มูลค่าคาดหมายของข้อมูลสมบูรณ์: หลักฐานที่ดีขึ้นอาจมีมูลค่าเพียงใด

# Two drugs, four possible worlds: what knowing the truth first is worth to a hospital network
# ยาสองตัว ความจริงที่เป็นไปได้สี่แบบ: การรู้ความจริงก่อนเลือกมีค่าเท่าใดต่อเครือข่ายโรงพยาบาล
# Requires: R >= 4.5.2
# Tested: works in R 4.6.1
# tibble 3.3.1 (stable)
# dplyr 1.2.1 (stable)
# dampack 1.0.2.1000 (stable)
# install.packages(c("tibble", "dplyr", "dampack"))
# update.packages(oldPkgs = c("tibble", "dplyr", "dampack"))
# remotes::install_version("tibble", "3.3.1")
# remotes::install_version("dplyr", "1.2.1")
# remotes::install_version("dampack", "1.0.2.1000")
library(tibble) # Write the four worlds as an explicit input table.
library(dplyr) # Add each world's best value and loss to the table.
library(dampack) # Cross-check the gain from knowing first with an independent routine.
stopifnot(getRversion() >= "4.5.2")

#%%@note 1. The series' shared examples
# A health weight over ten years, the threshold, and the share of reruns that favour a treatment.
# ค่าน้ำหนักสุขภาพตลอดสิบปี เกณฑ์ และสัดส่วนของการรันซ้ำที่เอียงไปทางการรักษา
big <- function(value) prettyNum(round(value, 2), big.mark = ",", digits = 10, scientific = FALSE)
years_lived <- 10
state_weight <- 0.70
lambda <- 160000
mean_gain <- 10000
sd_gain <- 40000
runs <- 1000
stopifnot(round(runs * stats::pnorm(mean_gain / sd_gain)) == 599)

#%%@note 2. Four worlds, both drugs valued in the same world
# Each row is one equally likely version of the truth. Values are thousand THB per patient.
# แต่ละแถวคือความจริงแบบหนึ่งที่มีโอกาสเท่ากัน ค่าเป็นพันบาทต่อผู้ป่วย และเป็นตัวเลขสมมติ
nmb_a <- c(100, 140, 90, 130)
nmb_b <- c(120, 110, 150, 100)
worlds <- tibble::tibble(world = seq_along(nmb_a), drug_a = nmb_a, drug_b = nmb_b) |>
  dplyr::mutate(best = pmax(drug_a, drug_b), loss_with_b = best - drug_b)
averages <- c(A = mean(nmb_a), B = mean(nmb_b))
choose_now <- max(averages)
row_best <- pmax(nmb_a, nmb_b)
know_first <- mean(row_best)
stopifnot(know_first - choose_now == mean(worlds$loss_with_b))

#%%@note 3. What choosing B loses, then two checks
# Every A value lower by 10 keeps the same losing worlds. Sorting each column breaks the worlds.
# ลด A ลง 10 ทุกแถวยังแพ้ในโลกเดิม ส่วนการเรียงแต่ละคอลัมน์แยกกันทำให้โลกแตกออกจากกัน
shift_a <- 10
loss_b <- pmax(nmb_a, nmb_b) - nmb_b
lower_a <- nmb_a - shift_a
loss_lower <- pmax(lower_a, nmb_b) - nmb_b
sorted_best <- pmax(sort(nmb_a), sort(nmb_b))
stopifnot(sum(loss_lower > 0) == sum(loss_b > 0), mean(sorted_best) < know_first)

#%%@note 4. Cross-check with an independent routine
# dampack values each strategy as effect x wtp - cost, so effect = value, cost = 0 and wtp = 1.
# dampack คิดค่าเป็น effect x wtp - cost จึงให้ effect เป็นค่า ให้ cost เป็น 0 และ wtp เป็น 1
zero_cost <- data.frame(A = 0 * nmb_a, B = 0 * nmb_b)
psa_obj <- dampack::make_psa_obj(cost = zero_cost, effectiveness = data.frame(A = nmb_a, B = nmb_b),
  parameters = data.frame(world = seq_along(nmb_a)), strategies = c("A", "B"))
checked <- dampack::calc_evpi(psa_obj, wtp = 1)
stopifnot(abs(checked[[2]] - (know_first - choose_now)) < 1e-9)

#%%@note 5. From one patient to the network, in today's money
# Patients reached over the years the choice stands, then each year divided by 1.03 once more.
# จำนวนผู้ป่วยตลอดช่วงที่การเลือกนี้ใช้อยู่ แล้วหารค่าของแต่ละปีด้วย 1.03 เพิ่มอีกหนึ่งครั้ง
per_patient <- 15000
per_year <- 2000
years_used <- 5
reached <- per_year * years_used
yearly_factor <- 1.03
trial_cost <- 40
dividers <- yearly_factor^(seq_len(years_used) - 1)
today <- per_year * per_patient / 1e6 / dividers
stopifnot(per_patient == 1000 * (know_first - choose_now), trial_cost < sum(today))

