读取样本数据
D=D[!is.na(apply(D,1,mean)),] ; dim(D)
## [1] 416 7查询部分数据（结果和预测因子）
head(D)
## time status age albumin edema protime bili ## 1 400 1 58.76523 2.60 1.0 12.2 14.5 ## 2 4500 0 56.44627 4.14 0.0 10.6 1.1 ## 3 1012 1 70.07255 3.48 0.5 12.0 1.4 ## 4 1925 1 54.74059 2.54 0.5 10.3 1.8 ## 5 1504 0 38.10541 3.53 0.0 10.9 3.4 ## 6 2503 1 66.25873 3.98 0.0 11.0 0.8模型0和模型1的结果数据和预测变量集
outcome=D[,c(1,2)] covs1<-as.matrix(D[,c(-1,-2)]) covs0<-as.matrix(D[,c(-1,-2, -7)]) head(outcome)
## time status ## 1 400 1 ## 2 4500 0 ## 3 1012 1 ## 4 1925 1 ## 5 1504 0 ## 6 2503 1
head(covs0)
## age albumin edema protime ## 1 58.76523 2.60 1.0 12.2 ## 2 56.44627 4.14 0.0 10.6 ## 3 70.07255 3.48 0.5 12.0 ## 4 54.74059 2.54 0.5 10.3 ## 5 38.10541 3.53 0.0 10.9 ## 6 66.25873 3.98 0.0 11.0
head(covs1)
## age albumin edema protime bili ## 1 58.76523 2.60 1.0 12.2 14.5 ## 2 56.44627 4.14 0.0 10.6 1.1 ## 3 70.07255 3.48 0.5 12.0 1.4 ## 4 54.74059 2.54 0.5 10.3 1.8 ## 5 38.10541 3.53 0.0 10.9 3.4 ## 6 66.25873 3.98 0.0 11.0 0.8推理
<span style="color:#333333"><span style="color:#333333"><code><span style="color:#000000">t0</span><span style="color:#687687">=</span><span style="color:#009999">365</span><span style="color:#687687">*</span><span style="color:#009999">5</span> <span style="color:#000000">x</span><span style="color:#687687"><-</span><span style="color:#000000">IDI </span><span style="color:#687687">(</span><span style="color:#000000">outcome</span>, <span style="color:#000000">covs0</span>, <span style="color:#000000">covs1</span>, <span style="color:#000000">t0</span>, <span style="color:#000000">npert</span><span style="color:#687687">=</span><span style="color:#009999">200</span><span style="color:#687687">)</span> ;</code></span></span>输出
## Est. Lower Upper p-value ## M1 0.090 0.052 0.119 0 ## M2 0.457 0.340 0.566 0 ## M3 0.041 0.025 0.062 0
M1表示IDI
M2表示NRI
M3表示中位数差异
图形演示
