# TITLE: "Replication of the "Reference Model", described in the manuscript submitted to the European Journal of Health Economics in November 2014: # "Economic evaluation in chronic pain: A systematic review and de novo flexible economic model" # WHEN VIEWED IN RStudio # TO VIEW THE CODE IN COLLAPSED SECTIONS: # Edit>Folding>Collapse All # Click on margin arrows to show section headings and contained code #Running order: #Description #Controls #Source data #Model parameters #Input matrices for model #Patient flow calculations #Summary results ############ #DESCRIPTION ############ #This model is the 'Reference Model' submitted as an appendix to the manuscript "Economic evaluation in chronic pain: A systematic review and de novo flexible economic model" #submitted to the European Journal of Health Economics in January 2015 #The model compares two treatments for chronic musculoskeletal pain: (i) oral morphine (though the model has the flexibility to select oxycodone data at #first-line instead) (Treatment 1 / Tx1) and (ii) a hypothetical novel formulation (Treatment 2 /Tx2). The model structure is identical across treatment arms. #The model has two absorbing states to capture outcomes for those who have discontinued treatment or moved on to subsequent treatment following two failed #attempts at therapy: 'treatment discontinuation' and 'subsequent treatment'. The other states in the model are states to capture outcomes while on treatment, #or transitory states to capture temporary outcomes following withdrawal from treatment. #Patients enter the model and begin treatment. While on treatment, patients may experience treatment-emergent tolerable adverse events (AEs) (constipation and #nausea/vomiting); the simulated cohort can therefore be separated into two AE-related Markov states within 1st-line treatment. HRQL consequences of differences #in analgesic effects across treatments are captured across treatment arms. #Patients may withdraw from 1st-line treatment and move to either 'withdrawal, AEs' or 'withdrawal, other (including pain)' for one model cycle to accrue the #temporary dip in HRQL and cost outcomes which patients experience immediately following treatment failure. These patients then either: # . forgo further therapy and move to the absorbing state 'treatment discontinuation, or # . attempt 2nd-line therapy. #The Markov states for 2nd-line therapy are identical to those for 1st-line therapy. Patients may withdraw from 2nd-line therapy for identical reasons as for #1st-line therapy. Outcomes following withdrawal from 2nd-line therapy are assumed to be captured by the two absorbing states in the model. #Different structural and data assumptions can be tested by adjusting settings in #CONTROLs #The scenarios tested in the manuscript are detailed within #CONTROLS #Treatment 1 - Comparator #Treatment 2 - Novel therapy ######### #CONTROLS ######### #MODEL SETTINGS - the user can adjust these to explore different parameter and structural assumptions #Base case settings stored below