#%%@note 6. Numbers the article quotes
# Print the short boxes' lines in their reading order.
# พิมพ์ผลลัพธ์ตรงกับกล่องโค้ดสั้นตามลำดับในบทความ
cat("10 years at 0.70 =", formatC(years_lived * state_weight, format = "f", digits = 1),
    "| full health adds", formatC(years_lived * 0.30, format = "f", digits = 1), "\n")
cat("Series example: 160000 x 1.00 - 150000 =", big(lambda * 1.00 - 150000), "THB\n")
cat("Average extra gain", big(mean_gain), "THB, spread", big(sd_gain), "THB | runs favouring it:",
    round(runs * stats::pnorm(mean_gain / sd_gain)), "of", big(runs), "\n")
cat("Average value, A and B:", averages, "| choose now: drug", names(which.max(averages)), "\n")
cat("Better drug in each world:", ifelse(nmb_a > nmb_b, "A", "B"), "\n")
cat("Best value in each world:", row_best, "| their average:", know_first, "\n")
cat("Knowing first:", know_first, "- choosing now:", choose_now, "=", know_first - choose_now, "\n")
cat("Loss from choosing B in each world:", loss_b, "| average:", mean(loss_b), "\n")
cat("Worlds where B loses:", sum(loss_b > 0), "of", length(loss_b),
    "| share (%):", 100 * mean(loss_b > 0), "\n")
cat("A lower by 10: average", mean(lower_a), "| losses", loss_lower, "| average:",
    mean(loss_lower), "\n")
cat("Sorted A:", sort(nmb_a), "| sorted B:", sort(nmb_b), "| best in each row:", sorted_best, "\n")
cat("Their average:", mean(sorted_best), "| gap over choosing now:",
    mean(sorted_best) - max(mean(nmb_a), mean(nmb_b)), "\n")
cat("Patients reached:", big(per_year), "x", years_used, "=", big(reached), "\n")
cat("Each year:", big(per_year), "x", big(per_patient), "THB =", big(per_year * per_patient / 1e6),
    "million THB\n")
cat("Ceiling:", big(reached), "x", big(per_patient), "THB =", big(reached * per_patient / 1e6),
    "million THB\n")
cat("Series: 1,834 THB due in 3 years at (%):", 3, "->", round(1834 / yearly_factor^3), "THB\n")
cat("Dividers, year by year:", formatC(dividers, format = "f", digits = 4), "\n")
cat("Each year in today's money (million THB):", formatC(today, format = "f", digits = 2), "\n")
cat("Ceiling today (million THB):", formatC(sum(today), format = "f", digits = 2), "\n")
cat("Trial (million THB):", trial_cost, "| below the ceiling:", trial_cost < sum(today), "\n")

# Two drugs, four possible worlds: what knowing the truth first is worth to a hospital network
# ยาสองตัว ความจริงที่เป็นไปได้สี่แบบ: การรู้ความจริงก่อนเลือกมีค่าเท่าใดต่อเครือข่ายโรงพยาบาล
# Requires: R >= 4.5.2
# Tested: works in R 4.6.1
# tibble 3.3.1 (stable)
# dplyr 1.2.1 (stable)
# dampack 1.0.2.1000 (stable)
# install.packages(c("tibble", "dplyr", "dampack"))
# update.packages(oldPkgs = c("tibble", "dplyr", "dampack"))
# remotes::install_version("tibble", "3.3.1")
# remotes::install_version("dplyr", "1.2.1")
# remotes::install_version("dampack", "1.0.2.1000")
library(tibble) # Write the four worlds as an explicit input table.
library(dplyr) # Add each world's best value and loss to the table.
library(dampack) # Cross-check the gain from knowing first with an independent routine.
stopifnot(getRversion() >= "4.5.2")

#%%@note 1. The series' shared examples
# A health weight over ten years, the threshold, and the share of reruns that favour a treatment.
# ค่าน้ำหนักสุขภาพตลอดสิบปี เกณฑ์ และสัดส่วนของการรันซ้ำที่เอียงไปทางการรักษา
big <- function(value) prettyNum(round(value, 2), big.mark = ",", digits = 10, scientific = FALSE)
years_lived <- 10
state_weight <- 0.70
lambda <- 160000
mean_gain <- 10000
sd_gain <- 40000
runs <- 1000
stopifnot(round(runs * stats::pnorm(mean_gain / sd_gain)) == 599)

#%%@note 2. Four worlds, both drugs valued in the same world
# Each row is one equally likely version of the truth. Values are thousand THB per patient.
# แต่ละแถวคือความจริงแบบหนึ่งที่มีโอกาสเท่ากัน ค่าเป็นพันบาทต่อผู้ป่วย และเป็นตัวเลขสมมติ
nmb_a <- c(100, 140, 90, 130)
nmb_b <- c(120, 110, 150, 100)
worlds <- tibble::tibble(world = seq_along(nmb_a), drug_a = nmb_a, drug_b = nmb_b) |>
  dplyr::mutate(best = pmax(drug_a, drug_b), loss_with_b = best - drug_b)
averages <- c(A = mean(nmb_a), B = mean(nmb_b))
choose_now <- max(averages)
row_best <- pmax(nmb_a, nmb_b)
know_first <- mean(row_best)
stopifnot(know_first - choose_now == mean(worlds$loss_with_b))

#%%@note 3. What choosing B loses, then two checks
# Every A value lower by 10 keeps the same losing worlds. Sorting each column breaks the worlds.
# ลด A ลง 10 ทุกแถวยังแพ้ในโลกเดิม ส่วนการเรียงแต่ละคอลัมน์แยกกันทำให้โลกแตกออกจากกัน
shift_a <- 10
loss_b <- pmax(nmb_a, nmb_b) - nmb_b
lower_a <- nmb_a - shift_a
loss_lower <- pmax(lower_a, nmb_b) - nmb_b
sorted_best <- pmax(sort(nmb_a), sort(nmb_b))
stopifnot(sum(loss_lower > 0) == sum(loss_b > 0), mean(sorted_best) < know_first)

