span>\nage,<\/span> data =<\/span> lung1)<\/span>\nn=<\/span> 226<\/span>,<\/span> number of events=<\/span> 163<\/span>\n(<\/span>1<\/span> observation deleted due to missingness)<\/span>\ncoef exp<\/span>(<\/span>coef)<\/span>  se<\/span>(<\/span>coef)<\/span>      z Pr<\/span>(<\/span>>|<\/span>z|<\/span>)<\/span>\nfactor<\/span>(<\/span>ph.ecog)<\/span>1<\/span>  0.409836<\/span>  1.506571<\/span>  0.199606<\/span>  2.053<\/span>  0.04005<\/span> *<\/span>\nfactor<\/span>(<\/span>ph.ecog)<\/span>2<\/span>  0.902321<\/span>  2.465318<\/span>  0.228092<\/span>  3.956<\/span> 7.62e-05<\/span> ***<\/span>\nfactor<\/span>(<\/span>sex)<\/span>2<\/span>     -<\/span>0.545631<\/span>  0.579476<\/span>  0.168229<\/span> -<\/span>3.243<\/span>  0.00118<\/span> **<\/span>\nage               0.010777<\/span>  1.010836<\/span>  0.009312<\/span>  1.157<\/span>  0.24713<\/span>\n---<\/span>\nSignif. codes:<\/span>  0<\/span> \u2018***<\/span>\u2019 0.001<\/span> \u2018**<\/span>\u2019 0.01<\/span> \u2018*<\/span>\u2019 0.05<\/span> \u2018.\u2019 0.1<\/span> \u2018 \u2019 1<\/span>\nexp<\/span>(<\/span>coef)<\/span> exp<\/span>(<\/span>-<\/span>coef)<\/span> lower .95<\/span> upper .95<\/span>\nfactor<\/span>(<\/span>ph.ecog)<\/span>1<\/span>    1.5066<\/span>     0.6638<\/span>    1.0188<\/span>    2.2279<\/span>\nfactor<\/span>(<\/span>ph.ecog)<\/span>2<\/span>    2.4653<\/span>     0.4056<\/span>    1.5766<\/span>    3.8550<\/span>\nfactor<\/span>(<\/span>sex)<\/span>2<\/span>        0.5795<\/span>     1.7257<\/span>    0.4167<\/span>    0.8058<\/span>\nage                 1.0108<\/span>     0.9893<\/span>    0.9926<\/span>    1.0295<\/span>\nConcordance=<\/span> 0.635  <\/span>(<\/span>se =<\/span> 0.025<\/span> )<\/span>\nLikelihood ratio test=<\/span> 28.94<\/span>  on 4<\/span> df,<\/span>   p=<\/span>8e-06<\/span>\nWald test            =<\/span> 28.6<\/span>  on 4<\/span> df,<\/span>   p=<\/span>9e-06<\/span>\nScore <\/span>(<\/span>logrank)<\/span> test =<\/span> 29.68<\/span>  on 4<\/span> df,<\/span>   p=<\/span>6e-06<\/span>\n<\/code><\/pre>\n\n\n\n
    \u7d71\u8a08\u5b66\u7684\u306a\u898f\u6e96\u306b\u3088\u308b\u5909\u6570\u9078\u629e\u3092\u53c2\u8003\u307e\u3067\u306b\u898b\u3066\u307f\u308b<\/h3>\n\n\n\n
    MASS \u30d1\u30c3\u30b1\u30fc\u30b8\u306e stepAIC() \u3068\u3044\u3046\u95a2\u6570\u306f\u3001AIC (Akaike’s Information Criterion) \u306e\u89b3\u70b9\u304b\u3089\u3001\u6700\u9069\u306a\u30e2\u30c7\u30eb\u3092\u9078\u3093\u3067\u304f\u308c\u308b\u95a2\u6570\u3002<\/p>\n\n\n\n
    AIC\u3068\u306f\uff1f<\/p>\n\n\n\n
    \u8d64\u6c60\u60c5\u5831\u91cf\u898f\u6e96 – Wikipedia<\/a><\/p>\n\n\n\n\n\n\n\n
    library<\/span>(<\/span>MASS)<\/span>\nstepAIC<\/span>(<\/span>coxph2)<\/span>\n<\/code><\/pre>\n\n\n\n
