In cancer studies, most of survival analyses use the following methods:
-Kaplan-Meier plots to visualize survival curves Log-rank test to compare 
 thesurvival curves of two or more groups 
-Cox proportional hazards model.

1. Upload library

1.1 Upload data

data("lung")
head(lung,3)
##   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

2. Compute survival

fit <- survfit(Surv(time, status) ~ sex, data = lung)

res.sum <- surv_summary(fit)
## Warning in .get_data(x, data = data): The `data` argument is not provided. Data
## will be extracted from model fit.
head(res.sum,3)
##   time n.risk n.event n.censor      surv    std.err     upper     lower strata
## 1   11    138       3        0 0.9782609 0.01268978 1.0000000 0.9542301  sex=1
## 2   12    135       1        0 0.9710145 0.01470747 0.9994124 0.9434235  sex=1
## 3   13    134       2        0 0.9565217 0.01814885 0.9911586 0.9230952  sex=1
##   sex
## 1   1
## 2   1
## 3   1

3. Value returned by survfit

d <- data.frame(time = fit$time,
                n.risk = fit$n.risk,
                n.event = fit$n.event,
                n.censor = fit$n.censor,
                surv = fit$surv,
                upper = fit$upper,
                lower = fit$lower)
head(d,3)
##   time n.risk n.event n.censor      surv     upper     lower
## 1   11    138       3        0 0.9782609 1.0000000 0.9542301
## 2   12    135       1        0 0.9710145 0.9994124 0.9434235
## 3   13    134       2        0 0.9565217 0.9911586 0.9230952

4. Visualize curv

ggsurvplot(fit,
           ggtheme = theme_classic(),
           palette = c("#E7B800", "#2E9FDF"))

The plot can be further customized using the following arguments:

conf.int.style = “step” to change the style of confidence interval bands. xlab to 
change the x axis label. break.time.by = 200 break x axis in time intervals by 200. 

risk.table = “abs_pct”to show both absolute number and percentage of individuals 
at risk. risk.table.y.text.col = TRUE and risk.table.y.text = FALSE 
to provide bars instead of names in text annotations of the legend
of risk table. ncensor.plot = TRUE to plot the number of censored subjects 
at time t. As suggested by Marcin Kosinski, This is a good additional feedback 
to survival curves, so that one could realize: how do survival curves look like,
what is the number of risk set AND what is the cause that the risk set
become smaller: is it caused by events or by censored events? 
legend.labs to change the legend labels.``

The Kaplan-Meier plot can be interpreted as follow: 
The horizontal axis (x-axis) represents time in days, 
and the vertical axis (y-axis) shows the probability
of surviving or the proportion of people surviving. 
The lines represent survival curves of the two groups. 
A vertical drop in the curves indicates an event. 
The vertical tick mark on the curves
means that a patient was censored at this time.

4.1 The survival shorten curves

ggsurvplot(fit, conf.int = TRUE, risk.table.col = "strata", break.time.by = 200, ggtheme = theme_bw(), palette = c("#E7B800", "#2E9FDF"), xlim = c(0, 600)) ```

5. Rank

surv_diff <- survdiff(Surv(time, status) ~ sex, data = lung)
surv_diff
## Call:
## survdiff(formula = Surv(time, status) ~ 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= 0.001

6. Fit complex survival curves

fit2 <- survfit( Surv(time, status) ~ sex + rx + adhere,
                data = colon )
ggsurv <- ggsurvplot(fit2, fun = "event", conf.int = TRUE, ggtheme = theme_bw())
ggsurv$plot + theme_bw() +
        theme(legend.position = "right") +
        facet_grid(rx ~ adhere)

7. Analyze weight loss and survival

library(dplyr)
summary(lung$wt.loss)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
## -24.000   0.000   7.000   9.832  15.750  68.000      14
lung <- lung %>%
        mutate(group = case_when(
                wt.loss >= quantile(wt.loss, 0.5, na.rm = TRUE) ~ 'wl.high',
                wt.loss < quantile(wt.loss, 0.5, na.rm = TRUE) ~ 'wl.low',
                TRUE ~ NA_character_
        ))
# when use quantile or mean, need to add na.rm = TRUE
fit1 <- survfit(Surv(time, status) ~ group, data = lung)
ggsurvplot(fit1,
           #pval = TRUE, conf.int = TRUE,
           #risk.table = TRUE,
           #risk.table.col = "group",
           #surv.median.line = "hv",
           ggtheme = theme_classic(),
           palette = c("#E7B800", "#2E9FDF"))

## Warning: Vectorized input to `element_text()` is not officially supported.
## Results may be unexpected or may change in future versions of ggplot2.