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# Budget impact analysis {#bia}

```{r setup5, include=FALSE, echo = FALSE, message = FALSE, warning=FALSE}
knitr::opts_chunk$set(message = FALSE)
rm(list = ls())  # delete everything that is in R's memory
options(scipen=1000) # removal of scientific notation

path <- "C:/Users/PouwelsXGLV/Documents/Maastricht/Eribulin CEA/Model final_Gamma/" #set location of model, to adapt!
setwd(paste(path,"report", sep = "")) # set working directory to File Source


#load package
library(rms)
library(survival)
library(muhaz)
library(survminer)
library(ggplot2)
library(data.table)
library(printr)
library(dplyr)
library(flexsurv)
library(plyr)
library(tidyverse)
library(scales)
library(matrixStats)
library(reshape2)

# load functions
source(paste(path,"R/01_fun_analyses.R", sep = ""))
source(paste(path,"R/03_fun_dowsa.R", sep = ""))
source(paste(path,"R/03_fun_scenarios.R", sep = ""))
source(paste(path,"R/03_fun_psa_inputs.R", sep = ""))
source(paste(path,"R/03_fun_threshold.R", sep = ""))

# load model inputs
load(paste(path,"data/01_model_inputs.R", sep = ""))
```

This Section presents the calculation underlying the budget impact analysis (BIA).

## Annual budget impact
```{r bia-inputs, echo = T}
scal.inci.pt   <- 14882 	#number of incident patients in 2018: https://www.iknl.nl/nkr-cijfers?fs%7Cepidemiologie_id=7&fs%7Ctumor_id=369&fs%7Cregio_id=135&fs%7Cgedrag_id=12&fs%7Cperiode_id=86%2C87%2C88%2C89%2C90%2C91%2C92%2C93%2C94%2C95%2C96%2C97%2C98%2C99%2C100%2C101%2C102%2C103%2C104%2C105%2C106%2C107%2C108%2C109%2C110%2C111%2C112%2C113%2C114%2C115&fs%7Cgeslacht_id=16&fs%7Cleeftijdsgroep_id=76%2C40%2C41%2C42%2C43%2C44%2C45&fs%7Cjaren_na_diagnose_id=18&fs%7Ceenheid_id=2&cs%7Ctype=column&cs%7CxAxis=periode_id&cs%7Cseries=leeftijdsgroep_id&ts%7CrowDimensions=periode_id&ts%7CcolumnDimensions=leeftijdsgroep_id&lang%7Clanguage=nl

scal.pr.i.abc  <- 0.05    #proportion metastatic patients at diagnosis ("incident" advanced breast cancer (ABC)  patients): https://www.zorginstituutnederland.nl/publicaties/adviezen/2017/04/11/pakketadvies-palbociclib
scal.pr.d.abc <- 0.2    #proportion of patients who will develop ABC: https://www.zorginstituutnederland.nl/publicaties/adviezen/2017/04/11/pakketadvies-palbociclib

scal.pr.eli.eri <- (45+74)/721 #proportion of eligible patients for eribulin in the entire population since the introduction of eribulin

scal.eri <- 15/32 # proportion of eligible patients who actually received eribulin after 01-01-2016 (15 is the number of patients who received eribulin, 32 is the number of eligible patients)

n.abc.pt <- ceiling(scal.inci.pt*scal.pr.i.abc + (1-scal.pr.i.abc)*scal.pr.d.abc*scal.inci.pt) #number of ABC patients per year

n.eli.pt <- ceiling(n.abc.pt*scal.pr.eli.eri)

c.weekly.eri  <- 686 #weekly costs of eribulin, based on SONABRE
c.weekly.comp <- 303 #weekly costs of non-eribulin, based on SONABRE

n.weeks.eri   <- 15.6 #number of weeks eribulin is administered
n.weeks.comp  <- 12.5 #number of weeks non-eribulin is administered
```

The code chunck above declares the model inputs of the BIA. For this analysis, we estimated an annual incidence of 3,572 patients diagnosed with advanced breast cancer (ABC) in the Netherlands, based on the following assumptions:  
- The annual incidence of patients with primary breast cancer is `r format(scal.inci.pt,big.mark=",",scientific=FALSE)` patient per year in the Netherlands (@IKL2018)  
- `r round(scal.pr.i.abc*100,0)`% of patients have incident *de novo* ABC  
- `r round(scal.pr.d.abc*100,0)`% of patients will develop ABC later on  
We assumed that `r round(scal.pr.eli.eri*scal.eri*100,0)`% of patients with ABC will receive eribulin treatment  
- `r round(scal.pr.eli.eri*100,0)`% of ABC patients are eligible for eribulin (hence, these patients were diagnosed with ABC, received at least 2 chemotherapy in the advanced setting and received at least a previous anthracycle- and taxane-based chemotherapy)  
- `r round(scal.eri*100,0)`% of eligible patients for eribulin actually received eribulin  
- We assumed that the total costs per patients are  &euro;`r c.weekly.eri*n.weeks.eri` for patients receiving eribulin and &euro;`r c.weekly.comp*n.weeks.comp` for patients receiving non-eribulin chemotherapy. Eribulin and non-eribulin are administered for `r n.weeks.eri` and `r n.weeks.comp` weeks (on average) respectively, and their cost per week is respectively &euro;`r c.weekly.eri` and &euro;`r c.weekly.comp` (source: SONABRE). 

