Survival analysis

准备工作

#安装R包
#install.packages(c("survival", "survminer"))
#加载R包
library(survival) 
library(survminer)  
#survival包里包含的数据集
data(package="survival") 

#以肺癌数据为例,显示数据前六行
head(lung)
##   inst time status age sex ph.ecog ph.karno pat.karno meal.cal wt.loss
## 1    3  306      2  74   1       1       90       100     1175      NA
## 2    3  455      2  68   1       0       90        90     1225      15
## 3    3 1010      1  56   1       0       90        90       NA      15
## 4    5  210      2  57   1       1       90        60     1150      11
## 5    1  883      2  60   1       0      100        90       NA       0
## 6   12 1022      1  74   1       1       50        80      513       0
#查看肺癌数据详细说明 
?lung
#inst:	 Institution code
#time:	 Survival time in days
#status:	 censoring status 1=censored, 2=dead
#age:	 Age in years
#sex:	 Male=1 Female=2
#ph.ecog:	 ECOG performance score (0=good 5=dead)
#ph.karno:	 Karnofsky performance score (bad=0-good=100) rated by physician
#pat.karno:	 Karnofsky performance score as rated by patient
#meal.cal:	 Calories consumed at meals
#wt.loss:	 Weight loss in last six months

建立生存分析

#创建生存数据对象
surv_obj <- with(lung, Surv(time, status))

#用survfit()估计KM生存曲线, 用数据集中所有患者作为分析对象
fit1  <- survfit(surv_obj ~ 1)

#显示寿命表
summary1 <- surv_summary(fit1)
head(summary1)
##   time n.risk n.event n.censor      surv     std.err     upper     lower
## 1    5    228       1        0 0.9956140 0.004395615 1.0000000 0.9870734
## 2   11    227       3        0 0.9824561 0.008849904 0.9996460 0.9655619
## 3   12    224       1        0 0.9780702 0.009916654 0.9972662 0.9592437
## 4   13    223       2        0 0.9692982 0.011786516 0.9919508 0.9471630
## 5   15    221       1        0 0.9649123 0.012628921 0.9890941 0.9413217
## 6   26    220       1        0 0.9605263 0.013425540 0.9861367 0.9355811
#中位生存期及95%置信区间
#中位生存期:当累积生存率为0.5时所对应的生存时间,表示有且只有50%的个体可以活过这个时间.
fit1
## Call: survfit(formula = surv_obj ~ 1)
## 
##       n  events  median 0.95LCL 0.95UCL 
##     228     165     310     285     363
##25%, 50%, 75%生存期
quantile(fit1, probs=c(0.25, 0.5, 0.75), conf.int=FALSE)
##  25  50  75 
## 170 310 550
#50天和100天生存状况
summary(fit1, times=c(200, 400))
## Call: survfit(formula = surv_obj ~ 1)
## 
##  time n.risk n.event survival std.err lower 95% CI upper 95% CI
##   200    144      72    0.680  0.0311        0.622        0.744
##   400     57      54    0.377  0.0358        0.313        0.454

不同因子生存曲线的比较

例如我们想比较男性和女性肺癌患者生存曲线的差别

#用survfit()估计KM生存曲线, 方程右边以性别为区分,分别估计男性和女性的生存曲线
fit2 <- survfit(surv_obj ~ sex, data = lung)

#分别显示男性和女性的寿命表
summary2 <- surv_summary(fit2)
head(summary2)
##   time n.risk n.event n.censor      surv    std.err     upper     lower
## 1   11    138       3        0 0.9782609 0.01268978 1.0000000 0.9542301
## 2   12    135       1        0 0.9710145 0.01470747 0.9994124 0.9434235
## 3   13    134       2        0 0.9565217 0.01814885 0.9911586 0.9230952
## 4   15    132       1        0 0.9492754 0.01967768 0.9866017 0.9133612
## 5   26    131       1        0 0.9420290 0.02111708 0.9818365 0.9038355
## 6   30    130       1        0 0.9347826 0.02248469 0.9768989 0.8944820
##   strata sex
## 1  sex=1   1
## 2  sex=1   1
## 3  sex=1   1
## 4  sex=1   1
## 5  sex=1   1
## 6  sex=1   1
#分别显示男性和女性的中位生存期及95%置信区间
fit2
## Call: survfit(formula = surv_obj ~ sex, data = lung)
## 
##         n events median 0.95LCL 0.95UCL
## sex=1 138    112    270     212     310
## sex=2  90     53    426     348     550
#用lag-rank test检验不同组生存曲线的差异
#survdiff()函数最后一个参数为rho,用于指定检验的类型
#rho=0(默认),进行log-rank检验或Mantel−Haenszelχ2检验,比较各组期望频数和实际观察数。如果两组间的差异水平太大,χ2会较大而P值较小,表示生存曲线有统计学差异.
#当rho=1时,进行Gehan-Wilcoxon的Peto校正检验,该检验赋予早期终点事件较大的权重
surv_diff <- survdiff(surv_obj ~ sex, data = lung)
surv_diff
## Call:
## survdiff(formula = surv_obj ~ sex, data = lung)
## 
##         N Observed Expected (O-E)^2/E (O-E)^2/V
## sex=1 138      112     91.6      4.55      10.3
## sex=2  90       53     73.4      5.68      10.3
## 
##  Chisq= 10.3  on 1 degrees of freedom, p= 0.00131

