我正在进行生存分析，我不确定我是否正确地进行了生存分析。我的数据集是种子萌发实验的结果。感兴趣的主要变量是“治疗”（分类为3级）。在我的剧本中，我试图通过比较PH系数百分比来确定治疗之间是否存在差异，哪一个是最好的，哪个程度是最好的。任何人都可以帮我解决我正在处理的一些问题吗？
1）我是否需要声明我的变量as.factor（）来使用它们？或者整数被同等解释？
2）如果违反了危险假设（PH）的相称性，我应该如何处理我的数据以进入考克斯模型建立？我已经进行了深入的研究，但却无法理解为我的模型添加协变量*时间交互或分层的编程。
3）如何将脆弱的术语包括在考克斯模型中并检测随机效应（例如种子发芽的板，4级的分类变量，代表重复）。
4）我也无法解释印刷品（摘要（cox.fra））。*
*见下文
请参阅下面我的两个带注释的整个脚本。
脚本1
rd01 <- read.table("sa_kb01.txt", header = T) # raw dataset, seed
survival
rd01
str(rd01)
rd01$begin <- as.factor(rd01$begin) # integers to factors
rd01$spp <- as.factor(rd01$spp)
rd01$cit <- as.factor(rd01$cit)
rd01$treat <- as.factor(rd01$treat)
rd01$plate <- as.factor(rd01$plate)
str(rd01)
summary(rd01)
names(rd01) # headers
### Survival analysis
# install.packages("survival")
library(survival)
library (survminer)
?survfit
?survfit.formula
?survfit.coxph
?ggsurvplot
## Fit Kaplan-Meier survivor function
km.fit <- survfit(Surv(day, status) ~ treat, data= rd01, type="kaplan-meier")
km.fit
print(summary(km.fit))
plot(km.fit, conf.int= T, fun = "event", mark.time = c(140), pch = c("S", "W", "A"), col = c("darkred","darkblue","darkgreen"), lty = c("solid","dotted","longdash"),lwd = 1.5, xlab = "time [days]", ylab = "germination probability [%]")
print(summary(km.fit))
## Comparison of Survivor Functions
# Log-rank tests
?survdiff
# Log-rank or Mantel-Haenszel test in "rho = 0" OR
# Peto & Peto modification of the Gehan-Wilcoxon test in "rho = 1"
# ... Assess all groups for heterogeneity
lrmh.123 <- survdiff(Surv(day,status) ~ treat, data= rd01, rho= 0)
print(lrmh.123) # If p<0.05 there are difference between all groups!
# ... Comparing groups pairwise
lrmh.120 <- survdiff(Surv(day,status) ~ treat, data= rd01, subset= {treat!=3}, rho= 0)
lrmh.103 <- survdiff(Surv(day,status) ~ treat, data= rd01, subset= {treat!=2}, rho= 0)
lrmh.023 <- survdiff(Surv(day,status) ~ treat, data= rd01, subset= {treat!=1}, rho= 0)
print(lrmh.120)
print(lrmh.103)
print(lrmh.023) # If p<0.05 there are difference pairwised groups!
## Checking Proportional Hazard (PH) assumption
# Define function mlogmlog() to calculate -log(-log(S(t)))
mlogmlog <- function(y){-log(-log(y))}
# Use estimated Kaplan-Meier survivor functions
km.fit
# ... to plot -log(-log(S(t))) versus log(t)
plot(km.fit, fun= mlogmlog, log="x", mark.time= c(140), pch = c("S", "W", "A"), col = c("darkred","darkblue","darkgreen"), lty = c("solid","dotted","longdash"), lwd = 1.5, xlab="time [days]", ylab= "-log(-log(S(t)))") # If lines do not cross, PH assumption is plausible!
# Interpretarion: http://www.sthda.com/english/wiki/cox-model-assumptions#testing-proportional-hazards-assumption
## Checking for multicollinearity
# install.packages("HH")
library(HH)
# Fit a generalized linear model predicting days from treatment
?glm
mc.glm <- glm(day ~ treat, data=rd01)
print(mc.glm) # doesn't need interpretation, only used to create object to VIF function
# Check for multicollinearity among covariates throught variance inflation factor (VIF)
?vif
mc.vif <- vif(mc.glm)
print(mc.vif) # VIF can determine what proportion of the variation in each covariate
# is explained by the other covariates:
# VIF > 10, serious multicollinearity; VIF = 5, evidence of multicollinearity;
# VIF < 1, no evidence of multicollinearity
## Adding covariates to the Cox model
# Create a Cox model
cox.mod <- coxph(Surv(day, status) ~ treat, data= rd01)
print(summary(cox.mod))
# Interpretation: http://www.sthda.com/english/wiki/cox-proportional-hazards-model
# Double check for PH assumption now with Cox model built
dc.ph <- cox.zph(cox.mod)
dc.ph
ggcoxzph(dc.ph) # if global and individual p-vale > 0.05, PH assumption is plausible!
