---
title: "Decision Tree Herpes Virus Encephalopathy"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Decision Tree Herpes Virus Encephalopathy}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
This is an example that we use to illustrate decision trees in our DARTH teaching materials. The example involves the treatment of herpes simplex ecncephalopathy (HVE) by either treating everyone without biopsy (Treat), not treating or taking biopsy (DoNotTreat), and Taking biopsy and treat if biopsy posivite (Biopsy).
We will first define the twig model and the parameters and then we will show how one would code the model direclty in R and compare the outcomes.
# twig
```{r setup}
library(twig)
```
## twig model definition
```{r}
mytwig <- twig() +
decisions(names=c(DoNotTreat, Treat, Biopsy)) + # treatment options
event(name = DIE, # first event
options = c(yes, none), # either occurs or doesn't occur
probs = c(pDie, leftover), # occurs with prob pDie and doesn't occur with 1-pDie (leftover)
transitions = c(Death, HVE_event)) + # if it occurs, transitions to Death, otherwise the HVE_event
event(name = HVE_event, # similarly, HVE_event occurs with f_HVE but if not it will be OVE
options = c(yes, none),
probs = c(f_HVE, leftover),
transitions = c(get_HVE_comp, get_OVE_comp)) +
event(name = get_HVE_comp, # evaluate whether HVE complications occured
options = c(yes, none),
probs = c(p_comp, leftover),
transitions = c(HVE_comp, no_HVE_comp)) +
event(name = get_OVE_comp, # evaluate whether other viral encephalitis (OVE) complications occured
options = c(yes, none),
probs = c(p_comp, leftover),
transitions = c(OVE_comp, no_OVE_comp)) +
payoffs(names = c(cost, utility)) # finally measure the cost and utilities
```
## DecisionTwig
In [DecisionTwig](https://www.dashlab.ca/projects/decision_twig/), this decision tree looks like this:
{width=700px}
# Model parameters
Define a data frame for model parameters as a list of scalar values. This can also ve a dataset for running a probabilistic analysis.
```{r}
params <- list(
# Probabilities,
p_HVE = 0.52 ,# prevalence of HVE
p_HVE_comp = 0.71 ,# complications with untreated HVE
p_OVE_comp = 0.01 ,# complications with untreated OVE
p_HVE_comp_tx = 0.36 ,# complications with treated HVE
p_OVE_comp_tx = 0.20 ,# complications with treated OVE
p_biopsy_death = 0.005 ,# probability of death due to biopsy
# Costs,
c_VE = 1200 ,# cost of viral encephalitis care without complications
c_VE_comp = 9000 ,# cost of viral encephalitis care with complications
c_tx = 9500 ,# cost of treatment
c_biopsy = 25000 ,# cost of brain biopsy
# QALYs,
q_VE = 20 ,# remaining QALYs for those without VE-related complications
q_VE_comp = 19 ,# remaining QALYs for those with VE-related complications
q_loss_biopsy = 0.01 ,# one-time QALY loss due to brain biopsy
q_death_biopsy = 0 # remaining QALYs for those who died during biopsy
)
```
# Functions
## Probability of death
We only look into death from biopsy in this decision tree.
```{r}
pDie <- function(decision, p_biopsy_death){
p_biopsy_death * (decision == "Biopsy")
}
```
## Probability of complications
This depends on whether treatment received or not, HVE vs. OVE, and whether either HVE or OVE complications occured.
```{r}
p_comp <- function(decision, HVE_event, p_HVE_comp, p_OVE_comp,
p_HVE_comp_tx, p_OVE_comp_tx) {
# complication of untreated HVE
p_HVE_comp * (decision == "DoNotTreat" & HVE_event=="yes") +
# complication of untreated OVE
p_OVE_comp * (decision %in% c("DoNotTreat", "Biopsy") & HVE_event=="none") +
# complications of treated HVE
p_HVE_comp_tx * (decision %in% c("Treat", "Biopsy") & HVE_event=="yes") +
# complications of treated OVE
p_OVE_comp_tx * (decision == "Treat" & HVE_event=="none")
}
```
## Probability of HVE
All event probabilities must be function names. So, we can just create a simple wrapper around the p_HVE variable.
```{r}
f_HVE <- function(p_HVE){
p_HVE
}
```
## Cost function
Cost is a function of the decision and the final model outcomes.
```{r}
cost <- function(decision, outcome, c_biopsy, c_tx, c_VE_comp, c_VE){
# cost of biopsy
c_biopsy*(decision=="Biopsy") +
# cost of treatment if treated or biopsy was +ve for HVE
c_tx*(decision=="Treat" | (decision=="Biopsy" & outcome %in% c("HVE_comp", "no_HVE_comp"))) +
# cost of complication if outcomes are in either HVE or OVE complications
c_VE_comp*(outcome %in% c("HVE_comp", "OVE_comp")) +
# cost of viral encephalitis if complications didn't occur
c_VE*(outcome %in% c("no_HVE_comp", "no_OVE_comp"))
}
```
## utility
Is a function of the decision and final outcome.
```{r}
utility <- function(decision, outcome, q_loss_biopsy, q_VE_comp, q_VE){
# apply utility discount for biopsy
-q_loss_biopsy*(decision=="Biopsy") +
# apply utility values for complications
q_VE_comp*(outcome %in% c("HVE_comp", "OVE_comp")) +
# apply utility values if complications didn't occur
q_VE*(outcome %in% c("no_HVE_comp", "no_OVE_comp"))
}
```
# Running the `twig`
We can run the model and check the results of the single parameter set under `results$sim_ev`
``` {r}
results <- run_twig(twig_obj = mytwig, params = params, parallel = FALSE, progress_bar = FALSE)
results$sim_ev
```
## Incremental Cost-Effectiveness Ratio (ICER)
using the `calculate_icers` function adapted from the dampack package, we can retrieve ICER table by passing the payoffs summary table.
``` {R}
calculate_icers(results$mean_ev)
```
## Summary
This example illustrated the following features of `twig`
* Decision tree
* Multiple decisions
* Multiple sequential events
* probability dependency on prior events
* transition payoffs
* using sequential events to model complications
* ICER
In addition, various intermediate objects and computations can be returned by enabling `verbose`.