我想使用tmerge()函数转换数据集，以便在重复事件的Cox回归框架的Andersen-Gill扩展中使用。见Therneau的excellent vignette。
我想指明个人在事件发生后30天内对重复事件免疫，即我希望个人暂时退出风险集，以便在个人不在以下情况时发生事件 - 风险，它被忽略了。
一种原始方法是迭代地添加所有事件，然后简单地将30添加到tstart变量。但是，这可能导致实例tstart >= tstop，并且在更大和更复杂的数据集中将是灾难性的。
我试图利用forloop来利用tmerge()函数来纠正我上面提到的问题。对于此示例，我将使用生存包中的cgd数据。
编辑：请参阅下面更正的forloop
library(survival)
cgd0 <- cgd0
newcgd <- tmerge(data1=cgd0[, 1:13], data2=cgd0, id=id, tstop=futime)
for(i in 1:7){
x <- paste0("etime", i) #etime1:etime7
# iteratively add each event
newcgd <- tmerge(newcgd, cgd0, id = id, infect = event(cgd0[,x]))
# select only observations that end in an event and iteratively create
# cumulative number of events for each individual
newcgd <- tmerge(newcgd, subset(newcgd, infect == 1),
id = id, cum_infect = cumtdc(tstop))
# for each loop add 30 days to the start time of the ith cumulative event
newcgd[which(newcgd$cum_infect == i), "tstart"] <-
newcgd[which(newcgd$cum_infect == i), "tstart"] + 30
# for each loop remove observations were the start time >= stop time
newcgd <- newcgd[which(newcgd$tstart < newcgd$tstop),]
}
attr(newcgd, "tcount")
# early late gap within boundary leading trailing tied
#infect 0 0 0 44 0 0 0 0
#cum_infect 0 0 0 0 44 0 0 0
#infect 0 0 4 11 0 1 1 0
#cum_infect 0 0 0 0 11 0 45 0
#infect 0 0 2 6 0 0 0 0
#cum_infect 0 0 0 0 6 0 56 0
#infect 0 0 1 2 0 0 0 0
#cum_infect 0 0 0 0 6 0 58 0
#infect 0 0 0 2 0 0 0 0
#cum_infect 0 0 0 0 8 0 58 0
#infect 0 0 0 1 0 0 0 0
#cum_infect 0 0 0 0 9 0 58 0
#infect 0 0 0 1 0 0 0 0
#cum_infect 0 0 0 0 10 0 58 0
我相信这个解决方案是正确的。然而，这是生存分析中的常见问题，我担心
i）我忽略了一些东西而且代码没有做我认为它做的事情。
ii）我在R
中忽略了一种经过验证的方法iii）如果i）和ii）不是问题，我相信这段代码效率低下，并且想知道是否有明显的方法可以提高执行速度。
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编辑：使用评论进一步进行错误检查。希望这能澄清我试图做的事情。从概念上;我指的是个人在经历一次事件后30天内不会有再次发生其他事件的风险。在Andersen-Gill计数过程公式中，每一行代表一个包含开始时间tstart和停止时间tstop以及指示符（在本例中为infect）的观察结果观察是否因事件infect == 1或审查infect == 0而结束。在这里，我手动完成上述forloop中的步骤，并量化每个循环发生的事件数量以及指定30天免疫期间的总跟踪时间。然后将相同的代码实现为forloop以实现完整性。结果显示在下面的单独代码块中。
newcgd <- tmerge(data1=cgd0[, 1:13], data2=cgd0, id=id, tstop=futime)
###1st event
x <- "etime1"
immunecgd <- tmerge(newcgd, cgd0, id = id, infect = event(cgd0[,x]))
immunecgd <- tmerge(immunecgd, subset(immunecgd, infect == 1), id = id, cum_infect = cumtdc(tstop))
immunecgd[which(immunecgd$cum_infect == 1), "tstart"] <- immunecgd[which(immunecgd$cum_infect == 1), "tstart"] + 30
immunecgd <- immunecgd[which(immunecgd$tstart < immunecgd$tstop),]
newcgd <- tmerge(newcgd, cgd0, id = id, infect = event(cgd0[,x]))
newcgd <- tmerge(newcgd, subset(newcgd, infect == 1), id = id, cum_infect = cumtdc(tstop))
newcgd[which(newcgd$cum_infect == 1), "tstart"] <- newcgd[which(newcgd$cum_infect == 1), "tstart"]
