In InnovationValueInitiative/IVI-RA: The IVI-RA model

library("iviRA")
load("output.RData")

Available treatments

The primary purpose of the model is to simulate health and cost outcomes for patients with RA treated as a function of the treatments use. Since patients typically use multiple treatments over a lifetime, the model is capable of simulating a treatment sequence of any arbitrary length. Treatments that can be included in a sequence are cDMARDs, bDMARDs, and JAK/STAT inhibitors. We refer to bDMARDs and JAK/STAT inhibitors collectively as targeted DMARDs (tDMARDs).

A dataset of available treatments loads with the iviRA package. The name, mname, and sname columns are long-form, medium-form, and short-form names, respectively. The column route refers to route of administration; if a treatment is a combination therapy, then all routes of administration in the combination are listed. Finally, approval_date is the date that a treatment was approved by the FDA in the US and years_since_approval is the number of years (from the last update of the model) since the treatment was approved.

iviRA::treatments

Selecting treatments

To run the model, we must specify a treatment sequence. The treatment sequence can consist of a single sequence of treatments for all patients in the population or can vary for each patient.

For example, suppose that we want to simulate a treatment sequence of three biologics: adalimumab + methotrexate, etanercept + methotrexate, and infliximab + methotrexate. They should be specified as a vector with elements matching the sname in iviRA::treatments. For comparison purposes, we might want to also simulate outcomes for a non-biologic treatment sequence of only cDMARDs.

txseq1 <- c("adamtx", "etnmtx", "ifxmtx")
txseq2 <- c("cdmards")

In some cases, it might also be useful to allow treatments to differ across patients. This could be useful when, for example, comparing treatment strategies that tailor treatment to individuals against one size fits all strategies. This can be done by creating a matrix of treatment sequences where the number of rows is equal to the number of patients in the cohort. Below we consider a scenario where some patients receive one of two sequences:

adalimumab + methotrexate -> etanercept + methotrexate -> infliximab + methotrexate
etanercept + methotrexate -> adalimumab + methotrexate -> infliximab + methotrexate

txseq.mat <- matrix(c("adamtx", "etnmtx", "ifxmtx", "etnmtx", "adamtx", "ifxmtx"), 
                     nrow = nrow(pop), ncol = 3, byrow = TRUE)
head(txseq.mat)
nrow(txseq.mat)
nrow(pop)