cont.standarderror<-0.1 cont.utilitypercimp_NovelTherap<-0 cont.costmultiplier_noveltherapy<-6 cont.prob.discont.after.failed.1L<-0.05 cont.prob.discont.after.failed.2L<-0.1 cont.PatPathway<-"Discontinuation" cont.titration<-"no adjustment for first 4 weeks" cont.Tx1Arm_1LTxoptions<-"Morphine" cont.Tx1Arm_2LTxoptions<-"Oxycodone" cont.Tx2Arm_2LTxoptions<-"Oxycodone" cont.withd_mult_init<-2 cont.doseadj_init<-0.65 cont.prop_improv_AE_noveltherapy<-0.3 cont.prop_disc_GP<-0.5 cont.prop_naus_GP<-0.2 cont.umult_failed1L<-0.9 cont.umult_failed2L<-0.8 #cycle length 1 week cont.CycleProp<-7/365.25 cont.TimeHorizon<-52 # MODEL SETTINGS - BASE CASE and ##RESTRICTIONS # cont.standarderror=0.1 ##input a value between 0 and 0.5, inclusive # cont.utilitypercimp_NovelTherap<-0 ##input a value between 0 and 0.2, inclusive - scenario 0.05 # cont.costmultiplier_noveltherapy<-4 ##input a positive number # cont.prob.discont.after.failed.1L<-0.05 ##input a value between 0 and 1, inclusive # cont.prob.discont.after.failed.2L<-0.1 ##input a value between 0 and 1, inclusive # cont.PatPathway<-"Discontinuation" ##"Discontinuation" or "Some enter subsequent treatment" # cont.titration<-"no adjustment for first 4 weeks" ##"no adjustment for first 4 weeks" or "User-defined dose and withdrawal rate adjustment for first 4 weeks" # cont.Tx1Arm_1LTxoptions<-"Morphine" ##"Morphine" or "Oxycodone" # cont.Tx1Arm_2LTxoptions<-"Oxycodone" ##"Oxycodone" or "Morphine" # cont.Tx2Arm_2LTxoptions<-"Oxycodone" ##"Oxycodone" or "Morphine" # cont.withd_mult_init<-2 ##input a positive value # cont.doseadj_init<-0.65 ##input a value between 0 and 1, inclusive # cont.prop_improv_AE_noveltherapy<-0.3 ##input a positive value # cont.prop_disc_GP<-0.5 ##input a value between 0 and 1, inclusive # cont.prop_naus_GP<-0.2 ##input a value between 0 and 1, inclusive # cont.umult_failed1L<-0.9 ##input a value between 0 and 1, inclusive # cont.umult_failed2L<-0.8 ##input a value between 0 and 1, inclusive # cont.TimeHorizon<-52 ##input a time horizon up to 104 (weeks) # SCENARIOS # Scenario 1 - Base case settings # Scenario 2 - Base case settings, but cont.PatPathway<-"Some enter subsequent treatment" # Scenario 3 - Base case settings, but cont.Tx2Arm_2LTxoptions<-"Morphine" # Scenario 4 - Base case settings, but cont.titration<-"User-defined dose and withdrawal rate adjustment for first 4 weeks" # Scenario 5 - Base case settings, but cont.utilitypercimp_NovelTherap<-0.05 # Scenario 6 - Base case settings, but cont.TimeHorizon<-104 # Scenario 7 - Base case settings, but cont.umult_failed1L<-1 and cont.umult_failed2L<-1 ############ #SOURCE DATA ############ #Cost source data #Much of source cost data are 2009/10 data - Curtis et al Inflation Factor update to 2012/2013 cost year PriceIndex0910<-268.6 PriceIndex1213<-289.1 InfFactor<-PriceIndex1213/PriceIndex0910 #Morphine cost from BNF 67, dose from NICE 2012 study c_cycle_Tx_morphine<-2.632729166666670000 #Morphine comed costs, withdrawal and constipation AE costs from NICE 2012 study