本文首发于公众号：医学和生信笔记

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医学和生信笔记，专注R语言在临床医学中的使用，R语言数据分析和可视化。主要分享R语言做医学统计学、meta分析、网络药理学、临床预测模型、机器学习、生物信息学等。

前面介绍了超多DCA的实现方法，基本上常见的方法都包括了，代码和数据获取方法也给了大家。

今天介绍的是如何实现其他模型的DCA，比如lasso回归、随机森林、决策树、SVM、xgboost等。

这是基于dca.r/stdca.r实现的一种通用方法，不过我在原本的代码上做了修改，原代码会在某些数据集报错。

• 多个模型多个时间点DCA数据提取并用 ggplot2画图
  
• lasso回归的DCA
  
• 随机森林的DCA
  

多个时间点多个cox模型的数据提取

其实ggDCA包完全可以做到，只要1行代码就搞定了，而且功能还很丰富。

我给大家演示一遍基于stdca.r的方法，给大家开阔思路，代码可能不够简洁，但是思路没问题，无非就是各种数据整理与转换。

而且很定会有人对默认结果不满意，想要各种修改，下面介绍的这个方法非常适合自己进行各种自定义！

rm(list = ls())
library(survival)
library(dcurves)
data("df_surv")# 加载函数
source("../000files/stdca.R") # 原函数有问题# 构建一个多元cox回归
df_surv$cancer <- as.numeric(df_surv$cancer) # stdca函数需要结果变量是0,1
df_surv <- as.data.frame(df_surv) # stdca函数只接受data.frame

# 建立多个模型
cox_fit1 <- coxph(Surv(ttcancer, cancer) ~ famhistory+marker, data = df_surv)
cox_fit2 <- coxph(Surv(ttcancer, cancer) ~ age + famhistory + marker, data = df_surv)
cox_fit3 <- coxph(Surv(ttcancer, cancer) ~ age + famhistory, data = df_surv)# 计算每个模型在不同时间点的概率
df_surv$prob11 <- c(1-(summary(survfit(cox_fit1, newdata=df_surv), times=1)$surv))
df_surv$prob21 <- c(1-(summary(survfit(cox_fit2, newdata=df_surv), times=1)$surv))
df_surv$prob31 <- c(1-(summary(survfit(cox_fit3, newdata=df_surv), times=1)$surv))df_surv$prob12 <- c(1-(summary(survfit(cox_fit1, newdata=df_surv), times=2)$surv))
df_surv$prob22 <- c(1-(summary(survfit(cox_fit2, newdata=df_surv), times=2)$surv))
df_surv$prob32 <- c(1-(summary(survfit(cox_fit3, newdata=df_surv), times=2)$surv))df_surv$prob13 <- c(1-(summary(survfit(cox_fit1, newdata=df_surv), times=3)$surv))
df_surv$prob23 <- c(1-(summary(survfit(cox_fit2, newdata=df_surv), times=3)$surv))
df_surv$prob33 <- c(1-(summary(survfit(cox_fit3, newdata=df_surv), times=3)$surv))

计算threshold和net benefit：

cox_dca1 <- stdca(data = df_surv, outcome = "cancer", ttoutcome = "ttcancer", timepoint = 1, predictors = c("prob11","prob21","prob31"),smooth=TRUE,graph = FALSE)cox_dca2 <- stdca(data = df_surv, outcome = "cancer", ttoutcome = "ttcancer", timepoint = 2, predictors = c("prob12","prob22","prob32"),smooth=TRUE,graph = FALSE)cox_dca3 <- stdca(data = df_surv, outcome = "cancer", ttoutcome = "ttcancer", timepoint = 3, predictors = c("prob13","prob23","prob33"),smooth=TRUE,graph = FALSE)library(tidyr)
library(dplyr)

第一种数据整理方法

cox_dca_df1 <- cox_dca1$net.benefit
cox_dca_df2 <- cox_dca2$net.benefit
cox_dca_df3 <- cox_dca3$net.benefitnames(cox_dca_df1)[2] <- "all1"
names(cox_dca_df2)[2] <- "all2"
names(cox_dca_df3)[2] <- "all3"tmp <- cox_dca_df1 %>% left_join(cox_dca_df2) %>% left_join(cox_dca_df3) %>% pivot_longer(cols = contains(c("all","sm","none")),names_to = "models",values_to = "net_benefit")

画图：

library(ggplot2)
library(ggsci)ggplot(tmp, aes(x=threshold,y=net_benefit))+geom_line(aes(color=models),size=1.2)+scale_x_continuous(labels = scales::label_percent(accuracy = 1),name="Threshold Probility")+scale_y_continuous(limits = c(-0.05,0.3),name="Net Benefit")+theme_bw(base_size = 14)