## Including random effects
?frailty
# Adding plate variable as frailty term
cox.fra <- coxph(Surv(day, status) ~ treat + frailty(plate), data= rd01)
print(summary(cox.fra)) # if global and individual p-vale < 0.05,
# maintain frailty term while adding covariates 1 at a time in cox model!`
脚本2 - 相同但不同的数据集，控制treat1没有事件！
rd01 <- read.table("sa_hal01.txt", header = T) # raw dataset, seed survival
rd01
str(rd01)
rd01$begin <- as.factor(rd01$begin) # integers to factors
rd01$spp <- as.factor(rd01$spp)
rd01$cit <- as.factor(rd01$cit)
rd01$treat <- as.factor(rd01$treat)
rd01$plate <- as.factor(rd01$plate)
str(rd01)
summary(rd01)
names(rd01) # headers
### Survival analysis
# install.packages("survival")
library(survival)
library (survminer)
?survfit
?survfit.formula
?survfit.coxph
?ggsurvplot
## Fit Kaplan-Meier survivor function
km.fit <- survfit(Surv(day, status) ~ treat, data= rd01, type="kaplan-meier")
km.fit
print(summary(km.fit))
plot(km.fit, conf.int= T, fun = "event", mark.time = c(140), pch = c("S", "W", "A"), col = c("darkred","darkblue","darkgreen"), lty = c("solid","dotted","longdash"),lwd = 1.5, xlab = "time [days]", ylab = "germination probability [%]")
print(summary(km.fit))
## Comparison of Survivor Functions
# Log-rank tests
?survdiff
# Log-rank or Mantel-Haenszel test in "rho = 0" OR
# Peto & Peto modification of the Gehan-Wilcoxon test in "rho = 1"
# ... Assess all groups for heterogeneity
lrmh.123 <- survdiff(Surv(day,status) ~ treat, data= rd01, rho= 0)
print(lrmh.123) # If p<0.05 there are difference between all groups!
# ... Comparing groups pairwise
lrmh.120 <- survdiff(Surv(day,status) ~ treat, data= rd01, subset= {treat!=3}, rho= 0)
lrmh.103 <- survdiff(Surv(day,status) ~ treat, data= rd01, subset= {treat!=2}, rho= 0)
lrmh.023 <- survdiff(Surv(day,status) ~ treat, data= rd01, subset= {treat!=1}, rho= 0)
print(lrmh.120)
print(lrmh.103)
print(lrmh.023) # If p<0.05 there are difference pairwised groups!
## Checking Proportional Hazard (PH) assumption
# Define function mlogmlog() to calculate -log(-log(S(t)))
mlogmlog <- function(y){-log(-log(y))}
# Use estimated Kaplan-Meier survivor functions
km.fit
# ... to plot -log(-log(S(t))) versus log(t)
plot(km.fit, fun= mlogmlog, log="x", mark.time= c(140), pch = c("S", "W", "A"), col = c("darkred","darkblue","darkgreen"), lty = c("solid","dotted","longdash"), lwd = 1.5, xlab="time [days]", ylab= "- log(-log(S(t)))") # If lines do not cross, PH assumption is plausible!
# Interpretarion: http://www.sthda.com/english/wiki/cox-model- assumptions#testing-proportional-hazards-assumption
## Checking for multicollinearity
# install.packages("HH")
library(HH)
# Fit a generalized linear model predicting days from treatment
?glm
mc.glm <- glm(day ~ treat, data=rd01)
print(mc.glm) # doesn't need interpretation, only used to create object to VIF function
# Check for multicollinearity among covariates throught variance inflation factor (VIF)
?vif
mc.vif <- vif(mc.glm)
print(mc.vif) # VIF can determine what proportion of the variation in each covariate
# is explained by the other covariates:
# VIF > 10, serious multicollinearity; VIF = 5, evidence of multicollinearity;
# VIF < 1, no evidence of multicollinearity
## Adding covariates to the Cox model
# Create a Cox model
cox.mod <- coxph(Surv(day, status) ~ treat, data= rd01)
print(summary(cox.mod))
# Interpretation: http://www.sthda.com/english/wiki/cox-proportional-hazards-model
# Double check for PH assumption now with Cox model built
dc.ph <- cox.zph(cox.mod)
dc.ph
ggcoxzph(dc.ph) # if global and individual p-vale > 0.05, PH assumption is plausible!
## Including random effects
?frailty
# Adding plate variable as frailty term
cox.fra <- coxph(Surv(day, status) ~ treat + frailty(plate), data= rd01)
print(summary(cox.fra)) # if global and individual p-vale < 0.05,
# maintain frailty term while adding covariates 1 at a time in cox model!
似乎存在统计上显着的差异，并且treat3与两个脚本中的其他组不同。在脚本1中PH被违反，我现在不做该做什么。除此之外，脚本1中的Cox模型似乎工作正常并且风险比的解释是可以的，但在脚本2中，不知道如何解释或解决（控制条件1中没有事件）。
1）我是否需要声明我的变量as.factor（）来使用它们？或者整数被同等解释？
我认为在你的情况下as.factor是正确的。如果你有连续的数字变量，你可以使用整数 - 例如，如果你在实验之前有时间存储种子，你可以使用as.numeric作为时间变量。
2）如果PH被违反，我应该如何处理我的数据以进入考克斯模型建设？我已经进行了深入的研究，但却无法理解为我的模型添加协变量x时间交互或分层的编程。
Cox回归，即Cox比例风险模型，基于比例风险的假设。如果违反该假设，您将无法获得可靠的结果。您可能可以尝试一些数据转换，看看它是否有帮助。或者，如果在某个子实验/组中违反了它，您可以将其删除。