c_cycle_comed_morphine<-2.1*InfFactor c_withdrawal<-99.33*InfFactor c_constipation<-4.68*InfFactor #Oxycodone cost from BNF 67, dose from Dunlop et al 2012 study c_cycle_Tx_oxycodone<-9.2011500000 #Oxycodone comed costs from Dunlop et al 2012 study c_cycle_comed_oxycodone<-0.04*InfFactor #GP visit cost from PSSRU 2013 (Applied to a proportion of nausea AE patients) c_GP<-37 #Utility source data #Utility data from Ikenberg et al 2012 u_OnTx<-0.695 u_disc_AE<-0.503 u_disc_efficacy<-0.405 u_OnTx_AE<-0.583 #Cycle (7 day) probability source data #Morphine data from Grenier et al 2006 28-day probability data - assume 7 day nausea/vomitting duration and continuous constipation duration #nausea/vomiting M28DNVprob<-0.387 McontinNVprob<-(-log(1-M28DNVprob))/28 Mcycle_prob_nausea<-1-exp(-McontinNVprob*7) #constipation Mcycle_prob_const<-0.321 #Tolerable AE cycle prob cycle_prob_morphine_tolerableAE<-Mcycle_prob_nausea+Mcycle_prob_const #withdrawal due to AEs M28DWITHDAEprob<-0.205 McontinWITHDAEprob<-(-log(1-M28DWITHDAEprob))/28 cycle_prob_morphine_withd_AEs<-1-exp(-McontinWITHDAEprob*7) #withdrawal due to other reason M28DWITHDOTHERprob<-0.050 McontinWITHDOTHERprob<-(-log(1-M28DWITHDOTHERprob))/28 cycle_prob_morphine_withd_other<-1-exp(-McontinWITHDOTHERprob*7) #Oxycodone data from Ikenberg et al 2012 105-day probability data - did not report tolerable AEs by each AE, assume continuous AE duration #tolerable AEs cycle_prob_oxy_tolerableAE<-0.4635 #withdrawal due to AEs O105DWITHDAEprob<-0.3936 OcontinWITHDAEprob<-(-log(1-O105DWITHDAEprob))/105 cycle_prob_oxy_withdrawAE<-1-exp(-OcontinWITHDAEprob*7) #withdrawal due to other reason O105DWITHDOTHERprob<-0.034 OcontinWITHDOTHERprob<-(-log(1-O105DWITHDOTHERprob))/105 cycle_prob_oxy_withdrawother<-1-exp(-OcontinWITHDOTHERprob*7) ################# #MODEL PARAMETERS ################# #costs c_cycle_tx_tx1<-ifelse(cont.Tx1Arm_1LTxoptions=="Morphine",c_cycle_Tx_morphine,c_cycle_Tx_oxycodone) c_cycle_tx_tx2<-c_cycle_Tx_oxycodone*cont.costmultiplier_noveltherapy c_cycle_comed_tx1<-ifelse(cont.Tx1Arm_1LTxoptions=="Morphine",c_cycle_comed_morphine,c_cycle_comed_oxycodone) c_cycle_comed_tx2<-c_cycle_comed_oxycodone*(1-cont.prop_improv_AE_noveltherapy) c_cycle_ae<-(Mcycle_prob_nausea/cycle_prob_morphine_tolerableAE)*cont.prop_naus_GP*c_GP+c_constipation c_cycle_withd_AE<-c_withdrawal c_cycle_withd_other<-c_withdrawal c_cycle_2Ltx_tx1arm<-ifelse(cont.Tx1Arm_2LTxoptions=="Morphine",c_cycle_Tx_morphine+c_cycle_comed_morphine,c_cycle_Tx_oxycodone+c_cycle_comed_oxycodone) c_cycle_2Ltx_tx2arm<-ifelse(cont.Tx2Arm_2LTxoptions=="Morphine",c_cycle_Tx_morphine+c_cycle_comed_morphine,c_cycle_Tx_oxycodone+c_cycle_comed_oxycodone) c_cycle_3Ltx_tx1modelarm<-c_cycle_Tx_morphine+c_cycle_comed_morphine c_cycle_3Ltx_tx2modelarm<-c_cycle_Tx_morphine+c_cycle_comed_morphine c_cycle_discontinued<-c_GP*cont.prop_disc_GP c_cycle_tx_tx1_init<-c_cycle_tx_tx1*cont.doseadj_init c_cycle_tx_tx2_init<-c_cycle_tx_tx1*cont.costmultiplier_noveltherapy*cont.doseadj_init #utilities u_on1LTx_Tx1_noAE<-u_OnTx