生存曲线可视化

绘制两条生存曲线

ggsurv <- ggsurvplot(fit2, #survfit object with calculated statistics.
           data = lung, #data used to fit survival curves.
           main = "Survival curve", #add title 
           xlab = "Time (Days)",  #change the x axis label.
           font.main = c(16, "bold", "darkblue"), #change title font size, style and color
           font.x = c(14, "bold.italic", "red"), #change x axis label font size, style and color
           font.y = c(14, "bold.italic", "darkred"), #change y axis font size, style and color
           font.tickslab = c(12, "plain", "darkgreen"), #change ticks label font size, style and color
           
           legend = "bottom",  #change legend position
           #legend = c(0.2, 0.2), #Specify legend position by its coordinates
           legend.title = "Sex", #change legend title
           legend.labs = c("Male", "Female"), #change legend labels
           
           size = 1,  #change line size
           linetype = "strata", #change line type by groups (i.e. "strata")
           break.time.by = 250, #break X axis in time intervals by 250
           #xlim = c(0, 600)), #shorten the survival curves
           palette = c("#E7B800", "#2E9FDF"), #custom color palette
           #palette = "Dark2", #Use brewer color palette "Dark2"
           #palette = "grey",  #Use grey palette
           
           conf.int = TRUE, #Add confidence interval
           conf.int.style = "step",  #customize style of confidence intervals
           pval = TRUE, #Add p-value of the Log-Rank test comparing the groups
           surv.median.line = "hv",  #add horizontal/vertical line at median survival.
           
           risk.table = TRUE, #Add risk table
           #risk.table = "absolute/percentage/abs_pct", 
           #to show absolute number, percentage of subjects, and both at risk by time, respectively.
           risk.table.col = "strata", #Change risk table color by groups
           risk.table.y.text.col = TRUE, #colour risk table text annotations by strata
           #risk.table.y.text = FALSE, #show bars instead of names in text annotations in legend of risk table 
           risk.table.height = 0.25, #the height of the risk table. Useful when you have multiple groups
           
           ncensor.plot = TRUE, # plot the number of censored subjects at time t
           ncensor.plot.height = 0.25,
           
           ggtheme = theme_bw(), #customize plot and risk table with a theme.
           xlim = c(0, 600) #Change x axis limits (xlim)
           )
ggsurv

绘制多条生存曲线

fit3 <- survfit(Surv(time, status) ~ sex + rx + adhere, data = colon )

ggsurvplot(fit3, 
           pval = TRUE, #Add p-value
           break.time.by = 800, #break time axis by 800
           risk.table = TRUE, #add risk table
           risk.table.col = "strata", #Change risk table color by groups
           risk.table.height = 0.5, #the height of the risk table. Useful when you have multiple groups
           ggtheme = theme_bw() #change plot theme
           #legend.labs = c("A", "B", "C", "D", "E", "F") #Change legend labels
           )

Cox回归

单因素COX回归

covariates <- c("age", "sex",  "ph.karno", "ph.ecog", "meal.cal", "wt.loss")
univ_formulas <- sapply(covariates,
                        function(x) as.formula(paste('Surv(time, status)~', x)))
                        
univ_models <- lapply( univ_formulas, function(x){coxph(x, data = lung)})

# Extract results
univ_results <- lapply(univ_models,
                       function(x){ 
                          x <- summary(x)
                          p.value<-signif(x$wald["pvalue"], digits=2)
                          wald.test<-signif(x$wald["test"], digits=2)
                          beta<-signif(x$coef[1], digits=2);#coeficient beta
                          HR <-signif(x$coef[2], digits=2);#exp(beta)
                          HR.confint.lower <- signif(x$conf.int[,"lower .95"], 2)
                          HR.confint.upper <- signif(x$conf.int[,"upper .95"],2)
                          HR <- paste0(HR, " (", 
                                       HR.confint.lower, "-", HR.confint.upper, ")")
                          res<-c(beta, HR, wald.test, p.value)
                          names(res)<-c("beta", "HR (95% CI for HR)", "wald.test", 
                                        "p.value")
                          return(res)
                          #return(exp(cbind(coef(x),confint(x))))
                         })
results <- t(as.data.frame(univ_results, check.names = FALSE))
as.data.frame(results)
##              beta HR (95% CI for HR) wald.test p.value
## age         0.019            1 (1-1)       4.1   0.042
## sex         -0.53   0.59 (0.42-0.82)        10  0.0015
## ph.karno   -0.016      0.98 (0.97-1)       7.9   0.005
## ph.ecog      0.48        1.6 (1.3-2)        18 2.7e-05
## meal.cal -0.00012            1 (1-1)      0.29    0.59
## wt.loss    0.0013         1 (0.99-1)      0.05    0.83

