## ----setup, include = FALSE----------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.width=6.5, fig.height=6.5
)
library("confinterpret")
## ----include=FALSE-------------------------------------------------------
#' Arrange an interpretation_set object into a table, for printing etc.
tabulate_interpretation_set <- function(interpretation_set, name) {
int_md <- as.data.frame(sapply(interpretation_set$interpretations,
"[[", "interpretation_md"))
rownames(int_md) <- LETTERS[1 : nrow(int_md)]
colnames(int_md) <- name
int_md
}
table_nums <- captioner::captioner(prefix = "Table")
figure_nums <- captioner::captioner(prefix = "Figure")
## ----echo=FALSE, results="asis"------------------------------------------
number_boundaries <- 1 : 5
number_regions <- number_boundaries + 1L
number_interpretations <- as.integer(number_regions * (number_regions + 1L) / 2)
numbers <- data.frame(number_boundaries,
number_regions,
number_interpretations,
row.names = NULL)
colnames(numbers) <- c("Number of boundaries, $n_b$",
"Number of regions, $n_r$",
"Number of interpretations")
knitr::kable(numbers,
caption = table_nums(name = "numbers_b_r_i",
caption = "Number of regions and interpretations for each number of boundaries."))
## ----echo=FALSE, results="asis"------------------------------------------
region_orders <- function(number_regions) {
number_interpretations <- number_regions * (number_regions + 1) / 2
lower_regions <- rep(1 : number_regions, number_regions : 1)
upper_regions <- unlist(mapply(function(x) { x : number_regions },
1 : number_regions))
regions <- data.frame(1 : number_interpretations,
paste("Region", lower_regions),
paste("Region", upper_regions))
colnames(regions) <- c("Order",
"Lower confidence level",
"Upper confidence level")
rownames(regions) <- LETTERS[1 : number_interpretations]
return(regions)
}
boundary_2 <- region_orders(2)
knitr::kable(boundary_2,
caption = table_nums(name = "order_2",
caption = "Order of interpretations with two regions."))
## ----echo=FALSE, results="asis"------------------------------------------
boundary_3 <- region_orders(3)
knitr::kable(boundary_3,
caption = table_nums(name = "order_3",
caption = "Order of interpretations with three regions."))
## ----echo=FALSE----------------------------------------------------------
dummy_i_s_label <- figure_nums("dummy-interpretation-set-plot", "Plot of a 'dummy' `interpretation_set` object, providing visual support for use while drafting interpretations.")
## ----dummy-interpretation-set-plot, fig.cap=dummy_i_s_label--------------
practical_superiority <- interpretation_set(
boundary_names =c("Actual null", "Practical null"),
placeholders = c(comparison_intervention = "$comp",
tested_intervention = "$test"),
interpretations = list(
# A
list(interpretation_short = "A",
interpretation = "A",
interpretation_md = "A"),
# B
list(interpretation_short = "B",
interpretation = "B",
interpretation_md = "B"),
# C
list(interpretation_short = "C",
interpretation = "C",
interpretation_md = "C"),
# D
list(interpretation_short = "D",
interpretation = "D",
interpretation_md = "D"),
# E
list(interpretation_short = "E",
interpretation = "E",
interpretation_md = "E"),
# F
list(interpretation_short = "F",
interpretation = "F",
interpretation_md = "F")))
# Set a nice colour scheme
grDevices::palette(RColorBrewer::brewer.pal(3,"RdYlBu"))
plot(practical_superiority)
## ----echo=FALSE----------------------------------------------------------
new_i_s_label <- figure_nums("new-interpretation-set-plot", "Plot of the newly-defined `interpretation_set` object, showing short versions of the drafted interpretations.")
## ----new-interpretation-set-plot, fig.cap=new_i_s_label------------------
practical_superiority <- interpretation_set(
boundary_names =c("Actual null", "Practical null"),
placeholders = c(comparison_intervention = "$comp",
tested_intervention = "$test"),
interpretations = list(
# A
list(interpretation_short = "Inferior",
interpretation = "$test inferior to $comp",
interpretation_md = "$test **inferior** to $comp"),
# B
list(interpretation_short = "Not practically superior",
interpretation = "$test not practically superior to $comp",
interpretation_md = "$test **not practically superior** to $comp"),
# C
list(interpretation_short = "Inconclusive",
interpretation = paste("Inconclusive: $test not shown to",
"be inferior or superior to $comp"),
interpretation_md = paste("**Inconclusive**: $test not shown to",
"be inferior or superior to $comp")),
# D
list(interpretation_short = "Not practically superior",
interpretation = "$test not practically superior to $comp",
interpretation_md = "$test **not practically superior** to $comp"),
# E
list(interpretation_short = "Inconclusive",
interpretation = paste("Inconclusive: $test not inferior",
"to $comp, but not shown to be",
"practically superior"),
interpretation_md = paste("**Inconclusive**: $test not inferior",
"to $comp, but not shown to be",
"practically superior")),
# F
list(interpretation_short = "Superior",
interpretation = paste("$test superior to $comp, to a",
"practically relevant extent"),
interpretation_md = paste("$test **superior** to $comp, to a",
"practically relevant extent"))))
plot(practical_superiority)
## ----echo=FALSE, results="asis"------------------------------------------
knitr::kable(tabulate_interpretation_set(practical_superiority,
"Practical superiority interpretations."),
caption = "Practical superiority interpretations.")
## ------------------------------------------------------------------------
estimate_gateway <- c("prevalence difference" = 0.1032195)
ci_gateway <- matrix(c(0.04777727, 0.2064016),
nrow = 1,
dimnames = list("estimate", c("2.5 %", "97.5 %")))
estimate_specialist <- c("prevalence difference" = 0.1270894)
ci_specialist <- matrix(c(0.0296644, 0.1767746),
nrow = 1,
dimnames = list("estimate", c("2.5 %", "97.5 %")))
## ----echo=FALSE----------------------------------------------------------
re_int_label <- figure_nums("re-interpret-plot", "Plot showing a study result, re-interpreted using the newly-defined `interpretation_set` object.")
## ----re-interpret-plot, fig.height=5.5, fig.cap=re_int_label-------------
specialist_prac_sup <- confinterpret(ci_specialist,
practical_superiority,
boundaries = c(0, 0.05),
comparison_labels =
c(comparison_intervention =
"Existing standard letter",
tested_intervention = "New letter"))
plot(specialist_prac_sup)
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