In tgerke/ggconsort: Create CONSORT Diagrams

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ggconsort

Overview

The goal of ggconsort is to provide convenience functions for creating CONSORT diagrams with ggplot2. ggconsort segments CONSORT creation into two stages: (1) counting and annotation at the time of data wrangling, and (2) diagram layout and aesthetic design. With the introduction of a ggconsort_cohort class, stage (1) can be accomplished within dplyr chains. Specifically, the following functions are implemented inside a dplyr chain to define a ggconsort_cohort:

• cohort_start() initializes a ggconsort_cohort object which contains a labeled copy of the source data
• cohort_define() constructs cohorts that are variations of the source data or other cohorts
• cohort_label() adds labels to each named cohort within the ggconsort_cohort object

Stage 2 makes use of three ggconsort consort_ verbs which equip the ggconsort_cohort object with ggconsort properties. The ggconsort object can be viewed with ggplot via geom_consort() + theme_consort(). plot and print methods are also available for the ggconsort object for visually iterative development.

• consort_box_add() adds a text box to the CONSORT diagram
• consort_arrow_add() adds an arrow to the CONSORT diagram
• consort_line_add() adds a line (without an arrow head) the CONSORT diagram

Installation

You can install the released version of ggconsort from GitHub with:

# install.packages("devtools")
devtools::install_github("tgerke/ggconsort")

Usage

To demonstrate usage, we use a simulated dataset within ggconsort called trial_data, which contains 1200 patients who were approached to participate in a randomized trial comparing Drug A to Drug B.

library(ggconsort)

head(trial_data)

Of the 1200 approached patients, only a subset were ultimately randomized: some declined to participate or were ineligible (due to prior chemotherapy or bone metastasis). We will use ggconsort verbs and geoms to count the patient flow and represent the process in a CONSORT diagram.

We first define the ggconsort_cohort object (study_cohorts) in the following dplyr chain.

library(dplyr)

study_cohorts <- 
  trial_data %>%
  cohort_start("Assessed for eligibility") %>%
  # Define cohorts using named expressions --------------------
  # Notice that you can use previously defined cohorts in subsequent steps
  cohort_define(
    consented = .full %>% filter(declined != 1),
    consented_chemonaive = consented %>% filter(prior_chemo != 1),
    randomized = consented_chemonaive %>% filter(bone_mets != 1),
    treatment_a = randomized %>% filter(treatment == "Drug A"),
    treatment_b = randomized %>% filter(treatment == "Drug B"),
    # anti_join is useful for counting exclusions -------------
    excluded = anti_join(.full, randomized, by = "id"),
    excluded_declined = anti_join(.full, consented, by = "id"),
    excluded_chemo = anti_join(consented, consented_chemonaive, by = "id"),
    excluded_mets = anti_join(consented_chemonaive, randomized, by = "id")
  ) %>%
  # Provide text labels for cohorts ---------------------------
  cohort_label(
    consented = "Consented",
    consented_chemonaive = "Chemotherapy naive",
    randomized = "Randomized",
    treatment_a = "Allocated to arm A",
    treatment_b = "Allocated to arm B",
    excluded = "Excluded",
    excluded_declined = "Declined to participate",
    excluded_chemo = "Prior chemotherapy",
    excluded_mets = "Bone metastasis"
  )

We can have a look at the study_cohorts object with its print and summary methods:

study_cohorts

summary(study_cohorts)

Next, we add CONSORT "boxes" and "arrows" with appropriate ggconsort verbs, and plot with the CONSORT diagram ggplot. Note the use of cohort_count_adorn(), which is a convenience function that glues cohort counts to their labels.

library(ggplot2)

study_consort <- study_cohorts %>%
  consort_box_add(
    "full", 0, 50, cohort_count_adorn(study_cohorts, .full)
  ) %>%
  consort_box_add(
    "exclusions", 20, 40, glue::glue(
      '{cohort_count_adorn(study_cohorts, excluded)}<br>
      • {cohort_count_adorn(study_cohorts, excluded_declined)}<br>
      • {cohort_count_adorn(study_cohorts, excluded_chemo)}<br>
      • {cohort_count_adorn(study_cohorts, excluded_mets)}
      ')
  ) %>%
  consort_box_add(
    "randomized", 0, 30, cohort_count_adorn(study_cohorts, randomized)
  ) %>%
  consort_box_add(
    "arm_a", -30, 10, cohort_count_adorn(study_cohorts, treatment_a)
  ) %>%
  consort_box_add(
    "arm_b", 30, 10, cohort_count_adorn(study_cohorts, treatment_b)
  ) %>%
  consort_arrow_add(
    end = "exclusions", end_side = "left", start_x = 0, start_y = 40
  ) %>%
  consort_arrow_add(
    "full", "bottom", "randomized", "top"
  ) %>% 
  consort_arrow_add(
    start_x = 0, start_y = 30, end_x = 0, end_y = 20,
  ) %>%
  consort_line_add(
    start_x = -30, start_y = 20, end_x = 30, end_y = 20,
  ) %>% 
  consort_arrow_add(
    end = "arm_a", end_side = "top", start_x = -30, start_y = 20
  ) %>%
  consort_arrow_add(
    end = "arm_b", end_side = "top", start_x = 30, start_y = 20
  )

study_consort %>%
  ggplot() + 
  geom_consort() +
  theme_consort(margin_h = 8, margin_v = 1) +
  # you can include other ggplot geoms, as needed -------------
  ggtext::geom_richtext(
    aes(x = 0, y = 10, label = "Allocation"),
    fill = "#9bc0fc"
  )

At this point, we are ready for analysis. The following retrieves the desired data frame of randomized subjects:

study_cohorts %>%
  cohort_pull(randomized)

tgerke/ggconsort documentation built on Dec. 23, 2021, 8:49 a.m.