In visR-sandbox/visR-survival: A package to wrap functionality for plots, tables and diagrams adhering to graphical principles

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

library(tidyverse)    
library(broom)
library(survival)
library(here)

load data

ADTTE <- read_csv(here("data", 'psi-vissig-adtte.csv'))

Set up meta data for the report

title <- "Evidence of improved progression-free survival for combo over monotherapy"
subtitle <- "Kaplan-Meier estimates over time including 95% uncertainty interval"
source <- "*The number of patients at risk (and events) are displayed the time point reference.
Data source: https://github.com/VIS-SIG/Wonderful-Wednesdays/tree/master/data/2020/2020-04-08"
y_axis <- "Progression free survival"
x_axis <- "Time [days]*"

Survival model for KM and risk set

fit <- survfit(Surv(AVAL, CNSR == 0) ~ TRT01P, data = ADTTE, conf.type = 'log-log' ) 

Specify timepoints

... to summarise risk table at. This uses the survival package helpers.

sumfit <- summary(fit, times = c(0, 250, 500, 750, 1000, 1250, 1500, 1750, 2000)) 

tidy KM curve by treatment for plotting

km <-
  survfit(Surv(AVAL, CNSR == 0) ~ TRT01P,
          data = ADTTE,
          conf.type = 'log-log')  %>%
  broom::tidy(fit) %>%
  dplyr::mutate(group = stringr::str_remove(strata, "TRT01P=")) %>%
  dplyr::mutate(group = factor(
    group,
    levels = c(
      "tablemab + vismab 52 weeks",
      "tablemab x 12 week -> vismab 34 weeks",
      "vismab x 52 weeks",
      "tablemab x 52 weeks"
    )
  ))

tidy KM curve by treatment for small multiples

This is a second data set to pass in for plotting ghost lines of each treatment

km_sm <- km %>%
  mutate(group2 = group)

Calculate risk set for annotations

risk_data <-
  tibble(
    time =  sumfit$time,
    group = sumfit$strata,
    n.risk =   sumfit$n.risk,
    n.events = sumfit$n.event
  ) %>%
  dplyr::mutate(
    group = stringr::str_remove(group, "TRT01P="),
    label = paste0(n.risk, " (", n.events, ")"),
    y_pos = 0.01,
    group2 = group
  )

risk_data %>% glimpse()

Create the plot

km %>% ggplot(aes(x = time, y = estimate, group = group)) +

  ## draw the ghost lines of each treatment by facet. Force the group facet to null
  geom_step(
    data = transform(km_sm, group = NULL),
    aes(x = time, y = estimate, group = group2),
    size = 0.75,
    color = "#000000",
    alpha = 0.15
  ) +

  ## draw km lines
  geom_ribbon(aes(ymin = conf.low, ymax = conf.high),
              alpha = 0.1,
              fill = "red") +
  geom_step(color = "red") +

  ## draw risk set
  geom_text(
    data = risk_data,
    mapping = aes(
      x = time,
      y = y_pos,
      label = label,
      group = group,
      fill = NULL
    ),
    size = 2.5
  ) +

  ## asthetics of the plot
  scale_x_continuous(breaks = c(0, 250, 500, 750, 1000, 1250, 1500, 1750, 2000)) +
  scale_y_continuous(breaks = c(0, 0.25, 0.50, 0.75, 1),
                     limits = c(0, 1)) +

  ## annotations
  labs(title = title,
       subtitle = subtitle,
       caption = source) +
  xlab(x_axis) +
  ylab(y_axis) +

  # set up basic theme
  theme_minimal(base_size = 12) +
  theme(
    panel.grid.minor = element_blank(),
    axis.title.y = element_blank(),
    legend.position = "none"
  ) +
  # Set the entire chart region to a light gray color
  theme(panel.border = element_rect(color = "grey", fill = NA, size = 0.35)) +
  facet_wrap( ~ group, scales = 'free', ncol = 2) 

visR-sandbox/visR-survival documentation built on Sept. 18, 2020, 6:21 p.m.