Multinomial outcome data

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 (TRUE) or exclusive (FALSE)? Default FALSE. Only used when type = "ordered". See details.

type

String, indicating whether categories are "ordered" or "competing". Currently only ordered categorical outcomes are supported by the modelling functions in this package.

Value

A matrix of (exclusive) category counts

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.

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)))
#>      r0 r1 r2
#> [1,]  0  0  1
#> [2,]  1  3  1
#> [3,]  3  0  2
#> [4,]  5  5  0
#> [5,]  0  5  0
#> [6,]  1 NA  6
#> [7,]  6  4 NA
#> attr(,"class")
#> [1] "multi_ordered" "matrix"        "array"        
(r_exclusive <- with(df_exclusive, multi(r0, r1, r2, inclusive = FALSE)))
#>      r0 r1 r2
#> [1,]  0  0  1
#> [2,]  1  3  1
#> [3,]  3  0  2
#> [4,]  5  5  0
#> [5,]  0  5  0
#> [6,]  1 NA  6
#> [7,]  6  4 NA
#> attr(,"class")
#> [1] "multi_ordered" "matrix"        "array"        

# 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)
#>   studyn   studyc year trtn             trtc sample_size PASI50 PASI75 PASI90
#> 1      1  Elewski 2004    1  Supportive care         193     12      5      1
#> 2      1  Elewski 2004    2 Etanercept 25 mg         196     59     46     21
#> 3      1  Elewski 2004    3 Etanercept 50 mg         194     54     56     40
#> 4      2 Gottlieb 2003    1  Supportive care          55      5      1      0
#> 5      2 Gottlieb 2003    2 Etanercept 25 mg          57     23     11      6
#> 6      3  Lebwohl 2003    1  Supportive care         122     13      5      1

# 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
#> A network with 16 AgD studies (arm-based).
#> 
#> ------------------------------------------------------- AgD studies (arm-based) ---- 
#>  Study         Treatment arms                                          
#>  ACD2058g 2004 2: Supportive care | Efalizumab                         
#>  ACD2600g 2004 2: Supportive care | Efalizumab                         
#>  Altmeyer 1994 2: Supportive care | Fumaderm                           
#>  Chaudari 2001 2: Supportive care | Infliximab                         
#>  Elewski 2004  3: Supportive care | Etanercept 25 mg | Etanercept 50 mg
#>  Ellis 1991    3: Supportive care | Ciclosporin | Ciclosporin          
#>  Gordon 2003   2: Supportive care | Efalizumab                         
#>  Gottlieb 2003 2: Supportive care | Etanercept 25 mg                   
#>  Gottlieb 2004 3: Supportive care | Infliximab | Infliximab            
#>  Guenther 1991 2: Supportive care | Ciclosporin                        
#>  ... plus 6 more studies
#> 
#>  Outcome type: ordered (4 categories)
#> ------------------------------------------------------------------------------------
#> Total number of treatments: 8
#> Total number of studies: 16
#> Reference treatment is: Supportive care
#> Network is connected