#%%@note 4. Cross-check with an independent routine
# dampack values each strategy as effect x wtp - cost, so effect = value, cost = 0 and wtp = 1.
# dampack คิดค่าเป็น effect x wtp - cost จึงให้ effect เป็นค่า ให้ cost เป็น 0 และ wtp เป็น 1
zero_cost <- data.frame(A = 0 * nmb_a, B = 0 * nmb_b)
psa_obj <- dampack::make_psa_obj(cost = zero_cost, effectiveness = data.frame(A = nmb_a, B = nmb_b),
  parameters = data.frame(world = seq_along(nmb_a)), strategies = c("A", "B"))
checked <- dampack::calc_evpi(psa_obj, wtp = 1)
stopifnot(abs(checked[[2]] - (know_first - choose_now)) < 1e-9)

#%%@note 5. From one patient to the network, in today's money
# Patients reached over the years the choice stands, then each year divided by 1.03 once more.
# จำนวนผู้ป่วยตลอดช่วงที่การเลือกนี้ใช้อยู่ แล้วหารค่าของแต่ละปีด้วย 1.03 เพิ่มอีกหนึ่งครั้ง
per_patient <- 15000
per_year <- 2000
years_used <- 5
reached <- per_year * years_used
yearly_factor <- 1.03
trial_cost <- 40
dividers <- yearly_factor^(seq_len(years_used) - 1)
today <- per_year * per_patient / 1e6 / dividers
stopifnot(per_patient == 1000 * (know_first - choose_now), trial_cost < sum(today))

#%%@note 6. Numbers the article quotes
# Print the short boxes' lines in their reading order.
# พิมพ์ผลลัพธ์ตรงกับกล่องโค้ดสั้นตามลำดับในบทความ
cat("10 years at 0.70 =", formatC(years_lived * state_weight, format = "f", digits = 1),
    "| full health adds", formatC(years_lived * 0.30, format = "f", digits = 1), "\n")
cat("Series example: 160000 x 1.00 - 150000 =", big(lambda * 1.00 - 150000), "THB\n")
cat("Average extra gain", big(mean_gain), "THB, spread", big(sd_gain), "THB | runs favouring it:",
    round(runs * stats::pnorm(mean_gain / sd_gain)), "of", big(runs), "\n")
cat("Average value, A and B:", averages, "| choose now: drug", names(which.max(averages)), "\n")
cat("Better drug in each world:", ifelse(nmb_a > nmb_b, "A", "B"), "\n")
cat("Best value in each world:", row_best, "| their average:", know_first, "\n")
cat("Knowing first:", know_first, "- choosing now:", choose_now, "=", know_first - choose_now, "\n")
cat("Loss from choosing B in each world:", loss_b, "| average:", mean(loss_b), "\n")
cat("Worlds where B loses:", sum(loss_b > 0), "of", length(loss_b),
    "| share (%):", 100 * mean(loss_b > 0), "\n")
cat("A lower by 10: average", mean(lower_a), "| losses", loss_lower, "| average:",
    mean(loss_lower), "\n")
cat("Sorted A:", sort(nmb_a), "| sorted B:", sort(nmb_b), "| best in each row:", sorted_best, "\n")
cat("Their average:", mean(sorted_best), "| gap over choosing now:",
    mean(sorted_best) - max(mean(nmb_a), mean(nmb_b)), "\n")
cat("Patients reached:", big(per_year), "x", years_used, "=", big(reached), "\n")
cat("Each year:", big(per_year), "x", big(per_patient), "THB =", big(per_year * per_patient / 1e6),
    "million THB\n")
cat("Ceiling:", big(reached), "x", big(per_patient), "THB =", big(reached * per_patient / 1e6),
    "million THB\n")
cat("Series: 1,834 THB due in 3 years at (%):", 3, "->", round(1834 / yearly_factor^3), "THB\n")
cat("Dividers, year by year:", formatC(dividers, format = "f", digits = 4), "\n")
cat("Each year in today's money (million THB):", formatC(today, format = "f", digits = 2), "\n")
cat("Ceiling today (million THB):", formatC(sum(today), format = "f", digits = 2), "\n")
cat("Trial (million THB):", trial_cost, "| below the ceiling:", trial_cost < sum(today), "\n")

# Two drugs, four possible worlds: what knowing the truth first is worth to a hospital network
# ยาสองตัว ความจริงที่เป็นไปได้สี่แบบ: การรู้ความจริงก่อนเลือกมีค่าเท่าใดต่อเครือข่ายโรงพยาบาล
# Requires: R >= 4.5.2
# Tested: works in R 4.6.1
# tibble 3.3.1 (stable)
# dplyr 1.2.1 (stable)
# dampack 1.0.2.1000 (stable)
# install.packages(c("tibble", "dplyr", "dampack"))
# update.packages(oldPkgs = c("tibble", "dplyr", "dampack"))
# remotes::install_version("tibble", "3.3.1")
# remotes::install_version("dplyr", "1.2.1")
# remotes::install_version("dampack", "1.0.2.1000")
library(tibble) # Write the four worlds as an explicit input table.
library(dplyr) # Add each world's best value and loss to the table.
library(dampack) # Cross-check the gain from knowing first with an independent routine.
stopifnot(getRversion() >= "4.5.2")

#%%@note 1. The series' shared examples
# A health weight over ten years, the threshold, and the share of reruns that favour a treatment.
# ค่าน้ำหนักสุขภาพตลอดสิบปี เกณฑ์ และสัดส่วนของการรันซ้ำที่เอียงไปทางการรักษา
big <- function(value) prettyNum(round(value, 2), big.mark = ",", digits = 10, scientific = FALSE)
years_lived <- 10
state_weight <- 0.70
lambda <- 160000
mean_gain <- 10000
sd_gain <- 40000
runs <- 1000
stopifnot(round(runs * stats::pnorm(mean_gain / sd_gain)) == 599)

