并行比较多个AUC(R) [英] Comparing multiple AUCs in parallel (R)

问题描述

我正在使用r中的pROC软件包来计算和比较多个测试的AUC，以查看哪个测试最能区分患者和对照组.但是，我有大量测试，并且本质上是想对每个测试AUC与其他每个测试进行一系列成对比较，然后针对多个比较进行校正.这是我所掌握的代码所能做到的(下面的示例具有可仿真和可复制的数据集):

I am using the pROC package in r to calculate and compare the AUCs of multiple tests, to see which test has the best ability to discriminate between patients and controls. However, I have a large number of tests and essentially want to run a series of pairwise comparisons of each tests AUC with every other test and then correct for multiple comparisons. This is as far as I've gotten with my code (example with simulated and replicable dataset below):

#load pROC
library(pROC)

#generate df with random numbers
set.seed(123)
df <- data.frame(disease_status = rbinom(n=100, size=1, prob=0.20),
                 test1 = rnorm(100, mean=15, sd=4),
                 test2 = rnorm(100, mean=30, sd=2),
                 test3 = rnorm(100, mean=50, sd=3))

#create roc object for test1, test2, test3
roc.out_test1<-roc(df$disease_status, df$test1, plot=TRUE, smooth = FALSE)
roc.out_test2<-roc(df$disease_status, df$test2, plot=TRUE, smooth = FALSE)
roc.out_test3<-roc(df$disease_status, df$test3, plot=TRUE, smooth = FALSE)

#compare the AUC of test1 and test 2
roc.test(roc.out_test1, roc.out_test2, reuse.auc=TRUE, method="delong", na.rm=TRUE)

#DeLong's test for two correlated ROC curves
#data:  roc.out_test1 and roc.out_test2
#Z = 0.60071, p-value = 0.548
#alternative hypothesis: true difference in AUC is not equal to 0
#sample estimates:
#AUC of roc1 AUC of roc2 
#0.5840108   0.5216802 

#create a function to do above for all comparisons
vec_ROCs1 <- c("roc.out_test1,", "roc.out_test2,", "roc.out_test3,")
vec_ROCs2 <- c("roc.out_test1", "roc.out_test2", "roc.out_test3")
ROCs2_specifications  <- paste0(vec_ROCs2, ",", "reuse.auc=TRUE")
test <- unlist(lapply(ROCs2_specifications, function(x) paste0(vec_ROCs1, x)))
test2 <- lapply(test, function(x) roc.test(x))

#Error in roc.test.default(x) : 
#  argument "predictor1" is missing, with no default 

请让我知道您对如何解决此问题的想法和建议！

Please let me know your thoughts and suggestions on how to fix this!

谢谢.

推荐答案

以下内容应该可以使用，请检查一下.我没有写所有的细节，但如果你不理解代码，你可以问我其他问题.

The following should work, please check it. I didn't write all the details, but you can ask me other questions if you don't understand the code.

#load pROC
library(pROC)
#> Type 'citation("pROC")' for a citation.
#> 
#> Attaching package: 'pROC'
#> The following objects are masked from 'package:stats':
#> 
#>     cov, smooth, var

#generate df with random numbers
set.seed(123)
df <- data.frame(disease_status = rbinom(n=100, size=1, prob=0.20),
                 test1 = rnorm(100, mean=15, sd=4),
                 test2 = rnorm(100, mean=30, sd=2),
                 test3 = rnorm(100, mean=50, sd=3))

#create roc object for test1, test2, test3
roc.out_test1<-roc(df$disease_status, df$test1, plot=TRUE, smooth = FALSE)
#> Setting levels: control = 0, case = 1
#> Setting direction: controls < cases

roc.out_test2<-roc(df$disease_status, df$test2, plot=TRUE, smooth = FALSE)
#> Setting levels: control = 0, case = 1
#> Setting direction: controls < cases

roc.out_test3<-roc(df$disease_status, df$test3, plot=TRUE, smooth = FALSE)
#> Setting levels: control = 0, case = 1
#> Setting direction: controls < cases

# compare the AUC of test1 and test 2
roc.test(roc.out_test1, roc.out_test2, reuse.auc = TRUE, method = "delong", na.rm = TRUE)
#> 
#>  DeLong's test for two correlated ROC curves
#> 
#> data:  roc.out_test1 and roc.out_test2
#> Z = 0.60071, p-value = 0.548
#> alternative hypothesis: true difference in AUC is not equal to 0
#> sample estimates:
#> AUC of roc1 AUC of roc2 
#>   0.5840108   0.5216802

现在，我们生成三个测试的所有可能组合的列表，并使用与您设置的相同的参数运行 roc.test 函数.

Now we generate a list of all possible combinations of the three tests and run the roc.test function using the same parameters that you set.

all_tests <- combn(
  list(
    "test1" = roc.out_test1,
    "test2" = roc.out_test2,
    "test3" = roc.out_test3
  ),
  FUN = function(x, ...) roc.test(x[[1]], x[[2]]),
  m = 2,
  simplify = FALSE, 
  reuse.auc = TRUE, 
  method = "delong", 
  na.rm = TRUE
)

输出是 choose(3，2)= 3 个元素的列表(即，一次取2个n个元素的组合数)，并且列表中的每个元素都是一个测试.例如，这与您之前的测试相同:

The output is a list of choose(3, 2) = 3 elements (i.e. the number of combinations of n elements taken 2 at a time) and each element of the list is a test. For example this is the same as your previous test:

all_tests[[1]]
#> 
#>  DeLong's test for two correlated ROC curves
#> 
#> data:  x[[1]] and x[[2]]
#> Z = 0.60071, p-value = 0.548
#> alternative hypothesis: true difference in AUC is not equal to 0
#> sample estimates:
#> AUC of roc1 AUC of roc2 
#>   0.5840108   0.5216802

这里唯一的问题是，很难识别比较中使用了哪些测试，因此我们还可以添加名称列表:

The only problem here is that it's difficult to recognise which tests are used in the comparisons, so we can also add a list of names:

tests_names <- combn(
  list("test1", "test2", "test3"), 
  m = 2, 
  FUN = paste, 
  simplify = TRUE, 
  collapse = "_"
)
all_tests <- setNames(all_tests, tests_names)

这是结果.

names(all_tests)
#> [1] "test1_test2" "test1_test3" "test2_test3"

对象的名称标记比较中使用的测试.

The names of the objects flag the tests that are used in the comparison.

all_tests$test1_test2
#> 
#>  DeLong's test for two correlated ROC curves
#> 
#> data:  x[[1]] and x[[2]]
#> Z = 0.60071, p-value = 0.548
#> alternative hypothesis: true difference in AUC is not equal to 0
#> sample estimates:
#> AUC of roc1 AUC of roc2 
#>   0.5840108   0.5216802

^{由 reprex软件包(v0.3.0)创建于2020-03-14 sup>}

^{Created on 2020-03-14 by the reprex package (v0.3.0)}

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