mice()可以处理crr()吗?细灰色模型 [英] can mice() handle crr()? Fine-Gray model
问题描述
我的疑问是，是否有可能在来自"crr()"的Fine-Gray的拟合模型上合并来自"mice()"的多个插补数据集，并且在统计上是正确的. ..
My doubt is if it is possible to pool multiple imputation data set, from "mice()", on a fit model of Fine-Gray from "crr()", and if it is statistically correct...
library(survival)
library(mice)
library(cmprsk)
test1 <- as.data.frame(list(time=c(4,3,1,1,2,2,3,5,2,4,5,1, 4,3,1,1,2,2,3,5,2,4,5,1),
status=c(1,1,1,0,2,2,0,0,1,1,2,0, 1,1,1,0,2,2,0,0,1,1,2,0),
x=c(0,2,1,1,NA,NA,0,1,1,2,0,1, 0,2,1,1,NA,NA,0,1,1,2,0,1),
sex=c(0,0,0,NA,1,1,1,1,NA,1,0,0, 0,0,0,NA,1,1,1,1,NA,1,0,0)))
dat <- mice(test1,m=10, seed=1982)
#Cox regression: cause 1
models.cox1 <- with(dat,coxph(Surv(time, status==1) ~ x +sex ))
summary(pool(models.cox1))
#Cox regression: cause 1 or 2
models.cox <- with(dat,coxph(Surv(time, status==1 | status==2) ~ x +sex ))
models.cox
summary(pool(models.cox))
#### crr()
#Fine-Gray model
models.FG<- with(dat,crr(ftime=time, fstatus=status, cov1=test1[,c( "x","sex")], failcode=1, cencode=0, variance=TRUE))
summary(pool(models.FG))
#Error in pool(models.FG) : Object has no vcov() method.
models.FG
推荐答案
要使其正常工作，需要做几件事.
There are a couple of things that need to be done to get this to work.
您的初始数据和估算值.
Your initial data and imputation.
library(survival)
library(mice)
library(cmprsk)
test1 <- as.data.frame(list(time=c(4,3,1,1,2,2,3,5,2,4,5,1, 4,3,1,1,2,2,3,5,2,4,5,1),
status=c(1,1,1,0,2,2,0,0,1,1,2,0, 1,1,1,0,2,2,0,0,1,1,2,0),
x=c(0,2,1,1,NA,NA,0,1,1,2,0,1, 0,2,1,1,NA,NA,0,1,1,2,0,1),
sex=c(0,0,0,NA,1,1,1,1,NA,1,0,0, 0,0,0,NA,1,1,1,1,NA,1,0,0)))
dat <- mice(test1,m=10, print=FALSE)
对于mice所需的crr模型没有vcov方法，但是，
我们可以使用model$var返回值访问协方差矩阵.
There is no vcov method for crr models which mice requires, however,
we can access the covariance matrix using the model$var returned value.
因此编写自己的vcov方法进行提取，并且还需要一个coef方法.
So write own vcov method to extract, and also need a coef method.
vcov.crr <- function(object, ...) object$var # or getS3method('vcov','coxph')
coef.crr <- function(object, ...) object$coef
将模型传递给with.mids的方式也有一个错误:您的代码具有cov1=test1[,c( "x","sex")]，但实际上您希望cov1使用估算的数据.由于cov1需要具有相关变量的矩阵，因此我不确定如何正确地将其写为表达式，但是您可以轻松地对函数进行硬编码.
There is also an error in how the model is passed to with.mids: your code has cov1=test1[,c( "x","sex")], but really you want cov1 to use the imputed data. I am not sure how to correctly write this as an expression due to the cov1 requiring a matrix with relevant variables, but you can easily hard code a function.
# This function comes from mice:::with.mids
Andreus_with <-
function (data, ...) {
call <- match.call()
if (!is.mids(data))
stop("The data must have class mids")
analyses <- as.list(1:data$m)
for (i in 1:data$m) {
data.i <- complete(data, i)
analyses[[i]] <- crr(ftime=data.i[,'time'], fstatus=data.i[,'status'],
cov1=data.i[,c( "x","sex")],
failcode=1, cencode=0, variance=TRUE)
}
object <- list(call = call, call1 = data$call, nmis = data$nmis,
analyses = analyses)
oldClass(object) <- c("mira", "matrix")
return(object)
}
自此回答以来，mice内部结构已更改；现在，它使用broom包从拟合的crr模型中提取元素.因此，用于crr模型的tidy和glance方法是必需的:
The mice internals have changed since this answer; it now uses the broom package to extract elements from the fitted crr model. So tidy and glance methods for crr models are required:
tidy.crr <- function(x, ...) {
co = coef(x)
data.frame(term = names(co),
estimate = unname(co),
std.error=sqrt(diag(x$var)),
stringsAsFactors = FALSE)
}
glance.crr <- function(x, ...){ }
上面的代码然后允许将数据合并.
The above code then allows the data to be pooled.
models.FG <- Andreus_with(dat)
summary(pool(models.FG))
请注意，这会在未定义df.residual时发出警告，因此假定为大样本.我对crr不熟悉，因此也许可以提取出更明智的值-然后将其添加到tidy方法中. (鼠标版本"3.6.0")
Note that this gives warnings over df.residual not being defined, and so large samples are assumed. I'm not familiar with crr so a more sensible value can perhaps be extracted -- this would then be added to the tidy method. (mice version ‘3.6.0’)
这篇关于mice()可以处理crr()吗?细灰色模型的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