#%%@note 2. Four worlds, both drugs valued in the same world
# Each row is one equally likely version of the truth. Values are thousand THB per patient.
# แต่ละแถวคือความจริงแบบหนึ่งที่มีโอกาสเท่ากัน ค่าเป็นพันบาทต่อผู้ป่วย และเป็นตัวเลขสมมติ
nmb_a <- c(100, 140, 90, 130)
nmb_b <- c(120, 110, 150, 100)
worlds <- tibble::tibble(world = seq_along(nmb_a), drug_a = nmb_a, drug_b = nmb_b) |>
  dplyr::mutate(best = pmax(drug_a, drug_b), loss_with_b = best - drug_b)
averages <- c(A = mean(nmb_a), B = mean(nmb_b))
choose_now <- max(averages)
row_best <- pmax(nmb_a, nmb_b)
know_first <- mean(row_best)
stopifnot(know_first - choose_now == mean(worlds$loss_with_b))

#%%@note 3. What choosing B loses, then two checks
# Every A value lower by 10 keeps the same losing worlds. Sorting each column breaks the worlds.
# ลด A ลง 10 ทุกแถวยังแพ้ในโลกเดิม ส่วนการเรียงแต่ละคอลัมน์แยกกันทำให้โลกแตกออกจากกัน
shift_a <- 10
loss_b <- pmax(nmb_a, nmb_b) - nmb_b
lower_a <- nmb_a - shift_a
loss_lower <- pmax(lower_a, nmb_b) - nmb_b
sorted_best <- pmax(sort(nmb_a), sort(nmb_b))
stopifnot(sum(loss_lower > 0) == sum(loss_b > 0), mean(sorted_best) < know_first)

#%%@note 4. Cross-check with an independent routine
# dampack values each strategy as effect x wtp - cost, so effect = value, cost = 0 and wtp = 1.
# dampack คิดค่าเป็น effect x wtp - cost จึงให้ effect เป็นค่า ให้ cost เป็น 0 และ wtp เป็น 1
zero_cost <- data.frame(A = 0 * nmb_a, B = 0 * nmb_b)
psa_obj <- dampack::make_psa_obj(cost = zero_cost, effectiveness = data.frame(A = nmb_a, B = nmb_b),
  parameters = data.frame(world = seq_along(nmb_a)), strategies = c("A", "B"))
checked <- dampack::calc_evpi(psa_obj, wtp = 1)
stopifnot(abs(checked[[2]] - (know_first - choose_now)) < 1e-9)

#%%@note 5. From one patient to the network, in today's money
# Patients reached over the years the choice stands, then each year divided by 1.03 once more.
# จำนวนผู้ป่วยตลอดช่วงที่การเลือกนี้ใช้อยู่ แล้วหารค่าของแต่ละปีด้วย 1.03 เพิ่มอีกหนึ่งครั้ง
per_patient <- 15000
per_year <- 2000
years_used <- 5
reached <- per_year * years_used
yearly_factor <- 1.03
trial_cost <- 40
dividers <- yearly_factor^(seq_len(years_used) - 1)
today <- per_year * per_patient / 1e6 / dividers
stopifnot(per_patient == 1000 * (know_first - choose_now), trial_cost < sum(today))

#%%@note 6. Numbers the article quotes
# Print the short boxes' lines in their reading order.
# พิมพ์ผลลัพธ์ตรงกับกล่องโค้ดสั้นตามลำดับในบทความ
cat("10 years at 0.70 =", formatC(years_lived * state_weight, format = "f", digits = 1),
    "| full health adds", formatC(years_lived * 0.30, format = "f", digits = 1), "\n")
cat("Series example: 160000 x 1.00 - 150000 =", big(lambda * 1.00 - 150000), "THB\n")
cat("Average extra gain", big(mean_gain), "THB, spread", big(sd_gain), "THB | runs favouring it:",
    round(runs * stats::pnorm(mean_gain / sd_gain)), "of", big(runs), "\n")
cat("Average value, A and B:", averages, "| choose now: drug", names(which.max(averages)), "\n")
cat("Better drug in each world:", ifelse(nmb_a > nmb_b, "A", "B"), "\n")
cat("Best value in each world:", row_best, "| their average:", know_first, "\n")
cat("Knowing first:", know_first, "- choosing now:", choose_now, "=", know_first - choose_now, "\n")
cat("Loss from choosing B in each world:", loss_b, "| average:", mean(loss_b), "\n")
cat("Worlds where B loses:", sum(loss_b > 0), "of", length(loss_b),
    "| share (%):", 100 * mean(loss_b > 0), "\n")
cat("A lower by 10: average", mean(lower_a), "| losses", loss_lower, "| average:",
    mean(loss_lower), "\n")
cat("Sorted A:", sort(nmb_a), "| sorted B:", sort(nmb_b), "| best in each row:", sorted_best, "\n")
cat("Their average:", mean(sorted_best), "| gap over choosing now:",
    mean(sorted_best) - max(mean(nmb_a), mean(nmb_b)), "\n")
cat("Patients reached:", big(per_year), "x", years_used, "=", big(reached), "\n")
cat("Each year:", big(per_year), "x", big(per_patient), "THB =", big(per_year * per_patient / 1e6),
    "million THB\n")
cat("Ceiling:", big(reached), "x", big(per_patient), "THB =", big(reached * per_patient / 1e6),
    "million THB\n")
cat("Series: 1,834 THB due in 3 years at (%):", 3, "->", round(1834 / yearly_factor^3), "THB\n")
cat("Dividers, year by year:", formatC(dividers, format = "f", digits = 4), "\n")
cat("Each year in today's money (million THB):", formatC(today, format = "f", digits = 2), "\n")
cat("Ceiling today (million THB):", formatC(sum(today), format = "f", digits = 2), "\n")
cat("Trial (million THB):", trial_cost, "| below the ceiling:", trial_cost < sum(today), "\n")

