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## name
## 1: cDMARDs
## 2: abatacept IV + methotrexate
## 3: abatacept SC + methotrexate
## 4: adalimumab + methotrexate
## 5: adalimumab
## 6: adalimumab-bwwd (biosimilar Samsung Bioepis) + methotrexate
## 7: anakinra + methotrexate
## 8: baricitinib
## 9: baricitinib + methotrexate
## 10: certolizumab pegol
## 11: certolizumab pegol + methotrexate
## 12: etanercept
## 13: etanercept + methotrexate
## 14: etanercept-szzs (biosimilar Sandoz) + methotrexate
## 15: etanercept-ykro (biosimilar Samsung Bioepis) + methotrexate
## 16: golimumab
## 17: golimumab + methotrexate
## 18: infliximab + methotrexate
## 19: infliximab-qbtx (biosimilar Pfizer) + methotrexate
## 20: placebo
## 21: non/biologic
## 22: rituximab
## 23: rituximab + methotrexate
## 24: sarilumab
## 25: sarilumab + methotrexate
## 26: sulfasalazine + hydroxychloroquine + methotrexate
## 27: tocilizumab
## 28: tocilizumab + methotrexate
## 29: tofacitinib + methotrexate
## 30: tofacitinib
## 31: upadacitinib + methotrexate
## name
## mname sname route approval_date
## 1: cDMARDs cdmards injection 12/31/88
## 2: ABT IV + MTX abtivmtx infusion/injection 12/23/05
## 3: ABT SC + MTX abtscmtx injection 8/2/11
## 4: ADA + MTX adamtx injection 12/31/02
## 5: ADA ada injection 12/31/02
## 6: ADA BWWD + MTX adabiosbwwdmtx injection 7/23/18
## 7: ANA + MTX anamtx injection 12/31/02
## 8: BCT bct oral 5/31/18
## 9: BCT + MTX bctmtx oral 5/31/18
## 10: CZP czp injection 4/22/08
## 11: CZP + MTX czpmtx injection 4/22/08
## 12: ETN etn injection 11/2/98
## 13: ETN + MTX etnmtx injection 11/2/98
## 14: ETN SZZS + MTX etnbiosszzsmtx injection 8/30/16
## 15: ETN YKRO + MTX etnbiosykromtx injection 4/25/19
## 16: GOL gol injection 4/24/09
## 17: GOL + MTX golmtx injection 4/24/09
## 18: IFX + MTX ifxmtx infusion/injection 11/10/99
## 19: IFX QBTX + MTX ifxbiosqbtxmtx infusion/injection 12/13/17
## 20: Placebo placebo none 12/31/88
## 21: NBT nbt injection 12/31/88
## 22: RTX rtx injection 2/28/06
## 23: RTX + MTX rtxmtx infusion/injection 2/28/06
## 24: SAR sar injection 22/05/17
## 25: SAR + MTX sarmtx injection 22/05/17
## 26: SSZ + HCQ + MTX triple injection 12/31/02
## 27: TCZ tcz injection 10/21/13
## 28: TCZ + MTX tczmtx injection 10/21/13
## 29: TOF + MTX tofmtx oral/injection 11/6/12
## 30: TOF tof oral 11/6/12
## 31: UPA + MTX upamtx oral 8/16/19
## mname sname route approval_date
## years_since_approval
## 1: 31.956254
## 2: 15.002051
## 3: 9.402597
## 4: 17.976760
## 5: 17.976760
## 6: 2.438824
## 7: 17.976760
## 8: 2.583732
## 9: 2.583732
## 10: 12.675325
## 11: 12.675325
## 12: 22.132604
## 13: 22.132604
## 14: 4.330827
## 15: 1.684211
## 16: 11.671907
## 17: 11.671907
## 18: 21.112782
## 19: 3.045796
## 20: 31.956254
## 21: 31.956254
## 22: 14.818865
## 23: 14.818865
## 24: 0.100000
## 25: 0.100000
## 26: 17.976760
## 27: 7.185236
## 28: 7.185236
## 29: 8.139440
## 30: 8.139440
## 31: 1.375256
## years_since_approvalTo 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.
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:
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] 100nrow(pop)## [1] 100