PROSAGA码农传奇-大数据安全-如何结合R中Cox回归分析的风险比和置信区间

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  <P>
    根据@ shadow的评论，参数估计的CI基于整个数据集，如果您需要年龄条件CI，则需要对数据进行子集化。相反，如果您希望以一组协变量（包括年龄）为条件生成预期的生存曲线，那么您就是这样做的：
  </p>
   <pre>
    <code>
      # Create a dummy dataset
df <- data.frame(sex = sample(c(T,F),100,T),
                 age = 50 + rnorm(100)*10,
                 foo = sample(c('a','b','c'),100,T),
                 bar = sample(c('x','y','z'),100,T),
                 status = sample(c(T,F),100,T),
                 start = 0,# required for the survfit with `individual = TRUE`
                 time = -log(runif(100)))

# fit the A coxph model in your full dataset data
cox <- coxph(Surv(start,time, status) ~ sex + age + foo + bar, data=df)

# create a data.frame with all the variables used in the formula
newData <- data.frame(sex = T,
                 age = 55,
                 foo = sample(c('a','b','c'),1),
                 bar = sample(c('x','y','z'),1),
                 status = T,# required but unused
                 start = 0,# required but unused
                 time = 1)# required but unused
# get a prediction from the fitted model, specifiying 'individual = TRUE'
pred  <-  survfit(cox, newdata=data.frame(newData),individual =TRUE)

# plot the survival curves 
matplot(x = cbind(pred$"time"),
        y = cbind(pred$surv,
                  pred$upper,
                  pred$lower),
        type = 'l',
        lty= c(1,2,2),
        main = 'Predicted Survial with 95% CI')

</code>
  </pre>
  <P>
    你也可以去检查一下
     <code>
      unclass(pred)
    </code>
     和
     <code>
      summary(pred)
    </code>
    。
  </p>
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