# Two drugs, four possible worlds: what knowing the truth first is worth to a hospital network
# ยาสองตัว ความจริงที่เป็นไปได้สี่แบบ: การรู้ความจริงก่อนเลือกมีค่าเท่าใดต่อเครือข่ายโรงพยาบาล
# Requires: R >= 4.5.2
# Tested: works in R 4.6.1
# tibble 3.3.1 (stable)
# dplyr 1.2.1 (stable)
# dampack 1.0.2.1000 (stable)
# install.packages(c("tibble", "dplyr", "dampack"))
# update.packages(oldPkgs = c("tibble", "dplyr", "dampack"))
# remotes::install_version("tibble", "3.3.1")
# remotes::install_version("dplyr", "1.2.1")
# remotes::install_version("dampack", "1.0.2.1000")
library(tibble) # Write the four worlds as an explicit input table.
library(dplyr) # Add each world's best value and loss to the table.
library(dampack) # Cross-check the gain from knowing first with an independent routine.
stopifnot(getRversion() >= "4.5.2")

#%%@note 1. The series' shared examples
# A health weight over ten years, the threshold, and the share of reruns that favour a treatment.
# ค่าน้ำหนักสุขภาพตลอดสิบปี เกณฑ์ และสัดส่วนของการรันซ้ำที่เอียงไปทางการรักษา
big <- function(value) prettyNum(round(value, 2), big.mark = ",", digits = 10, scientific = FALSE)
years_lived <- 10
state_weight <- 0.70
lambda <- 160000
mean_gain <- 10000
sd_gain <- 40000
runs <- 1000
stopifnot(round(runs * stats::pnorm(mean_gain / sd_gain)) == 599)

#%%@note 2. Four worlds, both drugs valued in the same world
# Each row is one equally likely version of the truth. Values are thousand THB per patient.
# แต่ละแถวคือความจริงแบบหนึ่งที่มีโอกาสเท่ากัน ค่าเป็นพันบาทต่อผู้ป่วย และเป็นตัวเลขสมมติ
nmb_a <- c(100, 140, 90, 130)
nmb_b <- c(120, 110, 150, 100)
worlds <- tibble::tibble(world = seq_along(nmb_a), drug_a = nmb_a, drug_b = nmb_b) |>
  dplyr::mutate(best = pmax(drug_a, drug_b), loss_with_b = best - drug_b)
averages <- c(A = mean(nmb_a), B = mean(nmb_b))
choose_now <- max(averages)
row_best <- pmax(nmb_a, nmb_b)
know_first <- mean(row_best)
stopifnot(know_first - choose_now == mean(worlds$loss_with_b))

#%%@note 3. What choosing B loses, then two checks
# Every A value lower by 10 keeps the same losing worlds. Sorting each column breaks the worlds.
# ลด A ลง 10 ทุกแถวยังแพ้ในโลกเดิม ส่วนการเรียงแต่ละคอลัมน์แยกกันทำให้โลกแตกออกจากกัน
shift_a <- 10
loss_b <- pmax(nmb_a, nmb_b) - nmb_b
lower_a <- nmb_a - shift_a
loss_lower <- pmax(lower_a, nmb_b) - nmb_b
sorted_best <- pmax(sort(nmb_a), sort(nmb_b))
stopifnot(sum(loss_lower > 0) == sum(loss_b > 0), mean(sorted_best) < know_first)

#%%@note 4. Cross-check with an independent routine
# dampack values each strategy as effect x wtp - cost, so effect = value, cost = 0 and wtp = 1.
# dampack คิดค่าเป็น effect x wtp - cost จึงให้ effect เป็นค่า ให้ cost เป็น 0 และ wtp เป็น 1
zero_cost <- data.frame(A = 0 * nmb_a, B = 0 * nmb_b)
psa_obj <- dampack::make_psa_obj(cost = zero_cost, effectiveness = data.frame(A = nmb_a, B = nmb_b),
  parameters = data.frame(world = seq_along(nmb_a)), strategies = c("A", "B"))
checked <- dampack::calc_evpi(psa_obj, wtp = 1)
stopifnot(abs(checked[[2]] - (know_first - choose_now)) < 1e-9)

#%%@note 5. From one patient to the network, in today's money
# Patients reached over the years the choice stands, then each year divided by 1.03 once more.
# จำนวนผู้ป่วยตลอดช่วงที่การเลือกนี้ใช้อยู่ แล้วหารค่าของแต่ละปีด้วย 1.03 เพิ่มอีกหนึ่งครั้ง
per_patient <- 15000
per_year <- 2000
years_used <- 5
reached <- per_year * years_used
yearly_factor <- 1.03
trial_cost <- 40
dividers <- yearly_factor^(seq_len(years_used) - 1)
today <- per_year * per_patient / 1e6 / dividers
stopifnot(per_patient == 1000 * (know_first - choose_now), trial_cost < sum(today))