u_on1LTx_Tx1_AE<-u_OnTx_AE u_on1LTx_Tx2_noAE<-u_OnTx*(1+cont.utilitypercimp_NovelTherap) u_on1LTx_Tx2_AE<-u_OnTx_AE*(1+cont.utilitypercimp_NovelTherap) u_withdrawn1L_AE<-u_disc_AE u_withdrawn1L_other<-u_disc_efficacy u_on2LTx_noAE<-u_OnTx*cont.umult_failed1L u_on2LTx_AE<-u_OnTx_AE*cont.umult_failed1L u_withdrawn2L_AE<-u_disc_AE*cont.umult_failed1L u_withdrawn2L_other<-u_disc_efficacy*cont.umult_failed1L u_3L<-((u_OnTx+u_OnTx_AE+u_disc_AE+u_disc_efficacy)/4)*cont.umult_failed2L u_discontinuation<-u_disc_efficacy*cont.umult_failed2L #cycle probabilities prob_AE_Tx1arm_1L<-ifelse(cont.Tx1Arm_1LTxoptions=="Morphine",cycle_prob_morphine_tolerableAE,cycle_prob_oxy_tolerableAE) prob_AE_Tx2arm_1L<-cycle_prob_oxy_tolerableAE*(1-cont.prop_improv_AE_noveltherapy) prob_withdAE_Tx1arm_1L<-ifelse(cont.Tx1Arm_1LTxoptions=="Morphine",cycle_prob_morphine_withd_AEs,cycle_prob_oxy_withdrawAE) prob_withdAE_Tx2arm_1L<-cycle_prob_oxy_withdrawAE*(1-cont.prop_improv_AE_noveltherapy) prob_withdother_Tx1arm_1L<-ifelse(cont.Tx1Arm_1LTxoptions=="Morphine",cycle_prob_morphine_withd_other,cycle_prob_oxy_withdrawother) prob_withdother_Tx2arm_1L<-cycle_prob_oxy_withdrawother*(1-cont.prop_improv_AE_noveltherapy) prob_AE_Tx1arm_2L<-ifelse(cont.Tx1Arm_2LTxoptions=="Morphine",cycle_prob_morphine_tolerableAE,cycle_prob_oxy_tolerableAE) prob_AE_Tx2arm_2L<-ifelse(cont.Tx2Arm_2LTxoptions=="Morphine",cycle_prob_morphine_tolerableAE,cycle_prob_oxy_tolerableAE) prob_withdAE_Tx1arm_2L<-ifelse(cont.Tx1Arm_2LTxoptions=="Morphine",cycle_prob_morphine_withd_AEs,cycle_prob_oxy_withdrawAE) prob_withdAE_Tx2arm_2L<-ifelse(cont.Tx2Arm_2LTxoptions=="Morphine",cycle_prob_morphine_withd_AEs,cycle_prob_oxy_withdrawAE) prob_withdother_Tx1arm_2L<-ifelse(cont.Tx1Arm_2LTxoptions=="Morphine",cycle_prob_morphine_withd_other,cycle_prob_oxy_withdrawother) prob_withdother_Tx2arm_2L<-ifelse(cont.Tx2Arm_2LTxoptions=="Morphine",cycle_prob_morphine_withd_other,cycle_prob_oxy_withdrawother) prob_discontafter1L<-cont.prob.discont.after.failed.1L prob_discontafter2L<-cont.prob.discont.after.failed.2L prob_withdAE_Tx1arm_1L_init<-prob_withdAE_Tx1arm_1L*cont.withd_mult_init prob_withdAE_Tx2arm_1L_init<-prob_withdAE_Tx2arm_1L*cont.withd_mult_init prob_withdother_Tx1arm_1L_init<-prob_withdother_Tx1arm_1L*cont.withd_mult_init prob_withdother_Tx2arm_1L_init<-prob_withdother_Tx2arm_1L*cont.withd_mult_init ######################## #INPUT MATRICES FOR MODEL ######################## #INITIAL VECTOR InitVector<-c(1,0,0,0,0, 0,0,0,0,0) #UTILITY VECTORS UtilityVector_Comp<-c(u_on1LTx_Tx1_noAE,u_on1LTx_Tx1_AE, u_withdrawn1L_AE,u_withdrawn1L_other, u_on2LTx_noAE,u_on2LTx_AE, u_withdrawn2L_AE,u_withdrawn2L_other, u_3L,u_discontinuation)*cont.CycleProp UtilityVector_Novel<-UtilityVector_Comp UtilityVector_Novel[1]<-u_on1LTx_Tx2_noAE*cont.CycleProp UtilityVector_Novel[2]<-u_on1LTx_Tx2_AE*cont.CycleProp #set Cost Matrix and Trace/Trans Matrices length to 105 - maximum time horizon tested+1 costANDtracematrices_nrows<-105 #COST MATRICES