multi {multinma} | R Documentation |
Multinomial outcome data
Description
This function aids the specification of multinomial outcome data when setting
up a network with set_agd_arm()
or set_ipd()
. It takes a set of columns
(or, more generally, numeric vectors of the same length) of outcome counts in
each category, and binds these together to produce a matrix.
Usage
multi(..., inclusive = FALSE, type = c("ordered", "competing"))
Arguments
... |
Two or more numeric columns (or vectors) of category counts. Argument names (optional) will be used to label the categories. |
inclusive |
Logical, are ordered category counts inclusive ( |
type |
String, indicating whether categories are |
Details
When specifying ordered categorical counts, these can either be
given as exclusive counts (inclusive = FALSE
, the default) where
individuals are only counted in the highest category they achieve, or
inclusive counts (inclusive = TRUE
) where individuals are counted in
every category up to and including the highest category achieved.
(Competing outcomes, by nature, are always specified as exclusive counts.)
NA
values can be used to indicate categories/cutpoints that were not
measured.
Value
A matrix of (exclusive) category counts
Examples
# These two data sets specify the same ordered categorical data for outcomes
# r0 < r1 < r2, but the first uses the "inclusive" format and the second the
# "exclusive" format.
df_inclusive <- tibble::tribble(~r0, ~r1, ~r2,
1, 1, 1,
5, 4, 1,
5, 2, 2,
10, 5, 0,
5, 5, 0,
7, NA, 6, # Achieved r2 or not (no r1)
10, 4, NA) # Achieved r1 or not (no r2)
df_exclusive <- tibble::tribble(~r0, ~r1, ~r2,
0, 0, 1,
1, 3, 1,
3, 0, 2,
5, 5, 0,
0, 5, 0,
1, NA, 6, # Achieved r2 or not (no r1)
6, 4, NA) # Achieved r1 or not (no r2)
(r_inclusive <- with(df_inclusive, multi(r0, r1, r2, inclusive = TRUE)))
(r_exclusive <- with(df_exclusive, multi(r0, r1, r2, inclusive = FALSE)))
# Counts are always stored in exclusive format
stopifnot(isTRUE(all.equal(r_inclusive, r_exclusive)))
## HTA Plaque Psoriasis
library(dplyr)
# Ordered outcomes here are given as "exclusive" counts
head(hta_psoriasis)
# Calculate lowest category count (failure to achieve PASI 50)
pso_dat <- hta_psoriasis %>%
mutate(`PASI<50` = sample_size - rowSums(cbind(PASI50, PASI75, PASI90), na.rm = TRUE))
# Set up network
pso_net <- set_agd_arm(pso_dat,
study = paste(studyc, year),
trt = trtc,
r = multi(`PASI<50`, PASI50, PASI75, PASI90,
inclusive = FALSE,
type = "ordered"))
pso_net