Survival Analysis

iyanxiao

Glioma Radioimmunotherapy
Figure 9.1 leads to the impression that patients treated with the novel radioimmunotherapy
survive longer, regardless of the tumor type. In order to
assess if this informal finding is reliable, we may perform a log-rank test via
R> survdiff(Surv(time, event) ~ group, data = g3)
Call:
survdiff(formula = Surv(time, event) ~ group, data = g3)
N Observed Expected (O-E)^2/E (O-E)^2/V
group=Control 6 4 1.49 4.23 6.06
group=RIT 11 2 4.51 1.40 6.06
Chisq= 6.1 on 1 degrees of freedom, p= 0.0138
which indicates that the survival times are indeed different in both groups.
However, the number of patients is rather limited and so it might be dangerous
to rely on asymptotic tests. As shown in Chapter 3, conditioning on the
data and computing the distribution of the test statistics without additional
assumptions is one alternative. The function surv_test from package coin
(Hothorn et al., 2006b,a) can be used to compute an exact conditional test
answering the question whether the survival times differ for grade III patients:
R> library("coin")
R> surv_test(Surv(time, event) ~ group, data = g3,
+ distribution = "exact")
Exact Logrank Test
data: Surv(time, event) by groups Control, RIT
Z = 2.1711, p-value = 0.02877
alternative hypothesis: two.sided
R> data("glioma", package = "coin")
R> library("survival")
R> layout(matrix(1:2, ncol = 2))
R> g3 <- subset(glioma, histology == "Grade3")
R> plot(survfit(Surv(time, event) ~ group, data = g3),
+ main = "Grade III Glioma", lty = c(2, 1), ylab = "Probability",
+ xlab = "Survival Time in Month", legend.bty = "n",
+ legend.text = c("Control", "Treated"))
R> g4 <- subset(glioma, histology == "GBM")
R> plot(survfit(Surv(time, event) ~ group, data = g4),
+ main = "Grade IV Glioma", ylab = "Probability",
+ lty = c(2, 1), xlab = "Survival Time in Month",
+ xlim = c(0, max(glioma$time) * 1.05))

genechip

学些了。试试你的例子