CostMatrix_Comp<-matrix(nrow=costANDtracematrices_nrows,ncol=10) CostMatrix_Comp[,1]<-c_cycle_tx_tx1+c_cycle_comed_tx1 CostMatrix_Comp[,2]<-c_cycle_tx_tx1+c_cycle_comed_tx1+c_cycle_ae CostMatrix_Comp[,3]<-c_cycle_withd_AE CostMatrix_Comp[,4]<-c_cycle_withd_other CostMatrix_Comp[,5]<-c_cycle_2Ltx_tx1arm CostMatrix_Comp[,6]<-c_cycle_2Ltx_tx1arm+c_cycle_ae CostMatrix_Comp[,7]<-c_cycle_withd_AE CostMatrix_Comp[,8]<-c_cycle_withd_other CostMatrix_Comp[,9]<-c_cycle_3Ltx_tx1modelarm CostMatrix_Comp[,10]<-c_cycle_discontinued if (cont.titration=="no adjustment for first 4 weeks"){ print('no titration adjustment...') } else { for (i in 2:5){ CostMatrix_Comp[i,1]<-c_cycle_tx_tx1_init+c_cycle_comed_tx1 CostMatrix_Comp[i,2]<-c_cycle_tx_tx1_init+c_cycle_comed_tx1+c_cycle_ae }} #NovelTherapy arm CostMatrix_Novel<-CostMatrix_Comp CostMatrix_Novel[,1]<-c_cycle_tx_tx2+c_cycle_comed_tx2 CostMatrix_Novel[,2]<-c_cycle_tx_tx2+c_cycle_comed_tx2+c_cycle_ae CostMatrix_Novel[,5]<-c_cycle_2Ltx_tx2arm CostMatrix_Novel[,6]<-c_cycle_2Ltx_tx2arm+c_cycle_ae if (cont.titration=="no adjustment for first 4 weeks"){ print('no titration adjustment...') } else { for (i in 2:5){ CostMatrix_Novel[i,1]<-c_cycle_tx_tx2_init+c_cycle_comed_tx2 CostMatrix_Novel[i,2]<-c_cycle_tx_tx2_init+c_cycle_comed_tx2+c_cycle_ae }} #TRANSITION ARRAYS - 1= transitions from 1LTx NO AEs, etc #Note - 3D arrays are required rather than 2D matrices as different transition matrices are required for early cycles if titration is considered TransVec1_Comp<-c((1-prob_withdAE_Tx1arm_1L-prob_withdother_Tx1arm_1L)*(1-prob_AE_Tx1arm_1L), (1-prob_withdAE_Tx1arm_1L-prob_withdother_Tx1arm_1L)*(prob_AE_Tx1arm_1L), prob_withdAE_Tx1arm_1L, prob_withdother_Tx1arm_1L, 0, 0,0,0,0,0 ) TransVec2_Comp<-c((1-prob_withdAE_Tx1arm_1L-prob_withdother_Tx1arm_1L)*(1-prob_AE_Tx1arm_1L), (1-prob_withdAE_Tx1arm_1L-prob_withdother_Tx1arm_1L)*(prob_AE_Tx1arm_1L), prob_withdAE_Tx1arm_1L, prob_withdother_Tx1arm_1L, 0, 0,0,0,0,0 ) TransVec3_Comp<-c(0,0,0,0, (1-prob_discontafter1L)*(1-prob_AE_Tx1arm_2L), (1-prob_discontafter1L)*prob_AE_Tx1arm_2L, 0,0,0, prob_discontafter1L ) TransVec4_Comp<-c(0,0,0,0, (1-prob_discontafter1L)*(1-prob_AE_Tx1arm_2L), (1-prob_discontafter1L)*prob_AE_Tx1arm_2L, 0,0,0, prob_discontafter1L ) TransVec5_Comp<-c(0,0,0,0, (1-prob_withdAE_Tx1arm_2L-prob_withdother_Tx1arm_2L)*(1-prob_AE_Tx1arm_2L), (1-prob_withdAE_Tx1arm_2L-prob_withdother_Tx1arm_2L)*(prob_AE_Tx1arm_2L), prob_withdAE_Tx1arm_2L, prob_withdother_Tx1arm_2L, 0,0 ) TransVec6_Comp<-c(0,0,0,0, (1-prob_withdAE_Tx1arm_2L-prob_withdother_Tx1arm_2L)*(1-prob_AE_Tx1arm_2L), (1-prob_withdAE_Tx1arm_2L-prob_withdother_Tx1arm_2L)*(prob_AE_Tx1arm_2L), prob_withdAE_Tx1arm_2L, prob_withdother_Tx1arm_2L, 0,0 ) TransVec7_Comp<-c(0,0,0,0,0, 0,0,0, ifelse(cont.PatPathway=="Discontinuation",0,1-prob_discontafter2L), ifelse(cont.PatPathway=="Discontinuation",1,prob_discontafter2L) ) TransVec8_Comp<-c(0,0,0,0,0, 0,0,0, ifelse(cont.PatPathway=="Discontinuation",0,1-prob_discontafter2L), ifelse(cont.PatPathway=="Discontinuation",1,prob_discontafter2L) ) TransVec9_Comp<-c(0,0,0,0,0, 