newcgd <- newcgd[which(newcgd$tstart < newcgd$tstop),]
etime1 <- c(sum(immunecgd$infect), sum(newcgd$infect))
futime1 <- c(sum(immunecgd$tstop - immunecgd$tstart), sum(newcgd$tstop - newcgd$tstart))
###2nd event
x <- "etime2"
immunecgd <- tmerge(immunecgd, cgd0, id = id, infect = event(cgd0[,x]))
immunecgd <- tmerge(immunecgd, subset(immunecgd, infect == 1), id = id, cum_infect = cumtdc(tstop))
immunecgd[which(immunecgd$cum_infect == 2), "tstart"] <- immunecgd[which(immunecgd$cum_infect == 2), "tstart"] + 30
immunecgd <- immunecgd[which(immunecgd$tstart < immunecgd$tstop),]
newcgd <- tmerge(newcgd, cgd0, id = id, infect = event(cgd0[,x]))
newcgd <- tmerge(newcgd, subset(newcgd, infect == 1), id = id, cum_infect = cumtdc(tstop))
newcgd[which(newcgd$cum_infect == 2), "tstart"] <- newcgd[which(newcgd$cum_infect == 2), "tstart"]
newcgd <- newcgd[which(newcgd$tstart < newcgd$tstop),]
etime2 <- c(sum(immunecgd$infect), sum(newcgd$infect))
futime2 <- c(sum(immunecgd$tstop - immunecgd$tstart), sum(newcgd$tstop - newcgd$tstart))
###3rd event
x <- "etime3"
immunecgd <- tmerge(immunecgd, cgd0, id = id, infect = event(cgd0[,x]))
immunecgd <- tmerge(immunecgd, subset(immunecgd, infect == 1), id = id, cum_infect = cumtdc(tstop))
immunecgd[which(immunecgd$cum_infect == 3), "tstart"] <- immunecgd[which(immunecgd$cum_infect == 3), "tstart"] + 30
immunecgd <- immunecgd[which(immunecgd$tstart < immunecgd$tstop),]
newcgd <- tmerge(newcgd, cgd0, id = id, infect = event(cgd0[,x]))
newcgd <- tmerge(newcgd, subset(newcgd, infect == 1), id = id, cum_infect = cumtdc(tstop))
newcgd[which(newcgd$cum_infect == 3), "tstart"] <- newcgd[which(newcgd$cum_infect == 3), "tstart"]
newcgd <- newcgd[which(newcgd$tstart < newcgd$tstop),]
etime3 <- c(sum(immunecgd$infect), sum(newcgd$infect))
futime3 <- c(sum(immunecgd$tstop - immunecgd$tstart), sum(newcgd$tstop - newcgd$tstart))
###4th event
x <- "etime4"
immunecgd <- tmerge(immunecgd, cgd0, id = id, infect = event(cgd0[,x]))
immunecgd <- tmerge(immunecgd, subset(immunecgd, infect == 1), id = id, cum_infect = cumtdc(tstop))
immunecgd[which(immunecgd$cum_infect == 4), "tstart"] <- immunecgd[which(immunecgd$cum_infect == 4), "tstart"] + 30
immunecgd <- immunecgd[which(immunecgd$tstart < immunecgd$tstop),]
newcgd <- tmerge(newcgd, cgd0, id = id, infect = event(cgd0[,x]))
newcgd <- tmerge(newcgd, subset(newcgd, infect == 1), id = id, cum_infect = cumtdc(tstop))
newcgd[which(newcgd$cum_infect == 4), "tstart"] <- newcgd[which(newcgd$cum_infect == 4), "tstart"]
newcgd <- newcgd[which(newcgd$tstart < newcgd$tstop),]
etime4 <- c(sum(immunecgd$infect), sum(newcgd$infect))
futime4 <- c(sum(immunecgd$tstop - immunecgd$tstart), sum(newcgd$tstop - newcgd$tstart))
###5th event
x <- "etime5"
