Kaplan-Meier Survival Curve Help

[hhagemei]

Hello, so I am trying to run Kaplan-Meier survival curve code and I am having trouble getting it to plot correctly. I managed to get each of my plots to work when I separate my data into my three trials on their own data sheets. But when I try to run the combined data set it makes everything look really messy. Is there a way to filter the data (while it is coming from one data sheet) so I can do all of the analyses on one Code-Page? I have three categories for my data: Salinity (7 treatments), Site (2 different sites), Trial (3 different trials)

Here is my code that leads up to this point and then the resulting curve:

#Library-----
library(data.table)
library(ggplot2)
library(survival)
library(survminer)
library(lubridate)
library(ggsurvfit)
library(performance)
#require(AICcmodavg) #allows you to make AIC table!
library(tidyverse)
library(finalfit)
library(dplyr)
library(forcats)
library(survival)

#NOTE: majority of notes are direct quotes from these sources to minimize issue 
#of re-wording something incorrectly 

#Data Input ------
survival = fread("/Users/haley/documents/Thesis/GROUPED_MASTER_CH1.csv") 

#Create a dataset with only healthy crabs

healthy = survival[survival$Parasitized != 1, ]

# healthy = healthy[healthy$Remove != 1, ] unsure if need these
# healthy = healthy[healthy$Gravid.in.Expt != 1, ]
# healthy = healthy[!is.na(healthy$Salinity)] 

healthy$Trial = as.factor(healthy$Trial) #was region in Darby's Data
healthy$Site = as.factor(healthy$Site)
healthy$Salinity = as.factor(healthy$Salinity) # was coev history in Darby's
healthy$GROUP = as.factor(healthy$GROUP)  # was Year in Darby's
healthy$Sex = as.factor(healthy$Sex) # Was regional.pop in Darby's

healthy$Initial_Carapace_Width = as.numeric(healthy$Initial_Carapace_Width)

health.all = healthy

glimpse(healthy)
missing_glimpse(healthy) #nothing super important is missing any data
ff_glimpse(healthy)

missing_plot(health.all)

#status = Live (i.e., cenosring) 
#0 = event did not happen (i.e., censored) 
#1 = event happened (i.e., died)

#Time = Survival (i.e., number of days from the start that the crab was alive or
#number of days of entire trial (if crab was censored))

#What is the replication by site, population, and salinity ----

health.all$one = 1

aggregate(one ~ Site + Trial + Salinity , data = health.all, FUN = sum) 
aggregate(one ~ Site + Salinity, data = health.all, FUN = sum) 

#.-----
#.-----
#Survival Analysis tutorial with interpretations

# Estimation of Survival Distributions -----
# Kaplan Meier Survival Estimator ----
#Kaplan Meier (KM) survival estimator = non-parametric statistic used to 
#estimate the survival function from time-to-event data

survival_object <- healthy %$%
  Surv(Survival, Live)

head(survival_object) # + marks censoring (i.e., alive)

# Model 
# overall survival in whole cohort (to do this subsitute "1" for coev below)

my_survfit <- survfit(survival_object ~ Salinity , data = healthy)
my_survfit

# Life table 
#A Life table is the tabular form of a KM plot -- shows survival as a 
#proportion, together with confidence limits

summary(my_survfit, times = c(0, 2, 5, 10, 15, 20, 25, 28))

#  Figures -----

dependent_os = "Surv(Survival, Live)"
explanatory = c("Salinity")

healthy %>% 
  surv_plot(dependent_os, explanatory, pval = TRUE)

# the ggsurvplot function from the survminer package is built on ggplot2, 
#and can be used to create Kaplan-Meier plots. Checkout the cheatsheet for 
#the survminer package

palette_Salinity = setNames(object = c("#7A0403FF", 
    "#E03F08FF", "#18DAC7FF", "#83FF52FF", "#FEAA33FF", "#2E9FDF", "#E7B800"), 
    nm = levels(healthy$Salinity))


ggsurvfit(survfit(Surv(Survival, Live) ~ Salinity, data=healthy), 
          linetype_aes = FALSE, size = 2) +
  scale_color_manual(values = palette_Salinity) +
  theme_bw() +
  theme(panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank()) +
  theme(axis.title.x = element_text(size=20), 
        axis.text.x  = element_text(size=16)) +
  theme(axis.title.y = element_text(size=20), 
        axis.text.y  = element_text(size=16)) +
  theme(panel.border = element_rect(colour = "black", fill=NA, size=2))+
  theme(axis.ticks = element_line(color = "black", size = 2),
        axis.ticks.length = unit(0.25, "cm")) +
  theme(legend.position = c(.8, .8)) +
  theme(legend.text = element_text(size=16)) +
  guides(color = guide_legend(override.aes = list(size = 10, stroke = 20))) 

ggsurvfit(survfit(Surv(Survival, Live) ~ Site, data=healthy), 
          linetype_aes = FALSE, size = 2) +
          scale_color_manual(values = c("#38F491FF","#E03F08FF")) +
          theme_bw() +
          theme(panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank())

ggsurvfit(survfit(Surv(Survival, Live) ~ Salinity, data=healthy),
          linetype_aes = FALSE, size = 2) 

# There is an error with this line for some reason

# ggsurvfit(Surv(Survival, Live) ~ Salinity + Site, data=healthy,
# linetype_aes = FALSE, size = 2)

survfit2(Surv(Survival, Live) ~ Salinity + Site + Trial, data=healthy) %>%
  ggsurvfit() +
  add_confidence_interval()

[hhagemei]

I am unsure why some of the code is bold and really large, apologies for that.

[nirgrahamuk]

It might be helpful for you to know how to properly format code and console output that you post here. Using proper code formatting makes the site easier to read, prevents confusion (unformatted code can get garbled by the forum software ).

Check out this FAQ to find out how — it's as easy as the click of a button! :

FAQ: How to format your code

Formatting Code: the Basics All code or console output you include in your posts should be formatted properly. Luckily, this is very easy to do! Just use the code formatting button at the top of the post editing box: [image] Select some code Click the </> button! The code formatting button automatically adds special Markdown formatting symbols to whatever you selected. If you check the preview pane to the right of your post, you'll see that the forum software now outputs nicely formatted code . [04] The code formatting button is pretty smart! It can tell the difference between snippets of code that are part of a sentence and blocks of code that stand alone. …