consort_plot {consort}

Self generating consort diagram

Description

Create CONSORT diagram from a participant disposition data.

Usage

consort_plot(
  data,
  orders,
  side_box,
  allocation = NULL,
  labels = NULL,
  cex = 0.8,
  text_width = NULL
)

Arguments

data

Data set with disposition information for each participants.

orders

A named vector or a list, names as the variable in the dataset and values as labels in the box. The order of the diagram will be based on this.

side_box

Variable vector, appeared as side box in the diagram. The next box will be the subset of the missing values of these variables.

allocation

Name of the grouping/treatment variable (optional), the diagram will split into branches on this variables forward.

labels

Named vector, names is the location of the terminal node. The position location should plus 1 after the allocation variables if the allocation is defined.

cex

Multiplier applied to font size, Default is 0.8

text_width

a positive integer giving the target column for wrapping lines in the output. String will not be wrapped if not defined (default). The stri_wrap function will be used if stringi package installed, otherwise strwrap will be used.

Details

The calculation of numbers is as in an analogous to Kirchhoff's Laws of electricity. The numbers in terminal nodes must sum to those in the ancestor nodes. All the drop outs will be populated as a side box. Which was different from the official CONSORT diagram template, which has dropout inside a vertical node.

Value

A consort object.

See Also

add_side_box,add_split, add_side_box textbox

Examples

## Prepare test data
set.seed(1001)
N <- 300

trialno <- sample(c(1000:2000), N)
exc1 <- rep(NA, N)
exc1[sample(1:N, 15)] <- sample(c("Sample not collected", "MRI not collected", "Other"),
  15,
  replace = TRUE, prob = c(0.4, 0.4, 0.2)
)

induc <- rep(NA, N)
induc[is.na(exc1)] <- trialno[is.na(exc1)]

exc2 <- rep(NA, N)
exc2[sample(1:N, 20)] <- sample(c(
  "Sample not collected", "Dead",
  "Other"
), 20,
replace = TRUE,
prob = c(0.4, 0.4, 0.2)
)
exc2[is.na(induc)] <- NA

exc <- ifelse(is.na(exc2), exc1, exc2)

arm <- rep(NA, N)
arm[is.na(exc)] <- sample(c("Conc", "Seq"), sum(is.na(exc)), replace = TRUE)
arm3 <- sample(c("Trt A", "Trt B", "Trt C"), N, replace = TRUE)
arm3[is.na(arm)] <- NA

fow1 <- rep(NA, N)
fow1[!is.na(arm)] <- sample(c("Withdraw", "Discontinued", "Death", "Other", NA),
  sum(!is.na(arm)),
  replace = TRUE,
  prob = c(0.05, 0.05, 0.05, 0.05, 0.8)
)
fow2 <- rep(NA, N)
fow2[!is.na(arm) & is.na(fow1)] <- sample(c("Protocol deviation", "Outcome missing", NA),
  sum(!is.na(arm) & is.na(fow1)),
  replace = TRUE,
  prob = c(0.05, 0.05, 0.9)
)


df <- data.frame(trialno, exc1, induc, exc2, exc, arm, arm3, fow1, fow2)
rm(trialno, exc1, induc, exc2, exc, arm, arm3, fow1, fow2, N)

## Multiple phase
p <- consort_plot(
  data = df,
  order = list(
    trialno = "Population",
    exc1 = "Excluded",
    induc = "Induction",
    exc2 = "Excluded",
    arm3 = "Randomized patient",
    fow1 = "Lost of Follow-up",
    trialno = "Finished Followup",
    fow2 = "Not evaluable",
    trialno = "Final Analysis"
  ),
  side_box = c("exc1", "exc2", "fow1", "fow2"),
  allocation = "arm3",
  labels = c(
    "1" = "Screening", "2" = "Month 4",
    "3" = "Randomization", "5" = "Month 24",
    "6" = "End of study"
  ),
  cex = 0.7
)