immunecgd <- tmerge(immunecgd, cgd0, id = id, infect = event(cgd0[,x]))
immunecgd <- tmerge(immunecgd, subset(immunecgd, infect == 1), id = id, cum_infect = cumtdc(tstop))
immunecgd[which(immunecgd$cum_infect == 5), "tstart"] <- immunecgd[which(immunecgd$cum_infect == 5), "tstart"] + 30
immunecgd <- immunecgd[which(immunecgd$tstart < immunecgd$tstop),]
newcgd <- tmerge(newcgd, cgd0, id = id, infect = event(cgd0[,x]))
newcgd <- tmerge(newcgd, subset(newcgd, infect == 1), id = id, cum_infect = cumtdc(tstop))
newcgd[which(newcgd$cum_infect == 5), "tstart"] <- newcgd[which(newcgd$cum_infect == 5), "tstart"]
newcgd <- newcgd[which(newcgd$tstart < newcgd$tstop),]
etime5 <- c(sum(immunecgd$infect), sum(newcgd$infect))
futime5 <- c(sum(immunecgd$tstop - immunecgd$tstart), sum(newcgd$tstop - newcgd$tstart))
###6th event
x <- "etime6"
immunecgd <- tmerge(immunecgd, cgd0, id = id, infect = event(cgd0[,x]))
immunecgd <- tmerge(immunecgd, subset(immunecgd, infect == 1), id = id, cum_infect = cumtdc(tstop))
immunecgd[which(immunecgd$cum_infect == 6), "tstart"] <- immunecgd[which(immunecgd$cum_infect == 6), "tstart"] + 30
immunecgd <- immunecgd[which(immunecgd$tstart < immunecgd$tstop),]
newcgd <- tmerge(newcgd, cgd0, id = id, infect = event(cgd0[,x]))
newcgd <- tmerge(newcgd, subset(newcgd, infect == 1), id = id, cum_infect = cumtdc(tstop))
newcgd[which(newcgd$cum_infect == 6), "tstart"] <- newcgd[which(newcgd$cum_infect == 6), "tstart"]
newcgd <- newcgd[which(newcgd$tstart < newcgd$tstop),]
etime6 <- c(sum(immunecgd$infect), sum(newcgd$infect))
futime6 <- c(sum(immunecgd$tstop - immunecgd$tstart), sum(newcgd$tstop - newcgd$tstart))
###7th event
x <- "etime7"
immunecgd <- tmerge(immunecgd, cgd0, id = id, infect = event(cgd0[,x]))
immunecgd <- tmerge(immunecgd, subset(immunecgd, infect == 1), id = id, cum_infect = cumtdc(tstop))
immunecgd[which(immunecgd$cum_infect == 7), "tstart"] <- immunecgd[which(immunecgd$cum_infect == 7), "tstart"] + 30
immunecgd <- immunecgd[which(immunecgd$tstart < immunecgd$tstop),]
newcgd <- tmerge(newcgd, cgd0, id = id, infect = event(cgd0[,x]))
newcgd <- tmerge(newcgd, subset(newcgd, infect == 1), id = id, cum_infect = cumtdc(tstop))
newcgd[which(newcgd$cum_infect == 7), "tstart"] <- newcgd[which(newcgd$cum_infect == 7), "tstart"]
newcgd <- newcgd[which(newcgd$tstart < newcgd$tstop),]
etime7 <- c(sum(immunecgd$infect), sum(newcgd$infect))
futime7 <- c(sum(immunecgd$tstop - immunecgd$tstart), sum(newcgd$tstop - newcgd$tstart))
df_event <- rbind.data.frame(etime1, etime2, etime3, etime4, etime5, etime6, etime7)
colnames(df_event) <- c("immunity", "no_immunity")
df_event$diff <- df_event$no_immunity - df_event$immunity
df_futime <- rbind.data.frame(futime1, futime2, futime3, futime4, futime5, futime6, futime7)
