Cumulative Incidence Curves {R}

This is an example of creating cumulative incidence curves for a time-to-event outcome. Here, we’re using our Personal Hypertension Care project data from the OneFlorida research consortium.

We’re using three packages: tidyverse, survival, and survminer.

Data Prep

# packages
library(tidyverse)
library(survival)
library(survminer)

# data + a bit of cleaning/wrangling
fu <- read_csv("/Users/stevensmith/Dropbox (UFL)/Manuscripts/K01/Early Patterns of Care/data/tfu_visualization.csv",
               show_col_types = FALSE) |> 
  rename_with(tolower) #|> 
#  replace_na(list(time_to_actual_sfu = 0))

# recalculate time variable for patients w/o a second follow-up
# fu <- fu |> 
#   mutate(t2fu = case_when(time_to_actual_ffu == 1 & 
#                             time_to_actual_sfu == 0 ~ 365 - t1fu, 
#                           TRUE ~ t2fu))

# create a subset of patients with a first follow-up (for time-to-second
# follow-up visit plot)
fu2 <- fu |> filter(time_to_actual_ffu == 1)

# create survival objects
fut1 <- survfit(Surv(t1fu, time_to_actual_ffu) ~ source, data = fu)
fut2 <- survfit(Surv(t2fu, time_to_actual_sfu) ~ source, data = fu2)

# function for readibility
comma <- function(x) format(x, digits = 2, big.mark = ",")

Data

We have 64,586 Medicaid patients, and 52,712 Medicare patients. We intend to compare cumulative incidence of time-to-first follow-up (first hypertension-related visit) after starting hypertension therapy. Time 0 is date that they started antihypertensive treatment.

Plotting time-to-first follow-up

First, we create a cumulative incidence curve for time-to-first follow-up. The y-axis represents the cumulative proportion that had a first follow-up visit over time (x-axis). We’re going to include Medicaid and Medicare on the same plot, as well as a formal comparison (w/ p-value), but if we don’t want to directly compare the two, we could remove the p-value, or even split these up onto 2 figures. We’re using the ggsurvplot() function from the survminer package.

ggsurvplot(fut1, 
           data = fu, 
           fun = "event", 
           censor = FALSE,
           pval = TRUE,    # set to FALSE if don't want p-value
           pval.coord = c(270, 0.05),  # can delete this line if don't want p-value
           risk.table = TRUE, 
           legend.title = "Popn", 
           legend.labs = c("Medicaid", "Medicare"),
           palette = c("#A7473A", "#4B5F6C"), 
           break.time.by = 90, 
           xlab = "Days from Initial Visit", 
           ylab = "Cumulative Incidence of Follow-up",
           axes.offset = TRUE,
           ylim = c(0, 1),
           tables.theme = theme_void(),
           tables.height = 0.15,
           tables.col = "strata",
           surv.scale = "percent")

Time-to-second follow-up

Same thing here, but now we are restricting this plot to the 28,304 Medicaid patients, and 14,608 Medicare patients that had a first-follow-up visit. And, time 0 on x-axis represents the time of their first follow-up visit, with the outcome being second follow-up visit.

ggsurvplot(fut2, 
           data = fu2, 
           fun = "event", 
           censor = FALSE,
           pval = TRUE, 
           pval.coord = c(30, 0.8),
           risk.table = TRUE, 
           legend.title = "Popn", 
           legend.labs = c("Medicaid", "Medicare"),
           palette = c("#A7473A", "#4B5F6C"), 
           break.time.by = 90, 
           xlab = "Days from First Follow-up", 
           ylab = "Cumulative Incidence of Second Follow-up",
           axes.offset = TRUE,
           ylim = c(0, 1),
           tables.theme = theme_void(),
           tables.height = 0.15,
           tables.col = "strata",
           surv.scale = "percent")

These curves look surprisingly similar! Particularly since there was a fairly stark difference in time-to-first follow-up. What to make of this?