第二种数据整理方法

cox_dca_df1 <- cox_dca1$net.benefit
cox_dca_df2 <- cox_dca2$net.benefit
cox_dca_df3 <- cox_dca3$net.benefitcox_dca_long_df1 <- cox_dca_df1 %>% rename(mod1 = prob11_sm,mod2 = prob21_sm,mod3 = prob31_sm) %>% select(-4:-6) %>% mutate(time = "1") %>% pivot_longer(cols = c(all,none,contains("mod")),names_to = "models",values_to = "net_benefit")cox_dca_long_df2 <- cox_dca_df2 %>% rename(mod1 = prob12_sm,mod2 = prob22_sm,mod3 = prob32_sm) %>% select(-4:-6) %>% mutate(time = "2") %>% pivot_longer(cols = c(all,none,contains("mod")),names_to = "models",values_to = "net_benefit")cox_dca_long_df3 <- cox_dca_df3 %>% rename(mod1 = prob13_sm,mod2 = prob23_sm,mod3 = prob33_sm) %>% select(-4:-6) %>% mutate(time = "3") %>% pivot_longer(cols = c(all,none,contains("mod")),names_to = "models",values_to = "net_benefit")tes <- bind_rows(cox_dca_long_df1,cox_dca_long_df2,cox_dca_long_df3)

画图：

ggplot(tes,aes(x=threshold,y=net_benefit))+geom_line(aes(color=models,linetype=time),size=1.2)+scale_x_continuous(labels = scales::label_percent(accuracy = 1),name="Threshold Probility")+scale_y_continuous(limits = c(-0.05,0.3),name="Net Benefit")+theme_bw(base_size = 14)

这种方法可以分面。

ggplot(tes,aes(x=threshold,y=net_benefit))+geom_line(aes(color=models),size=1.2)+scale_y_continuous(limits = c(-0.05,0.3),name="Net Benefit")+scale_x_continuous(labels = scales::label_percent(accuracy = 1),name="Threshold Probility")+scale_y_continuous(limits = c(-0.05,0.3),name="Net Benefit")+theme_bw(base_size = 14)+facet_wrap(~time)

接下来演示其他模型的DCA实现方法，这里就以二分类变量为例，生存资料的DCA也是一样的，就是需要一个概率而已！

lasso回归

rm(list = ls())
suppressMessages(library(glmnet))
suppressPackageStartupMessages(library(tidyverse))

准备数据，这是从TCGA下载的一部分数据，其中sample_type是样本类型，1代表tumor，0代表normal，我们首先把因变量变为0,1。然后划分训练集和测试集。

df <- readRDS(file = "df_example.rds")df <- df %>% select(-c(2:3)) %>% mutate(sample_type = ifelse(sample_type=="Tumor",1,0))ind <- sample(1:nrow(df),nrow(df)*0.6)train_df <- df[ind,]
test_df <- df[-ind,]

构建lasso回归需要的参数值。

x <- as.matrix(train_df[,-1])
y <- train_df$sample_type

建立lasso回归模型：

cvfit = cv.glmnet(x, y, family = "binomial")
plot(cvfit)

在测试集上查看模型表现：

prob_lasso <- predict(cvfit,newx = as.matrix(test_df[,-1]),s="lambda.1se",type="response") #返回概率

然后进行DCA，也是基于训练集的：

source("../000files/dca.r")test_df$lasso <- prob_lassodf_lasso <- dca(data = test_df, # 指定数据集,必须是data.frame类型outcome="sample_type", # 指定结果变量predictors="lasso", # 指定预测变量probability = T)

这就是lasso的DCA，由于数据和模型原因，这个DCA看起来很诡异，大家千万要理解实现方法！

library(ggplot2)
library(ggsci)
library(tidyr)df_lasso$net.benefit %>% pivot_longer(cols = -threshold, names_to = "type", values_to = "net_benefit") %>% ggplot(aes(threshold, net_benefit, color = type))+geom_line(size = 1.2)+scale_color_jama(name = "Model Type")+ scale_y_continuous(limits = c(-0.02,1),name = "Net Benefit")+ scale_x_continuous(limits = c(0,1),name = "Threshold Probility")+theme_bw(base_size = 16)+theme(legend.position = c(0.2,0.3),legend.background = element_blank())

随机森林

library(ranger)rf <- ranger(sample_type ~ ., data = train_df)prob_rf <- predict(rf,test_df[,-1],type = "response")$predictions

test_df$rf <- prob_rfdf_rf <- dca(data = test_df, # 指定数据集,必须是data.frame类型outcome="sample_type", # 指定结果变量predictors="rf", # 指定预测变量probability = T,graph = F)

df_rf$net.benefit %>% pivot_longer(cols = -threshold, names_to = "type", values_to = "net_benefit") %>% ggplot(aes(threshold, net_benefit, color = type))+geom_line(size = 1.2)+scale_color_jama(name = "Model Type")+ scale_y_continuous(limits = c(-0.02,1),name = "Net Benefit")+ scale_x_continuous(limits = c(0,1),name = "Threshold Probility")+theme_bw(base_size = 16)+theme(legend.position = c(0.2,0.3),legend.background = element_blank())

logistic

logis <- glm(sample_type ~ ., data = train_df,family = binomial())prob_logis <- predict(logis, test_df[,-1],type = "response")

test_df$logis <- prob_logisdf_logis <- dca(data = test_df, # 指定数据集,必须是data.frame类型outcome="sample_type", # 指定结果变量predictors="logis", # 指定预测变量probability = T,graph = T)

还有其他比如k最近邻、支持向量机等等等等，就不一一介绍了，实现原理都是一样的，就是需要一个概率而已。

需要修改后的 stdca.r 脚本的，赞赏5元，截图发我即可~

本文首发于公众号：医学和生信笔记

“

医学和生信笔记，专注R语言在临床医学中的使用，R语言数据分析和可视化。主要分享R语言做医学统计学、meta分析、网络药理学、临床预测模型、机器学习、生物信息学等。