    \u5e74\u9f62\u306f\u7d71\u8a08\u5b66\u7684\u6709\u610f\u3067\u306a\u3044\u3053\u3068\u304c\u3053\u3061\u3089\u3067\u3082\u53cd\u6620\u3055\u308c\u3001\u5e74\u9f62\u3092\u9664\u3044\u305fECOG performance score\u3068\u6027\u5225\u3060\u3051\u306e\u30e2\u30c7\u30eb\u304c\u6700\u9069\u30e2\u30c7\u30eb\u3068\u3057\u3066\u9078\u3070\u308c\u305f\u3002<\/p>\n\n\n\n
    \u6700\u4e0b\u6bb5\u3092\u53c2\u7167\u3002<\/p>\n\n\n\n
    ><\/span> stepAIC<\/span>(<\/span>coxph2)<\/span>\nStart:<\/span>  AIC=<\/span>1457.19<\/span>\nSurv<\/span>(<\/span>time,<\/span> status)<\/span> ~<\/span> factor<\/span>(<\/span>ph.ecog)<\/span> +<\/span> factor<\/span>(<\/span>sex)<\/span> +<\/span> age\nDf    AIC\n-<\/span> age              1<\/span> 1456.5<\/span>\n<<\/span>none><\/span>               1457.2<\/span>\n-<\/span> factor<\/span>(<\/span>sex)<\/span>      1<\/span> 1466.3<\/span>\n-<\/span> factor<\/span>(<\/span>ph.ecog)<\/span>  2<\/span> 1468.8<\/span>\nStep:<\/span>  AIC=<\/span>1456.55<\/span>\nSurv<\/span>(<\/span>time,<\/span> status)<\/span> ~<\/span> factor<\/span>(<\/span>ph.ecog)<\/span> +<\/span> factor<\/span>(<\/span>sex)<\/span>\nDf    AIC\n<<\/span>none><\/span>               1456.5<\/span>\n-<\/span> factor<\/span>(<\/span>sex)<\/span>      1<\/span> 1465.6<\/span>\n-<\/span> factor<\/span>(<\/span>ph.ecog)<\/span>  2<\/span> 1470.1<\/span>\nCall:<\/span>\ncoxph<\/span>(<\/span>formula =<\/span> Surv<\/span>(<\/span>time,<\/span> status)<\/span> ~<\/span> factor<\/span>(<\/span>ph.ecog)<\/span> +<\/span> factor<\/span>(<\/span>sex),<\/span>\ndata =<\/span> lung1)<\/span>\ncoef exp<\/span>(<\/span>coef)<\/span> se<\/span>(<\/span>coef)<\/span>      z        p\nfactor<\/span>(<\/span>ph.ecog)<\/span>1<\/span>  0.4180<\/span>    1.5189<\/span>   0.1995<\/span>  2.096<\/span>   0.0361<\/span>\nfactor<\/span>(<\/span>ph.ecog)<\/span>2<\/span>  0.9464<\/span>    2.5765<\/span>   0.2248<\/span>  4.211<\/span> 2.54e-05<\/span>\nfactor<\/span>(<\/span>sex)<\/span>2<\/span>     -<\/span>0.5447<\/span>    0.5800<\/span>   0.1681<\/span> -<\/span>3.240<\/span>   0.0012<\/span>\nLikelihood ratio test=<\/span>27.58<\/span>  on 3<\/span> df,<\/span> p=<\/span>4.451e-06<\/span>\nn=<\/span> 226<\/span>,<\/span> number of events=<\/span> 163<\/span>\n(<\/span>1<\/span> observation deleted due to missingness)<\/span>\n<\/code><\/pre>\n\n\n\n
    \u3053\u306e\u7d71\u8a08\u5b66\u7684\u306a\u898f\u6e96\u306b\u3088\u308b\u5909\u6570\u9078\u629e\u306e\u7d50\u679c\u3082\u8e0f\u307e\u3048\u3066\u3001\u6700\u7d42\u30e2\u30c7\u30eb\u306f\u7814\u7a76\u8005\u304c\u6c7a\u3081\u308b\u3002<\/p>\n\n\n\n
    \u307e\u3068\u3081<\/h2>\n\n\n\n
    Cox\u306e\u6bd4\u4f8b\u30cf\u30b6\u30fc\u30c9\u30e2\u30c7\u30eb\u3092 R \u3067\u5b9f\u884c\u3057\u3066\u307f\u305f\u3002<\/p>\n\n\n\n
