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.

##                                   name        mname    sname
##  1:                            cDMARDs      cDMARDs  cdmards
##  2:        abatacept IV + methotrexate ABT IV + MTX abtivmtx
##  3:          adalimumab + methotrexate    ADA + MTX   adamtx
##  4:                         adalimumab          ADA      ada
##  5:          etanercept + methotrexate    ETN + MTX   etnmtx
##  6:                         etanercept          ETN      etn
##  7:           golimumab + methotrexate    GOL + MTX   golmtx
##  8:          infliximab + methotrexate    IFX + MTX   ifxmtx
##  9:                            placebo      Placebo  placebo
## 10:         tocilizumab + methotrexate    TCZ + MTX   tczmtx
## 11:                        tocilizumab          TCZ      tcz
## 12:  certolizumab pegol + methotrexate    CZP + MTX   czpmtx
## 13:        abatacept SC + methotrexate ABT SC + MTX abtscmtx
## 14:                       non-biologic          NBT      nbt
## 15:           rituximab + methotrexate    RTX + MTX   rtxmtx
## 16: tofacitinib citrate + methotrexate    TOF + MTX   tofmtx
## 17:                          rituximab          RTX      rtx
## 18:                tofacitinib citrate          TOF      tof
## 19:                 certolizumab pegol          CZP      czp
## 20:                          golimumab          GOL      gol
##                  route approval_date years_since_approval
##  1:          injection      12/31/88            28.809296
##  2: infusion/injection      12/23/05            11.855092
##  3:          injection      12/31/02            14.829802
##  4:          injection      12/31/02            14.829802
##  5:          injection       11/2/98            18.985646
##  6:          injection       11/2/98            18.985646
##  7:          injection       4/24/09             8.524949
##  8: infusion/injection      11/10/99            17.965824
##  9:               none      12/31/88            28.809296
## 10:          injection      10/21/13             4.038278
## 11:          injection      10/21/13             4.038278
## 12:          injection       4/22/08             9.528366
## 13:          injection        8/2/11             6.255639
## 14:          injection      12/31/88            28.809296
## 15: infusion/injection       2/28/06            11.671907
## 16:     oral/injection       11/6/12             4.992481
## 17:          injection       2/28/06            11.671907
## 18:               oral       11/6/12             4.992481
## 19:          injection       4/22/08             9.528366
## 20:          injection       4/24/09             8.524949

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:

  1. adalimumab + methotrexate -> etanercept + methotrexate -> infliximab + methotrexate
  2. 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)
##      [,1]     [,2]     [,3]    
## [1,] "adamtx" "etnmtx" "ifxmtx"
## [2,] "etnmtx" "adamtx" "ifxmtx"
## [3,] "adamtx" "etnmtx" "ifxmtx"
## [4,] "etnmtx" "adamtx" "ifxmtx"
## [5,] "adamtx" "etnmtx" "ifxmtx"
## [6,] "etnmtx" "adamtx" "ifxmtx"
nrow(txseq.mat)
## [1] 100
nrow(pop)
## [1] 100