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This vignette describes how EQ-5D descriptive system data can be
summarised and reported using eq5d. It covers the outputs
produced by descriptive_data() and
table_descriptive() and explains how these can be used to
report response distributions across EQ-5D dimensions.
The descriptive_data() function summarises EQ-5D
responses in a tidy format, with one row for each dimension, response
level and metric combination. This format is designed to work naturally
with standard R workflows and is used by the reporting functions
provided in the package.
suppressPackageStartupMessages(library(eq5d))
dat <- read.csv(
system.file("extdata", "eq5d3l_example.csv", package = "eq5d")
)
# Ungrouped example.
dat1 <- subset(dat, Group == "Group1")
dd <- descriptive_data(dat1, version = "3L", metric = "percent")
head(dd)
#> Dimension Level Value Metric
#> 1 MO 1 43 percent
#> 2 MO 2 57 percent
#> 3 MO 3 0 percent
#> 4 SC 1 48 percent
#> 5 SC 2 52 percent
#> 6 SC 3 0 percentThe metric argument controls whether descriptive
summaries are reported as counts or percentages. For example, the
following returns counts rather than percentages:
descriptive_data(dat1, version = "3L", metric = "count")
#> Dimension Level Value Metric
#> 1 MO 1 43 count
#> 2 MO 2 57 count
#> 3 MO 3 0 count
#> 4 SC 1 48 count
#> 5 SC 2 52 count
#> 6 SC 3 0 count
#> 7 UA 1 22 count
#> 8 UA 2 71 count
#> 9 UA 3 7 count
#> 10 PD 1 6 count
#> 11 PD 2 78 count
#> 12 PD 3 16 count
#> 13 AD 1 57 count
#> 14 AD 2 43 count
#> 15 AD 3 0 countDescriptive tables can be created using
table_descriptive(). This function reshapes the output from
descriptive_data() into a format commonly used for
reporting EQ-5D results.
Tables may contain percentages or counts, depending on the metric used when creating the descriptive data.
table_descriptive(dd, include_total = TRUE)
#> Level MO SC UA PD AD
#> 1 1 43 48 22 6 57
#> 2 2 57 52 71 78 43
#> 3 3 0 0 7 16 0
#> 4 Total 100 100 100 100 100In this table, rows represent EQ-5D response levels and columns represent dimensions. The values show the proportion of respondents reporting each level.
Response distributions are often compared across study groups, populations or time points.
When a grouping variable is supplied to
descriptive_data(), summaries are calculated separately for
each group. table_descriptive() then returns a list
containing one table per group.
dd_grp <- descriptive_data(dat, version = "3L", metric = "count", group = "Group")
table_descriptive(dd_grp)
#> $Group1
#> Level MO SC UA PD AD
#> 1 1 43 48 22 6 57
#> 2 2 57 52 71 78 43
#> 3 3 0 0 7 16 0
#> 4 Total 100 100 100 100 100
#>
#> $Group2
#> Level MO SC UA PD AD
#> 1 1 62 72 57 28 74
#> 2 2 38 28 40 66 19
#> 3 3 0 0 3 6 7
#> 4 Total 100 100 100 100 100This approach makes it straightforward to compare descriptive system distributions across groups using a consistent reporting format throughout an analysis.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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