```
################################################################################
# Tests for treatment.R
context('Assigning treatment')
################################################################################
test_that('shifttreatment_indicator works',
{
# First has no shift, second has 15% shift (0.85 HR)
s <- list(matrix(0, nrow=1000, ncol=2),
stageshift_indicator(0.85, 1000, 2))
# Base case has equally distributed groups 1 to 4
b <- matrix(sample.int(4, size=2000, replace=TRUE),
nrow=1000, ncol=2)
# Map of 1:4 onto stage and ER status
m <- matrix(1:4, nrow=2, dimnames=list(c('Early', 'Advanced'),
c('ER+', 'ER-')))
# If type[x]=NA, should return all NA's
expect_equal(sum(is.na(shifttreatment_indicator(x=1, type=c(NA, 2),
s, b, m))), 2000)
ind <- shifttreatment_indicator(x=2, type=c(NA, 2), s, b, m)
expect_true(abs(round(mean(ind)-(0.5*0.15),2))<=0.02)
}
)
test_that('sim_treatment_by_subgroup works',
{
library(bcimodel)
set.seed(98103)
data(ex1)
# Small example
popsize <- 1000
sims <- 100
# Base case has equally distributed groups 1 to 4
b <- matrix(sample.int(4, size=popsize*sims, replace=TRUE),
nrow=popsize, ncol=sims)
# Map of 1:4 onto stage and ER status
m <- ex1[[3]]
# Shifts
s <- lapply(ex1[[1]]$earlydetHR,
stageshift_indicator, pop_size=popsize, nsim=sims)
# Get new stages (advanced cases only)
n <- lapply(s, shift_stages, original=b, map=m)
# Create indicator for shifting treatment (advanced cases only)
st <- lapply(ex1[[1]]$num, shifttreatment_indicator,
type=ex1[[1]]$pairnum, shifts=s, basecase=b, map=m)
# Simulate treatment (for early detection scenarios, candidate
# early-stage treatments for shifted cases)
t <- sim_treatment_by_subgroup(ex1[[4]], n[[1]], 'base', popsize, nsim)
}
)
test_that('treatments_by_policy and update_treat_stageshift work',
{
library(bcimodel)
set.seed(98103)
data(ex1)
# Small example
popsize <- 10
sims <- 5
# Base case has equally distributed groups 1 to 4
b <- matrix(sample.int(4, size=popsize*sims, replace=TRUE),
nrow=popsize, ncol=sims)
# Map of 1:4 onto stage and ER status
m <- ex1$map
# Shifts
s <- lapply(ex1$pol$earlydetHR,
stageshift_indicator, pop_size=popsize, nsim=sims)
# Get new stages (advanced cases only)
n <- lapply(s, shift_stages, original=b, map=m)
# Create indicator for shifting treatment (advanced cases only)
st <- lapply(ex1$pol$num, shifttreatment_indicator,
type=ex1$pol$pairnum, shifts=s, basecase=b, map=m)
# Simulate treatment (for early detection scenarios, candidate
# early-stage treatments for shifted cases)
t <- treatments_by_policy(policies=ex1[[1]], treat_chars=ex1[[4]],
stagegroups=n, map=m, popsize, sims)
####### TEST ONE - TO DO
# Scenarios with early detection should only have early-stage
# treatments
####### TEST TWO - TO DO
# Scenarios with early detection should only have early-stage
####### TEST THREE
# Shift treatment indicator is TRUE only for cases that were
# advanced-stage in the base case
expect_equal(sum(!b[st[[3]]]%in%m['Advanced',]),0)
####### TEST FOUR
# In paired non-earlydet scenario, shift treatment indicator
# is TRUE only for advanced-stage treatments
expect_equal(sum(!t[[2]][st[[3]]]%in%
subset(ex1[[4]], SSno%in%m['Advanced',])$txSSno), 0)
####### TEST FIVE
# New stages for the shifted cases are early stages
expect_equal(
sum(!n[[3]][st[[3]]]%in%m['Early',]), 0
)
####### TEST SIX (similar to TEST FOUR)
# In paired non-earlydet scenario, shift treatment indicator
# is TRUE only for advanced-stage treatments
expect_equal(
sum(!t[[3]][st[[3]]]%in%
subset(ex1[[4]], SSno%in%m['Early',])$txSSno), 0
)
####### TEST SEVEN (similar to TEST FOUR)
# Scenario 3 is where txSSno 1 and 4 have prop=0, so we
# should only see 2 and 3
expect_equal(
sum(!t[[3]][st[[3]]]%in%c(2,3)), 0
)
# Finalize treatments by pairing the non-stage shifted scenario
# with the stage-shifted cases for the early detection scenario
tfinal <- update_treat_stageshift(ex1$pol, shifts=s, treats=t)
####### TEST EIGHT
# Treatments are the same between scenario 2 and 3 for
# non-shifted cases
expect_equal(tfinal[[2]][!s[[3]]], tfinal[[3]][!s[[3]]])
####### TEST NINE
# Treatments are only early-stage for shifted cases in final
expect_equal(
sum(!tfinal[[3]][s[[3]]]%in%c(2,3)), 0
)
}
)
test_that('sim_treatment_by_subgroup works if only 1 treatment', {
# Set up 1-treatment scenario
library(bcimodel)
data(ex1)
ex1$tx <- subset(ex1$tx, txSSid=='None')
ex1$tx <- transform(ex1$tx, txSSno=1:4, base=1, tam=1, tamandshift=1)
# Small example
popsize <- 10
sims <- 5
# Base case has equally distributed groups 1 to 4
b <- matrix(sample.int(4, size=popsize*sims, replace=TRUE),
nrow=popsize, ncol=sims)
# Map of 1:4 onto stage and ER status
m <- ex1[[3]]
# Shifts
s <- lapply(ex1[[1]]$earlydetHR,
stageshift_indicator, pop_size=popsize, nsim=sims)
# Get new stages (advanced cases only)
n <- lapply(s, shift_stages, original=b, map=m)
# Create indicator for shifting treatment (advanced cases only)
st <- lapply(ex1[[1]]$num, shifttreatment_indicator,
type=ex1[[1]]$pairnum, shifts=s, basecase=b, map=m)
# Simulate treatment (for early detection scenarios, candidate
# early-stage treatments for shifted cases)
t <- treatments_by_policy(policies=ex1[[1]], treat_chars=ex1[[4]],
stagegroups=n, map=m, popsize, sims)
# MANUALLY RERAN TESTS FROM ABOVE...
}
```

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