多因素COX回归

多个自变量是如何共同影响因变量

cox_model <- coxph(Surv(time, status) ~ age + sex + ph.ecog + ph.karno, data = lung)
summary(cox_model)
## Call:
## coxph(formula = Surv(time, status) ~ age + sex + ph.ecog + ph.karno, 
##     data = lung)
## 
##   n= 226, number of events= 163 
##    (2 observations deleted due to missingness)
## 
##               coef exp(coef)  se(coef)      z Pr(>|z|)    
## age       0.012868  1.012951  0.009404  1.368 0.171226    
## sex      -0.572802  0.563943  0.169222 -3.385 0.000712 ***
## ph.ecog   0.633077  1.883397  0.176034  3.596 0.000323 ***
## ph.karno  0.012558  1.012637  0.009514  1.320 0.186842    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##          exp(coef) exp(-coef) lower .95 upper .95
## age         1.0130     0.9872    0.9945    1.0318
## sex         0.5639     1.7732    0.4048    0.7857
## ph.ecog     1.8834     0.5310    1.3338    2.6594
## ph.karno    1.0126     0.9875    0.9939    1.0317
## 
## Concordance= 0.632  (se = 0.026 )
## Rsquare= 0.129   (max possible= 0.999 )
## Likelihood ratio test= 31.27  on 4 df,   p=2.695e-06
## Wald test            = 30.61  on 4 df,   p=3.676e-06
## Score (logrank) test = 31.06  on 4 df,   p=2.974e-06

COX回归结果解释

1. z结果是wald检验的结果,z = coef/se(coef),用于检验回归系数是否显著区别于0
2. 回归系数(coef)
3. 风险???(hazard ratio, HR): HR = exp(coef), 衡量协变量影响的程度
4. 风险比的95%置信区间
5. 对模型的整体分析,即评价模型是否有意义的三种检验(Likelihood ratio test, Wald test, Score (logrank) test)

COX模型等比性检验

方法一:通过图形展示,对纵轴和横轴分别取对数,绘制不同自变量水平下的生存曲线。如果两条曲线平行,则符合比例风险假定.
方法二:利用cox.zph函数进行检验
cox.zph(cox_model)
##             rho  chisq      p
## age      0.0168 0.0502 0.8227
## sex      0.0881 1.2539 0.2628
## ph.ecog  0.0600 0.5122 0.4742
## ph.karno 0.1843 4.4660 0.0346
## GLOBAL       NA 8.0414 0.0901
#检验结果中ph.karno具有显著性(p<0.05),即这一自变量不符合“等比性”要求,这一回归模型不准确

根据检验结果调整模型,将不可比的自变量进行分层, 也就是说排除混杂因素的影响,分析研究因素的影响

cox_model1 <- coxph(Surv(time, status) ~ age + sex + ph.ecog + strata(ph.karno), data =  lung)
summary(cox_model1)
## Call:
## coxph(formula = Surv(time, status) ~ age + sex + ph.ecog + strata(ph.karno), 
##     data = lung)
## 
##   n= 226, number of events= 163 
##    (2 observations deleted due to missingness)
## 
##              coef exp(coef)  se(coef)      z Pr(>|z|)    
## age      0.014807  1.014918  0.009651  1.534 0.124972    
## sex     -0.591683  0.553395  0.173049 -3.419 0.000628 ***
## ph.ecog  0.483879  1.622356  0.196558  2.462 0.013825 *  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
##         exp(coef) exp(-coef) lower .95 upper .95
## age        1.0149     0.9853    0.9959    1.0343
## sex        0.5534     1.8070    0.3942    0.7768
## ph.ecog    1.6224     0.6164    1.1037    2.3848
## 
## Concordance= 0.601  (se = 0.054 )
## Rsquare= 0.085   (max possible= 0.986 )
## Likelihood ratio test= 19.99  on 3 df,   p=0.0001708
## Wald test            = 19.14  on 3 df,   p=0.0002557
## Score (logrank) test = 19.57  on 3 df,   p=0.0002083

再次检验模型

cox.zph(cox_model1)
##            rho chisq     p
## age     0.0454 0.363 0.547
## sex     0.0919 1.289 0.256
## ph.ecog 0.0358 0.184 0.668
## GLOBAL      NA 1.958 0.581
#哈哈哈,bingo~
CHENTONG
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CHENTONG

CHENTONG
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