Cox regression analysis (forward step-wise likelihood-quotient) using the significant variable from univariate analysis (table 4) to predict survival.Christian, JansenHannah, EischeidJan, GoertzenRobert, SchierwagenEvrim, AnadolChristian, P. Strassbu...
# 每层抽取70%的数据 train_id <- strata(lung, "status", size = rev(round(table(lung$status) * 0.7)))$ID_unit # 训练数据 trainData <- lung[train_id, 4:24] # 测试数据 testData <- lung[-train_id, 4:24] 实例操作 逐步回归步骤其实就是两个函数的连用,分别是step函数以及cox函数,简单...
一、 一元线性回归 data=read.table("consum_p.txt") data x=data$V1 #定义可支配收入V1为x y=data$V2 #定义消费支出V2为y plot(x,y,xlab="x(可支配收入)",ylab="y(消费支出)") 1. 2. 3. 4. 5. 6. 由散点图可知,家庭消费支出y的平均值随可支配收入x的增加而增加并且y的条件均值和收入x...
# 每层抽取70%的数据 train_id <- strata(lung, "status", size = rev(round(table(lung$status) * 0.7)))$ID_unit # 训练数据 trainData <- lung[train_id, ] # 测试数据 testData <- lung[-train_id, ] 输入:自变量X至少一项或以上的定量变量或二分类定类变量,因变量Y要求为定量变量(若为定...
2. 读取文件 1setwd("C:\Users\000\Desktop\09_Nomogram")#设置工作目录 2 rt <- read.table("...
然后借助CreateTableOne{tableone}、mytable{moonBook}等批量处理神器来快速完成训练集和测试集间的比较,具体可参见《多分组资料的倾向性得分加权分析,以逆概率加权为例》、《基线批量分析神器:moonBook & autoReg》。【3】利用训练集数据建立Cox模型,结果用Nomogram图来呈现 利用训练集确定建模变量,常用策略是先进行...
(survminer)# 根据性别分组进行生存曲线拟合surv_fit_male<-survfit(cox_model,newdata=data[data$sex=="male",])surv_fit_female<-survfit(cox_model,newdata=data[data$sex=="female",])# 绘制生存曲线ggsurvplot(surv_fit_male,surv_fit_female,data=data,conf.int=TRUE,pval=TRUE,risk.table=TRUE,...
The table below shows survival data ready for analysis. These data are from a lung cancer study reported in Kalbfleisch (1980), page 223. These data are in the LungCancer dataset. The variables are TIME CENSOR STATUS MONTHS AGE THERAPY Days of survival Censor indicator Performance status ...
risk.table.col = "strata", linetype = "strata", surv.median.line = "hv", ggtheme = theme_bw(), palette = c("#E7B800", "#2E9FDF")) 语言中的Cox模型分析 1.单变量Cox回归 library("survival")library("survminer")res.cox <- coxph(Surv(time, status) ~ sex, data = lung)summary(...
N PFS χ2(**) OS χ2(**) HR (95% CI) P value HR (95% CI) P value Number of metastatic locations (1,2vsother) 51 0,72 (0,26–1,99) 0,524 <0,001 4,38 (1,35–14,20) 0,014 <0,001 Lung mets. (yesvsno) ...