#%%@note 6. Numbers the article quotes
# Print the short boxes' lines in their reading order.
# พิมพ์ผลลัพธ์ตรงกับกล่องโค้ดสั้นตามลำดับในบทความ
cat("10 years at 0.70 =", formatC(years_lived * state_weight, format = "f", digits = 1),
    "| full health adds", formatC(years_lived * 0.30, format = "f", digits = 1), "\n")
cat("Series example: 160000 x 1.00 - 150000 =", big(lambda * 1.00 - 150000), "THB\n")
cat("Average extra gain", big(mean_gain), "THB, spread", big(sd_gain), "THB | runs favouring it:",
    round(runs * stats::pnorm(mean_gain / sd_gain)), "of", big(runs), "\n")
cat("Average value, A and B:", averages, "| choose now: drug", names(which.max(averages)), "\n")
cat("Better drug in each world:", ifelse(nmb_a > nmb_b, "A", "B"), "\n")
cat("Best value in each world:", row_best, "| their average:", know_first, "\n")
cat("Knowing first:", know_first, "- choosing now:", choose_now, "=", know_first - choose_now, "\n")
cat("Loss from choosing B in each world:", loss_b, "| average:", mean(loss_b), "\n")
cat("Worlds where B loses:", sum(loss_b > 0), "of", length(loss_b),
    "| share (%):", 100 * mean(loss_b > 0), "\n")
cat("A lower by 10: average", mean(lower_a), "| losses", loss_lower, "| average:",
    mean(loss_lower), "\n")
cat("Sorted A:", sort(nmb_a), "| sorted B:", sort(nmb_b), "| best in each row:", sorted_best, "\n")
cat("Their average:", mean(sorted_best), "| gap over choosing now:",
    mean(sorted_best) - max(mean(nmb_a), mean(nmb_b)), "\n")
cat("Patients reached:", big(per_year), "x", years_used, "=", big(reached), "\n")
cat("Each year:", big(per_year), "x", big(per_patient), "THB =", big(per_year * per_patient / 1e6),
    "million THB\n")
cat("Ceiling:", big(reached), "x", big(per_patient), "THB =", big(reached * per_patient / 1e6),
    "million THB\n")
cat("Series: 1,834 THB due in 3 years at (%):", 3, "->", round(1834 / yearly_factor^3), "THB\n")
cat("Dividers, year by year:", formatC(dividers, format = "f", digits = 4), "\n")
cat("Each year in today's money (million THB):", formatC(today, format = "f", digits = 2), "\n")
cat("Ceiling today (million THB):", formatC(sum(today), format = "f", digits = 2), "\n")
cat("Trial (million THB):", trial_cost, "| below the ceiling:", trial_cost < sum(today), "\n")

# Two drugs, four possible worlds: what knowing the truth first is worth to a hospital network
# ยาสองตัว ความจริงที่เป็นไปได้สี่แบบ: การรู้ความจริงก่อนเลือกมีค่าเท่าใดต่อเครือข่ายโรงพยาบาล
# Requires: R >= 4.5.2
# Tested: works in R 4.6.1
# tibble 3.3.1 (stable)
# dplyr 1.2.1 (stable)
# dampack 1.0.2.1000 (stable)
# install.packages(c("tibble", "dplyr", "dampack"))
# update.packages(oldPkgs = c("tibble", "dplyr", "dampack"))
# remotes::install_version("tibble", "3.3.1")
# remotes::install_version("dplyr", "1.2.1")
# remotes::install_version("dampack", "1.0.2.1000")
library(tibble) # Write the four worlds as an explicit input table.
library(dplyr) # Add each world's best value and loss to the table.
library(dampack) # Cross-check the gain from knowing first with an independent routine.
stopifnot(getRversion() >= "4.5.2")

#%%@note 1. The series' shared examples
# A health weight over ten years, the threshold, and the share of reruns that favour a treatment.
# ค่าน้ำหนักสุขภาพตลอดสิบปี เกณฑ์ และสัดส่วนของการรันซ้ำที่เอียงไปทางการรักษา
big <- function(value) prettyNum(round(value, 2), big.mark = ",", digits = 10, scientific = FALSE)
years_lived <- 10
state_weight <- 0.70
lambda <- 160000
mean_gain <- 10000
sd_gain <- 40000
runs <- 1000
stopifnot(round(runs * stats::pnorm(mean_gain / sd_gain)) == 599)

#%%@note 2. Four worlds, both drugs valued in the same world
# Each row is one equally likely version of the truth. Values are thousand THB per patient.
# แต่ละแถวคือความจริงแบบหนึ่งที่มีโอกาสเท่ากัน ค่าเป็นพันบาทต่อผู้ป่วย และเป็นตัวเลขสมมติ
nmb_a <- c(100, 140, 90, 130)
nmb_b <- c(120, 110, 150, 100)
worlds <- tibble::tibble(world = seq_along(nmb_a), drug_a = nmb_a, drug_b = nmb_b) |>
  dplyr::mutate(best = pmax(drug_a, drug_b), loss_with_b = best - drug_b)
averages <- c(A = mean(nmb_a), B = mean(nmb_b))
choose_now <- max(averages)
row_best <- pmax(nmb_a, nmb_b)
know_first <- mean(row_best)
stopifnot(know_first - choose_now == mean(worlds$loss_with_b))

#%%@note 3. What choosing B loses, then two checks
# Every A value lower by 10 keeps the same losing worlds. Sorting each column breaks the worlds.
# ลด A ลง 10 ทุกแถวยังแพ้ในโลกเดิม ส่วนการเรียงแต่ละคอลัมน์แยกกันทำให้โลกแตกออกจากกัน
shift_a <- 10
loss_b <- pmax(nmb_a, nmb_b) - nmb_b
lower_a <- nmb_a - shift_a
loss_lower <- pmax(lower_a, nmb_b) - nmb_b
sorted_best <- pmax(sort(nmb_a), sort(nmb_b))
stopifnot(sum(loss_lower > 0) == sum(loss_b > 0), mean(sorted_best) < know_first)

