对于当前项目，我正试图使用从Cox比例风险模型中获得的风险来计算人群可归因风险。包AF中有一个函数专门执行此操作(链接(。然而，当我尝试运行代码时，我得到一个错误，上面写着Error in[.data.frame(data, , eventvar) : undefined columns selected，我不知道是什么原因导致了错误。
一些示例代码：
# Load packages
library(dplyr)
library(magrittr)
library(survival)
library(AF)
# Get data
mydata <- structure(list(id = c(7971001, 3098, 1314, 5178001, 756001, 6787002,
693, 2839001, 1186, 5897002, 6761002, 2839002, 3606001, 4530001,
3094001, 6902001, 489001, 2010, 3451, 4526002, 854001, 1942,
678, 3327, 8381001, 443002, 2920001, 5302001, 6413002, 3645001,
830, 8776001, 7289001, 1198, 3307003, 1159, 5014002, 1727001,
756, 1454, 3198002, 469001, 3823001, 2959001, 3472, 6555002,
3091002, 1047, 2060, 7759001, 906002, 5826002, 6745001, 592001,
3136, 5784001, 1194001, 335001, 2376, 2895, 1627001, 5565002,
1862, 3429, 3425, 5978001, 651, 7833001, 37, 1702, 266, 3282001,
336, 2675001, 804001), exposure = c(1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0,
1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1), event = c(0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0), time = c(12.7748117727584,
2.08350444900753, 14.8774811772758, 2.06981519507187, 11.0581793292266,
15.4661190965092, 4.90349075975359, 4.67898699520876, 8.4435318275154,
14.1409993155373, 14.1464750171116, 14.4394250513347, 15.6632443531828,
13.2265571526352, 14.839151266256, 9.60164271047228, 11.1567419575633,
14.8692676249144, 14.9322381930185, 5.87268993839836, 14.3928815879535,
14.2012320328542, 10.2724161533196, 13.6317590691307, 13.4401095140315,
12.2929500342231, 5.70841889117043, 14.2368240930869, 14.6858316221766,
15.8083504449008, 14.6255989048597, 15.7015742642026, 8.90349075975359,
15.0609171800137, 4.54483230663929, 1.2703627652293, 9.36892539356605,
10.258726899384, 10.6721423682409, 11.6714579055441, 13.1772758384668,
15.813826146475, 10.8911704312115, 2.51060917180014, 14.5872689938398,
12.5147159479808, 14.1656399726215, 9.18275154004107, 14.2614647501711,
5.8425735797399, 12.2108145106092, 15.9808350444901, 14.3518138261465,
9.29226557152635, 14.1464750171116, 10.113620807666, 7.37850787132101,
9.10061601642711, 14.3326488706366, 11.2689938398357, 13.1060917180014,
4.61875427789186, 8.72005475701574, 14.031485284052, 13.9000684462697,
8.65982203969884, 14.5872689938398, 2.18480492813142, 9.79603011635866,
3.40041067761807, 3.35112936344969, 0.454483230663929, 5.39082819986311,
13.5578370978782, 14.9650924024641)), row.names = c(NA, -75L), class = "data.frame")
# Fit a Cox model
cox_model <- coxph(formula=Surv(time=time, event=event, type="right") ~ 1 + exposure, data=mydata, ties="breslow")
# Calculate PAR
par_model <- AFcoxph(cox_model, data=mydata, exposure ="exposure") # Gives error
par_model <- AFcoxph(cox_model, data=mydata, exposure ="exposure", times="time") # Gives error
par_model <- AFcoxph(cox_model, data=mydata, exposure ="exposure", clusterid="id") # Gives error
par_model <- AFcoxph(cox_model, data=mydata, exposure ="exposure", times="time", clusterid="id") # Gives error
有人知道是什么导致了这个错误吗？
确保您的R版本是最新的。你可以在这里下载最新版本。
> sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 22000)
##snip##
other attached packages:
[1] AF_0.1.5 survival_3.2-13
你的代码中似乎没有任何缺陷，因为我不明白你描述的错误。
library(survival)
# install.packages('AF')
library(AF)
cox_model <- coxph(Surv(time, event) ~ exposure, data = mydata, ties="breslow")
par_model <- AFcoxph(cox_model, data = mydata, exposure ="exposure")
par_model_cluster <- AFcoxph(cox_model, data=mydata,
exposure ="exposure", clusterid="id")
# ------------------------------------------------------------------
> identical(par_model, par_model_cluster)
[1] TRUE
# ---------------------------------------
> par_model
Estimated attributable fraction (AF) and standard error :
Time AF Std.Error
4.544832 0.5875401 0.3919930
4.678987 0.5854788 0.3923111
8.659822 0.5830906 0.3926216
9.182752 0.5804960 0.3929152
9.601643 0.5777211 0.3931865
10.258727 0.5747394 0.3934271
12.292950 0.5710542 0.3937199
12.514716 0.5672505 0.3939801
13.106092 0.5631900 0.3981882
13.177276 0.5590750 0.3984770
13.226557 0.5548138 0.3987241
14.439425 0.5467442 0.3994595