Debora Oliveira
A Kaplan-Meier analysis allows estimation of survival over time, even when patients drop out or are studied for different lengths of time. The goal is to estimate a population survival curve by computing the fraction surviving at each time.
To run this project you need the following libraries:
library(redcapAPI)
library(dplyr)
library(plyr)
library(survival)
library(ggplot2)
library(survminer)
library(knitr)In this project, we use data collected using redcap to further use redcap API to establish a connection between the two softwares. Apart from recapConnection(), this package also has other very useful tools, such as exportRecords() which allows to import into RStudio patient's data.
source("token.txt")
rcon <- redcapConnection(url=url, token=token)
rm(token)
##########
#Calling our variables
##########
vector_id <- c("cadast_same")
trial_time <- c("t0_arm_1")
data_base_all <- exportRecords(rcon, factors = FALSE,
fields = c("record_id", paste(vector_id)),
events = trial_time, dates = TRUE)Both token and url must be provided by your institution.
These two libraries can be used mainly to clean and group the data from our source. From package plyr, ddply() is used o group patients by record_id and same. Although, it seems too repetitive, a patient can have only one same and many entries along the trial. The following code gives an example of how ddply() is implemented:
group_by_record <- ddply(data_base_all, ~record_id , summarise,
cadast_same = max(cadast_same))Before ddply() number of rows and columns::
## [1] 636
## [1] 18
After ddply() number of rows and columns:
## [1] 231
## [1] 8
Furthermore, the dplyr package has two important functions mutate_all() that allows to mutate all the data frame to numeric, including dates, and select() which helps cleaning the extra data that exportRecords() brings with the records, such as DAG.
The survival package allows us to use both functions Suv() and survfit(). Surv() function contains failure time and censoring information. It also provides the basic survival analysis data structure.
## Call: survfit(formula = Surv(group_same_clean$dias_vivo, group_same_clean$censored) ~
## 1, data = group_same_clean)
##
## time n.risk n.event survival std.err lower 95% CI upper 95% CI
## 3 117 1 0.991 0.00851 0.975 1.000
## 7 114 1 0.983 0.01209 0.959 1.000
## 8 111 2 0.965 0.01717 0.932 0.999
## 9 107 1 0.956 0.01923 0.919 0.994
## 13 99 2 0.937 0.02319 0.892 0.983
## 14 97 1 0.927 0.02488 0.880 0.977
## 16 95 1 0.917 0.02647 0.867 0.971
## 19 93 1 0.907 0.02796 0.854 0.964
## 20 92 1 0.898 0.02934 0.842 0.957
## 24 88 1 0.887 0.03073 0.829 0.950
## 26 86 2 0.867 0.03330 0.804 0.935
## 27 83 1 0.856 0.03450 0.791 0.927
## 35 78 1 0.845 0.03576 0.778 0.918
## 38 72 2 0.822 0.03843 0.750 0.901
## 42 66 2 0.797 0.04110 0.720 0.882
## 50 55 1 0.782 0.04283 0.703 0.871
## 52 53 1 0.768 0.04450 0.685 0.860
## 63 46 1 0.751 0.04655 0.665 0.848
## 66 45 1 0.734 0.04842 0.645 0.836
## 70 43 1 0.717 0.05021 0.625 0.823
## 91 39 1 0.699 0.05218 0.604 0.809
## 120 34 1 0.678 0.05455 0.579 0.794
## 138 33 1 0.658 0.05663 0.556 0.779
## 163 31 1 0.637 0.05865 0.531 0.762
## 175 29 1 0.615 0.06059 0.507 0.746
## 176 28 1 0.593 0.06228 0.482 0.728
## 180 27 1 0.571 0.06372 0.458 0.710
## 184 25 1 0.548 0.06513 0.434 0.692
The survminer package contain the function ggsurvplot().
group_same_clean$age <- cut(group_same_clean$cadast_idade_anos, c(20,60,80,100))
icc.fit<- survfit(Surv(group_same_clean$dias_vivo, group_same_clean$censored)~group_same_clean$age,
data= group_same_clean)
ggsurvplot(icc.fit, fun = NULL, color = "strata", palette = "hue", linetype = 1,
break.x.by = 30, surv.scale = c("percent"),
conf.int = FALSE, conf.int.fill = "strata", censor = TRUE, pval = TRUE,
pval.size = TRUE, pval.coord = c(NULL, NULL), main = "Congestive Heart Failure Survival" ,
xlab = "Days", ylab = "Survival probability", font.main = c(16, "plain", "black"),
font.x = c(14, "plain", "black"), font.y = c(14, "plain", "black"),
font.tickslab = c(12, "plain", "black"), xlim = c(0,248), ylim = c(0.5,1.0),
legend = c("top"), legend.title = " ",
legend.labs = NULL, font.legend = c(10, "plain", "black"), risk.table = TRUE,
risk.table.title = "Number at risk by time", risk.table.col = "strata",
risk.table.fontsize = 4.5, risk.table.y.text = TRUE, risk.table.y.text.col = TRUE,
risk.table.height = 0.25, surv.plot.height = 0.75, ggtheme = theme_minimal())To plot a Kaplan-Meier curve few information is required. It is the number of days in the trial, a variable indicating whether the patient reached the event of interest (censored) and maybe a variable to stratify the sample (age, gender, comorbidities, etc).
## censored dias_vivo age
## 2 0 38 days (80,100]
## 3 0 106 days (80,100]
## 4 1 52 days (80,100]
## 5 0 5 days (80,100]
## 6 0 25 days (80,100]
## 7 1 26 days (80,100]