#%%@note 4. Cross-check with an independent routine
# dampack values each strategy as effect x wtp - cost, so effect = value, cost = 0 and wtp = 1.
# dampack คิดค่าเป็น effect x wtp - cost จึงให้ effect เป็นค่า ให้ cost เป็น 0 และ wtp เป็น 1
zero_cost <- data.frame(A = 0 * nmb_a, B = 0 * nmb_b)
psa_obj <- dampack::make_psa_obj(cost = zero_cost, effectiveness = data.frame(A = nmb_a, B = nmb_b),
  parameters = data.frame(world = seq_along(nmb_a)), strategies = c("A", "B"))
checked <- dampack::calc_evpi(psa_obj, wtp = 1)
stopifnot(abs(checked[[2]] - (know_first - choose_now)) < 1e-9)

#%%@note 5. From one patient to the network, in today's money
# Patients reached over the years the choice stands, then each year divided by 1.03 once more.
# จำนวนผู้ป่วยตลอดช่วงที่การเลือกนี้ใช้อยู่ แล้วหารค่าของแต่ละปีด้วย 1.03 เพิ่มอีกหนึ่งครั้ง
per_patient <- 15000
per_year <- 2000
years_used <- 5
reached <- per_year * years_used
yearly_factor <- 1.03
trial_cost <- 40
dividers <- yearly_factor^(seq_len(years_used) - 1)
today <- per_year * per_patient / 1e6 / dividers
stopifnot(per_patient == 1000 * (know_first - choose_now), trial_cost < sum(today))

#%%@note 6. Numbers the article quotes
# Print the short boxes' lines in their reading order.
# พิมพ์ผลลัพธ์ตรงกับกล่องโค้ดสั้นตามลำดับในบทความ
cat("10 years at 0.70 =", formatC(years_lived * state_weight, format = "f", digits = 1),
    "| full health adds", formatC(years_lived * 0.30, format = "f", digits = 1), "\n")
cat("Series example: 160000 x 1.00 - 150000 =", big(lambda * 1.00 - 150000), "THB\n")
cat("Average extra gain", big(mean_gain), "THB, spread", big(sd_gain), "THB | runs favouring it:",
    round(runs * stats::pnorm(mean_gain / sd_gain)), "of", big(runs), "\n")
cat("Average value, A and B:", averages, "| choose now: drug", names(which.max(averages)), "\n")
cat("Better drug in each world:", ifelse(nmb_a > nmb_b, "A", "B"), "\n")
cat("Best value in each world:", row_best, "| their average:", know_first, "\n")
cat("Knowing first:", know_first, "- choosing now:", choose_now, "=", know_first - choose_now, "\n")
cat("Loss from choosing B in each world:", loss_b, "| average:", mean(loss_b), "\n")
cat("Worlds where B loses:", sum(loss_b > 0), "of", length(loss_b),
    "| share (%):", 100 * mean(loss_b > 0), "\n")
cat("A lower by 10: average", mean(lower_a), "| losses", loss_lower, "| average:",
    mean(loss_lower), "\n")
cat("Sorted A:", sort(nmb_a), "| sorted B:", sort(nmb_b), "| best in each row:", sorted_best, "\n")
cat("Their average:", mean(sorted_best), "| gap over choosing now:",
    mean(sorted_best) - max(mean(nmb_a), mean(nmb_b)), "\n")
cat("Patients reached:", big(per_year), "x", years_used, "=", big(reached), "\n")
cat("Each year:", big(per_year), "x", big(per_patient), "THB =", big(per_year * per_patient / 1e6),
    "million THB\n")
cat("Ceiling:", big(reached), "x", big(per_patient), "THB =", big(reached * per_patient / 1e6),
    "million THB\n")
cat("Series: 1,834 THB due in 3 years at (%):", 3, "->", round(1834 / yearly_factor^3), "THB\n")
cat("Dividers, year by year:", formatC(dividers, format = "f", digits = 4), "\n")
cat("Each year in today's money (million THB):", formatC(today, format = "f", digits = 2), "\n")
cat("Ceiling today (million THB):", formatC(sum(today), format = "f", digits = 2), "\n")
cat("Trial (million THB):", trial_cost, "| below the ceiling:", trial_cost < sum(today), "\n")

# Two drugs, four possible worlds: what knowing the truth first is worth to a hospital network
# ยาสองตัว ความจริงที่เป็นไปได้สี่แบบ: การรู้ความจริงก่อนเลือกมีค่าเท่าใดต่อเครือข่ายโรงพยาบาล
# Requires: R >= 4.5.2
# Tested: works in R 4.6.1
# tibble 3.3.1 (stable)
# dplyr 1.2.1 (stable)
# dampack 1.0.2.1000 (stable)
# install.packages(c("tibble", "dplyr", "dampack"))
# update.packages(oldPkgs = c("tibble", "dplyr", "dampack"))
# remotes::install_version("tibble", "3.3.1")
# remotes::install_version("dplyr", "1.2.1")
# remotes::install_version("dampack", "1.0.2.1000")
library(tibble) # Write the four worlds as an explicit input table.
library(dplyr) # Add each world's best value and loss to the table.
library(dampack) # Cross-check the gain from knowing first with an independent routine.
stopifnot(getRversion() >= "4.5.2")

#%%@note 1. The series' shared examples
# A health weight over ten years, the threshold, and the share of reruns that favour a treatment.
# ค่าน้ำหนักสุขภาพตลอดสิบปี เกณฑ์ และสัดส่วนของการรันซ้ำที่เอียงไปทางการรักษา
big <- function(value) prettyNum(round(value, 2), big.mark = ",", digits = 10, scientific = FALSE)
years_lived <- 10
state_weight <- 0.70
lambda <- 160000
mean_gain <- 10000
sd_gain <- 40000
runs <- 1000
stopifnot(round(runs * stats::pnorm(mean_gain / sd_gain)) == 599)