    \u89e3\u6790\u306e\u6e96\u5099\u6bb5\u968e\u3068\u3057\u3066\u3001survfit() \u3067\u30ab\u30d7\u30e9\u30f3\u30de\u30a4\u30e4\u30fc\u66f2\u7dda\u3092\u63cf\u3044\u305f\u308a\u3001survdiff() \u3067\u30ed\u30b0\u30e9\u30f3\u30af\u691c\u5b9a\u3092\u884c\u3046\u5fc5\u8981\u304c\u3042\u308b\u3002<\/p>\n\n\n\n
    \u307e\u305f\u3001\u4ea4\u7d61\u8981\u56e0\u306e\u691c\u8a0e\u3068\u3057\u3066\u3001\u30ab\u30c6\u30b4\u30ea\u306a\u3089Fisher Exact Test\u3001\u9023\u7d9a\u91cf\u3067\u4e09\u7fa4\u4ee5\u4e0a\u306a\u3089\u4e00\u5143\u914d\u7f6e\u5206\u6563\u5206\u6790\uff08\u4e8c\u7fa4\u306a\u3089\u5e73\u5747\u5024\u306e\u5dee\u306e\u691c\u5b9a\uff09\u3067\u691c\u8a0e\u3059\u308b\u3002<\/p>\n\n\n\n
    \u6700\u7d42\u7684\u306b\u306f coxph() \u95a2\u6570\u3092\u4f7f\u3063\u3066\u591a\u5909\u91cf\u89e3\u6790\u3092\u3059\u308b\u3002<\/p>\n\n\n\n
    stepAIC() \u3067\u6700\u7d42\u7684\u306a\u5909\u6570\u9078\u629e\u306e\u53c2\u8003\u3068\u3059\u308b\u7d50\u679c\u3092\u5f97\u308b\u3053\u3068\u304c\u3067\u304d\u308b\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
    Cox\u306e\u6bd4\u4f8b\u30cf\u30b6\u30fc\u30c9\u30e2\u30c7\u30eb\uff08\u30b3\u30c3\u30af\u30b9\u306e\u6bd4\u4f8b\u30cf\u30b6\u30fc\u30c9\u30e2\u30c7\u30eb\u3001Cox\u56de\u5e30\u3001\u30b3\u30c3\u30af\u30b9\u56de\u5e30\u306a\u3069\u8868\u793a\u30fb\u547c\u3073\u540d\u306f\u305f\u304f\u3055\u3093\u3042\u308b\u304c\u7686\u540c\u3058\u3082\u306e\u3092\u6307\u3057\u3066\u3044\u308b\uff09\u306f\u3001\u751f\u5b58\u6642\u9593\u3068\u30a4\u30d9\u30f3\u30c8\u30c7\u30fc\u30bf\u3092\u591a\u5909\u91cf\u89e3\u6790\u3067\u304d\u308b\u7d71\u8a08\u30e2\u30c7\u30eb\u3060\u3002 \u6ce8\u76ee\u3057\u305f\u3044\u8981\u56e0\u304c\u3001\u4ed6 […]<\/p>\n","protected":false},"author":2,"featured_media":2470,"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":[5,21],"tags":[],"class_list":["post-432","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-r","category-21"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"https:\/\/best-biostatistics.com\/toukei-er\/wp-content\/uploads\/2019\/08\/20190806215943.png","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/posts\/432","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=432"}],"version-history":[{"count":1,"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/posts\/432\/revisions"}],"predecessor-version":[{"id":2473,"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/posts\/432\/revisions\/2473"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/media\/2470"}],"wp:attachment":[{"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/media?parent=432"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/categories?post=432"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/best-biostatistics.com\/toukei-er\/wp-json\/wp\/v2\/tags?post=432"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}