Based on above-mentioned assumptions, the budget impact of eribulin is &euro;`r round((ceiling(n.eli.pt*scal.eri)*c.weekly.eri*n.weeks.eri + ceiling(n.eli.pt*(1-scal.eri))*c.weekly.comp*n.weeks.comp - n.eli.pt*c.weekly.comp*n.weeks.comp)/1000000,1)` million, see Table \@ref(tab:bia-pt) for the number of patients calculations and Table \@ref(tab:bia-res) for the budget impact analysis.  

Table: (\#tab:bia-pt) Calculation number of eligible patients for eribulin  

|       BIA assumptions     |       Estimate      |   Source and calculations    |
|:--------------------------|:--------------------|:-----------------------------|
|A. Number of incident breast cancer patients|`r format(scal.inci.pt,big.mark=",",scientific=FALSE)`|@IKL2018|
|B. Percentage of patients diagnosed with incident ABC| `r round(scal.pr.i.abc*100,0)`%|Assumption based on @ZIN2017|
|C. Percentage of patients who will develop ABC|`r round(scal.pr.d.abc*100,0)`%|Assumption based on @ZIN2017|
|D. Number of ABC patients|`r n.abc.pt`|A x B + A x (1-B) x C|
|E. Percentage ABC patients eligible for eribulin|`r round(scal.pr.eli.eri*100,0)`%| SONABRE|
|F. Number of eligible patients for eribulin|`r n.eli.pt`|D x E|

Table: (\#tab:bia-res) Budget impact calculations  

|       Calculations|   Eribulin group   |   Non-eribulin group    |
|:------------------|:-------------------|:------------------------|
|Number of eligible patients|`r n.eli.pt`|`r n.eli.pt`|
|Proportion of patients receiving eribulin|`r round(scal.eri*100,0)`%|0%|
|Number of patients receiving eribulin|`r ceiling(n.eli.pt*scal.eri)`|0|
|Number of patients receiving non-eribulin chemotherapy|`r ceiling(n.eli.pt*(1-scal.eri))`|`r n.eli.pt`|
|Costs of eribulin treatment|&euro;`r round(ceiling(n.eli.pt*scal.eri)*c.weekly.eri*n.weeks.eri/1000000,1)` million|&euro;0|
|Costs of non-eribulin treatment|&euro;`r round(ceiling(n.eli.pt*(1-scal.eri))*c.weekly.comp*n.weeks.comp/1000000,1)` million|&euro;`r round(n.eli.pt*c.weekly.comp*n.weeks.comp/1000000,1)` million|
Budget impact|&euro;`r round((ceiling(n.eli.pt*scal.eri)*c.weekly.eri*n.weeks.eri + ceiling(n.eli.pt*(1-scal.eri))*c.weekly.comp*n.weeks.comp - n.eli.pt*c.weekly.comp*n.weeks.comp)/1000000,1)` million|Not applicable|

## Scenario analysis
In Table \@ref(tab:bia-scen), the budget impact was calculated with assuming an uptake of eribulin of 100% in the eligible population.

Table: (\#tab:bia-scen) Budget impact calculation, scenario assuming 100% uptake of eribulin   

|       Calculations|   Eribulin group   |   Non-eribulin group    |
|:------------------|:-------------------|:------------------------|
|Number of eligible patients|`r n.eli.pt`|`r n.eli.pt`|
|Proportion of patients receiving eribulin|100%|0%|
|Number of patients receiving eribulin|`r n.eli.pt`|0|
|Number of patients receiving non-eribulin chemotherapy|0|`r n.eli.pt`|
|Costs of eribulin treatment|&euro;`r round(n.eli.pt*c.weekly.eri*n.weeks.eri/1000000,1)` million|&euro;0|
|Costs of non-eribulin treatment|&euro;0|&euro;`r round(n.eli.pt*c.weekly.comp*n.weeks.comp/1000000,1)` million|
Budget impact|&euro;`r round((n.eli.pt*c.weekly.eri*n.weeks.eri - n.eli.pt*c.weekly.comp*n.weeks.comp)/1000000,1)` million|Not applicable|