colnames(df_futime) <- c("immunity", "no_immunity")
df_futime$diff <- df_futime$no_immunity - df_futime$immunity
与forloop相同的代码。
newcgd <- tmerge(data1=cgd0[, 1:13], data2=cgd0, id=id, tstop=futime)
immunecgd <- tmerge(data1=cgd0[, 1:13], data2=cgd0, id=id, tstop=futime)
event <- matrix(NA, nrow = 7, ncol = 2)
futime <- matrix(NA, nrow = 7, ncol = 2)
for(i in 1:7){
x <- paste0("etime", i) #etime1:etime7
# iteratively add each event
immunecgd <- tmerge(immunecgd, cgd0, id = id, infect = event(cgd0[,x]))
newcgd <- tmerge(newcgd, cgd0, id = id, infect = event(cgd0[,x]))
# select only observations that end in an event and iteratively create
# cumulative number of events for each individual
immunecgd <- tmerge(immunecgd, subset(immunecgd, infect == 1), id = id, cum_infect = cumtdc(tstop))
newcgd <- tmerge(newcgd, subset(newcgd, infect == 1), id = id, cum_infect = cumtdc(tstop))
# for each loop add 30 days to the start time of the ith cumulative event
immunecgd[which(immunecgd$cum_infect == i), "tstart"] <- immunecgd[which(immunecgd$cum_infect == i), "tstart"] + 30
newcgd[which(newcgd$cum_infect == i), "tstart"] <- newcgd[which(newcgd$cum_infect == i), "tstart"]
# for each loop remove observations were the start time >= stop time
immunecgd <- immunecgd[which(immunecgd$tstart < immunecgd$tstop),]
newcgd <- newcgd[which(newcgd$tstart < newcgd$tstop),]
event[i,] <- c(sum(immunecgd$infect), sum(newcgd$infect))
futime[i,] <- c(sum(immunecgd$tstop - immunecgd$tstart), sum(newcgd$tstop - newcgd$tstart))
}
event <- data.frame(event)
colnames(event) <- c("immunity", "no_immunity")
event$diff <- event$no_immunity - event$immunity
futime <- data.frame(futime)
colnames(futime) <- c("immunity", "no_immunity")
futime$diff <- futime$no_immunity - futime$immunity
上面的错误测试代码给出了以下结果
df_event
immunity no_immunity diff
1 44 44 0
2 56 61 5
3 62 69 7
4 64 72 8
5 66 74 8
6 67 75 8
7 68 76 8
df_futime
immunity no_immunity diff
1 36202 37477 1275
2 35935 37477 1542
3 35875 37477 1602
4 35875 37477 1602
5 35875 37477 1602
6 35875 37477 1602
7 35875 37477 1602
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通过对survival包中的不同数据集，模拟数据集和我自己的个人数据集（我希望使用此代码的数据集）进行进一步测试，我发现了一个“故障”。在上面的代码版本中，如果新事件etime[i-1]属于其中一个时期，那么我们已经指定了个人不具备事件 - 这正是代码旨在创建的实例 - 事件未纳入累积事件计数器cum_infect。在下一次运行etime[i]期间，个人将只有[i-1]个累积事件，并且控制是否应将30天添加到开始时间的代码部分
immunecgd[which(immunecgd$cum_infect == i), "tstart"] <- immunecgd[which(immunecgd$cum_infect == i), "tstart"] + 30
不会将个人识别为有事件。这意味着forloop只会在事件发生后正确添加30天的免疫力，直到事件的第一次发生在这样的免疫期。我提出了一个相当不优雅的解决方案。但它的确有效。
newcgd <- tmerge(data1=cgd0[, 1:13], data2=cgd0, id=id, tstop=futime)
immunecgd <- tmerge(data1=cgd0[, 1:13], data2=cgd0, id=id, tstop=futime)