#%%@note 2. Four worlds, both drugs valued in the same world
# Each row is one equally likely version of the truth. Values are thousand THB per patient.
# แต่ละแถวคือความจริงแบบหนึ่งที่มีโอกาสเท่ากัน ค่าเป็นพันบาทต่อผู้ป่วย และเป็นตัวเลขสมมติ
nmb_a <- c(100, 140, 90, 130)
nmb_b <- c(120, 110, 150, 100)
worlds <- tibble::tibble(world = seq_along(nmb_a), drug_a = nmb_a, drug_b = nmb_b) |>
  dplyr::mutate(best = pmax(drug_a, drug_b), loss_with_b = best - drug_b)
averages <- c(A = mean(nmb_a), B = mean(nmb_b))
choose_now <- max(averages)
row_best <- pmax(nmb_a, nmb_b)
know_first <- mean(row_best)
stopifnot(know_first - choose_now == mean(worlds$loss_with_b))

#%%@note 3. What choosing B loses, then two checks
# Every A value lower by 10 keeps the same losing worlds. Sorting each column breaks the worlds.
# ลด A ลง 10 ทุกแถวยังแพ้ในโลกเดิม ส่วนการเรียงแต่ละคอลัมน์แยกกันทำให้โลกแตกออกจากกัน
shift_a <- 10
loss_b <- pmax(nmb_a, nmb_b) - nmb_b
lower_a <- nmb_a - shift_a
loss_lower <- pmax(lower_a, nmb_b) - nmb_b
sorted_best <- pmax(sort(nmb_a), sort(nmb_b))
stopifnot(sum(loss_lower > 0) == sum(loss_b > 0), mean(sorted_best) < know_first)

#%%@note 4. Cross-check with an independent routine
# dampack values each strategy as effect x wtp - cost, so effect = value, cost = 0 and wtp = 1.
# dampack คิดค่าเป็น effect x wtp - cost จึงให้ effect เป็นค่า ให้ cost เป็น 0 และ wtp เป็น 1
zero_cost <- data.frame(A = 0 * nmb_a, B = 0 * nmb_b)
psa_obj <- dampack::make_psa_obj(cost = zero_cost, effectiveness = data.frame(A = nmb_a, B = nmb_b),
  parameters = data.frame(world = seq_along(nmb_a)), strategies = c("A", "B"))
checked <- dampack::calc_evpi(psa_obj, wtp = 1)
stopifnot(abs(checked[[2]] - (know_first - choose_now)) < 1e-9)

#%%@note 5. From one patient to the network, in today's money
# Patients reached over the years the choice stands, then each year divided by 1.03 once more.
# จำนวนผู้ป่วยตลอดช่วงที่การเลือกนี้ใช้อยู่ แล้วหารค่าของแต่ละปีด้วย 1.03 เพิ่มอีกหนึ่งครั้ง
per_patient <- 15000
per_year <- 2000
years_used <- 5
reached <- per_year * years_used
yearly_factor <- 1.03
trial_cost <- 40
dividers <- yearly_factor^(seq_len(years_used) - 1)
today <- per_year * per_patient / 1e6 / dividers
stopifnot(per_patient == 1000 * (know_first - choose_now), trial_cost < sum(today))

#%%@note 6. Numbers the article quotes
# Print the short boxes' lines in their reading order.
# พิมพ์ผลลัพธ์ตรงกับกล่องโค้ดสั้นตามลำดับในบทความ
cat("10 years at 0.70 =", formatC(years_lived * state_weight, format = "f", digits = 1),
    "| full health adds", formatC(years_lived * 0.30, format = "f", digits = 1), "\n")
cat("Series example: 160000 x 1.00 - 150000 =", big(lambda * 1.00 - 150000), "THB\n")
cat("Average extra gain", big(mean_gain), "THB, spread", big(sd_gain), "THB | runs favouring it:",
    round(runs * stats::pnorm(mean_gain / sd_gain)), "of", big(runs), "\n")
cat("Average value, A and B:", averages, "| choose now: drug", names(which.max(averages)), "\n")
cat("Better drug in each world:", ifelse(nmb_a > nmb_b, "A", "B"), "\n")
cat("Best value in each world:", row_best, "| their average:", know_first, "\n")
cat("Knowing first:", know_first, "- choosing now:", choose_now, "=", know_first - choose_now, "\n")
cat("Loss from choosing B in each world:", loss_b, "| average:", mean(loss_b), "\n")
cat("Worlds where B loses:", sum(loss_b > 0), "of", length(loss_b),
    "| share (%):", 100 * mean(loss_b > 0), "\n")
cat("A lower by 10: average", mean(lower_a), "| losses", loss_lower, "| average:",
    mean(loss_lower), "\n")
cat("Sorted A:", sort(nmb_a), "| sorted B:", sort(nmb_b), "| best in each row:", sorted_best, "\n")
cat("Their average:", mean(sorted_best), "| gap over choosing now:",
    mean(sorted_best) - max(mean(nmb_a), mean(nmb_b)), "\n")
cat("Patients reached:", big(per_year), "x", years_used, "=", big(reached), "\n")
cat("Each year:", big(per_year), "x", big(per_patient), "THB =", big(per_year * per_patient / 1e6),
    "million THB\n")
cat("Ceiling:", big(reached), "x", big(per_patient), "THB =", big(reached * per_patient / 1e6),
    "million THB\n")
cat("Series: 1,834 THB due in 3 years at (%):", 3, "->", round(1834 / yearly_factor^3), "THB\n")
cat("Dividers, year by year:", formatC(dividers, format = "f", digits = 4), "\n")
cat("Each year in today's money (million THB):", formatC(today, format = "f", digits = 2), "\n")
cat("Ceiling today (million THB):", formatC(sum(today), format = "f", digits = 2), "\n")
cat("Trial (million THB):", trial_cost, "| below the ceiling:", trial_cost < sum(today), "\n")