并行比较多个AUC（R）

debugcn 发表于 Dev

牛津大学

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（下面的示例具有可仿真的可复制数据集）：

#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！

谢谢。

敏捷

以下应该可以工作，请检查一下。我没有写所有细节，但是如果您不理解代码，可以问我其他问题。

#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，我们将生成这三个测试的所有可能组合的列表，并roc.test使用您设置的相同参数来运行该函数。

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元素列表（即，一次取2个n个元素的组合数），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：

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

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：

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

这就是结果。

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。

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}

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