newcgd$cum_infect_0 <- 0
immunecgd$cum_infect_0 <- 0
event <- matrix(NA, nrow = 7, ncol = 2)
futime <- matrix(NA, nrow = 7, ncol = 2)
for(i in 1:7){
x <- paste0("etime", i) #etime1:etime7
# iteratively add each event
immunecgd <- tmerge(immunecgd, cgd0, id = id, infect = event(cgd0[,x]))
newcgd <- tmerge(newcgd, cgd0, id = id, infect = event(cgd0[,x]))
# select only observations that end in an event and iteratively create
# cumulative number of events for each individual
immunecgd <- tmerge(immunecgd, subset(immunecgd, infect == 1), id = id, cum_infect = cumtdc(tstop))
newcgd <- tmerge(newcgd, subset(newcgd, infect == 1), id = id, cum_infect = cumtdc(tstop))
# create new column that will hold cumulative events between loops
immunecgd[, paste0("cum_infect_", i)] <- immunecgd[, "cum_infect"]
newcgd[, paste0("cum_infect_", i)] <- newcgd[, "cum_infect"]
# for each loop add 30 days to the start time if there is atleast one cumulative event
# and the value of the ith cumulative event is larger than the i-1th cumulative event
immunecgd[which(immunecgd$cum_infect > 0 & immunecgd$cum_infect > immunecgd[, paste0("cum_infect_", i - 1)]), "tstart"] <-
immunecgd[which(immunecgd$cum_infect > 0 & immunecgd$cum_infect > immunecgd[, paste0("cum_infect_", i - 1)]), "tstart"] + 30
newcgd[which(newcgd$cum_infect > 0 & newcgd$cum_infect > newcgd[, paste0("cum_infect_", i - 1)]), "tstart"] <-
newcgd[which(newcgd$cum_infect > 0 & newcgd$cum_infect > newcgd[, paste0("cum_infect_", i - 1)]), "tstart"]
# for each loop remove observations were the start time >= stop time
immunecgd <- immunecgd[which(immunecgd$tstart < immunecgd$tstop),]
newcgd <- newcgd[which(newcgd$tstart < newcgd$tstop),]
event[i,] <- c(sum(immunecgd$infect), sum(newcgd$infect))
futime[i,] <- c(sum(immunecgd$tstop - immunecgd$tstart), sum(newcgd$tstop - newcgd$tstart))
}
immunecgd <- immunecgd[,!grepl("cum_infect_", colnames(immunecgd))]
newcgd <- newcgd[,!grepl("cum_infect_", colnames(newcgd))]
event <- data.frame(event)
colnames(event) <- c("immunity", "no_immunity")
event$diff <- event$no_immunity - event$immunity
futime <- data.frame(futime)
colnames(futime) <- c("immunity", "no_immunity")
futime$diff <- futime$no_immunity - futime$immunity
在这里，我们可以看到事件总数的差异
immunity no_immunity diff
1 44 44 0
2 56 61 5
3 62 69 7
4 64 72 8
5 65 74 9
6 66 75 9
7 66 76 10
正确指定forloop已经发现另外2个实例是免疫期间发生的事件。
答案 0 :(得分：0)
跟进我的评论，这是我在尝试在代码中实现它时看到的内容：
with( newcgd, table( tstart-tstop <= 30, infect))
#-------------
infect
0 1
TRUE 120 68
所以，如果我理解你的目标，我不会认为你在那里，我想知道你是否搞砸了，因为：
> newcgd$infect <- with( newcgd,ifelse(infect, tstart-tstop > 30, 0 ) )
> with( newcgd, table( tstart-tstop <= 30, infect))
infect
0
TRUE 188
当我将所有短间隔事件设置为0时，我根本没有事件。但也许我还没有理解这些问题？