0,0,0,1,0) TransVec10_Comp<-c(0,0,0,0,0, 0,0,0,0,1) TransMat_Comp<-rbind(TransVec1_Comp,TransVec2_Comp,TransVec3_Comp,TransVec4_Comp,TransVec5_Comp, TransVec6_Comp,TransVec7_Comp,TransVec8_Comp,TransVec9_Comp,TransVec10_Comp) colnames(TransMat_Comp)<-c("NoAEs_1L","AEs_1L","WithdAEs_1L","WithdOther_1L","NoAEs_2L","AEs_2L","WithdAEs_2L","WithdOther_1L","3rdL","Discontinuation") rownames(TransMat_Comp)<-colnames(TransMat_Comp) rowSums(TransMat_Comp) #Novel Therapy arm TransVec1_Novel<-c((1-prob_withdAE_Tx2arm_1L-prob_withdother_Tx2arm_1L)*(1-prob_AE_Tx2arm_1L), (1-prob_withdAE_Tx2arm_1L-prob_withdother_Tx2arm_1L)*(prob_AE_Tx2arm_1L), prob_withdAE_Tx2arm_1L, prob_withdother_Tx2arm_1L, 0, 0,0,0,0,0 ) TransVec2_Novel<-c((1-prob_withdAE_Tx2arm_1L-prob_withdother_Tx2arm_1L)*(1-prob_AE_Tx2arm_1L), (1-prob_withdAE_Tx2arm_1L-prob_withdother_Tx2arm_1L)*(prob_AE_Tx2arm_1L), prob_withdAE_Tx2arm_1L, prob_withdother_Tx2arm_1L, 0, 0,0,0,0,0 ) TransVec3_Novel<-c(0,0,0,0, (1-prob_discontafter1L)*(1-prob_AE_Tx2arm_2L), (1-prob_discontafter1L)*prob_AE_Tx2arm_2L, 0,0,0, prob_discontafter1L ) TransVec4_Novel<-c(0,0,0,0, (1-prob_discontafter1L)*(1-prob_AE_Tx2arm_2L), (1-prob_discontafter1L)*prob_AE_Tx2arm_2L, 0,0,0, prob_discontafter1L ) TransVec5_Novel<-c(0,0,0,0, (1-prob_withdAE_Tx2arm_2L-prob_withdother_Tx2arm_2L)*(1-prob_AE_Tx2arm_2L), (1-prob_withdAE_Tx2arm_2L-prob_withdother_Tx2arm_2L)*(prob_AE_Tx2arm_2L), prob_withdAE_Tx2arm_2L, prob_withdother_Tx2arm_2L, 0,0 ) TransVec6_Novel<-c(0,0,0,0, (1-prob_withdAE_Tx2arm_2L-prob_withdother_Tx2arm_2L)*(1-prob_AE_Tx2arm_2L), (1-prob_withdAE_Tx2arm_2L-prob_withdother_Tx2arm_2L)*(prob_AE_Tx2arm_2L), prob_withdAE_Tx2arm_2L, prob_withdother_Tx2arm_2L, 0,0 ) TransVec7_Novel<-c(0,0,0,0,0, 0,0,0, ifelse(cont.PatPathway=="Discontinuation",0,1-prob_discontafter2L), ifelse(cont.PatPathway=="Discontinuation",1,prob_discontafter2L) ) TransVec8_Novel<-c(0,0,0,0,0, 0,0,0, ifelse(cont.PatPathway=="Discontinuation",0,1-prob_discontafter2L), ifelse(cont.PatPathway=="Discontinuation",1,prob_discontafter2L) ) TransVec9_Novel<-c(0,0,0,0,0, 0,0,0,1,0) TransVec10_Novel<-c(0,0,0,0,0, 0,0,0,0,1) TransMat_Novel<-rbind(TransVec1_Novel,TransVec2_Novel,TransVec3_Novel,TransVec4_Novel,TransVec5_Novel, TransVec6_Novel,TransVec7_Novel,TransVec8_Novel,TransVec9_Novel,TransVec10_Novel) rownames(TransMat_Novel)<-rownames(TransMat_Comp) colnames(TransMat_Novel)<-colnames(TransMat_Comp) rowSums(TransMat_Novel) #### #Generate alternate transition matrix if titration scenario being considered #### if (cont.titration=="no adjustment for first 4 weeks"){ print('no titration adjustment...') } else { #Comp arm InitTransVec1_Comp<-c((1-prob_withdAE_Tx1arm_1L_init-prob_withdother_Tx1arm_1L_init)*(1-prob_AE_Tx1arm_1L), (1-prob_withdAE_Tx1arm_1L_init-prob_withdother_Tx1arm_1L_init)*(prob_AE_Tx1arm_1L), prob_withdAE_Tx1arm_1L_init, prob_withdother_Tx1arm_1L_init, 0, 0,0,0,0,0 ) InitTransVec2_Comp<-c((1-prob_withdAE_Tx1arm_1L_init-prob_withdother_Tx1arm_1L_init)*(1-prob_AE_Tx1arm_1L), (1-prob_withdAE_Tx1arm_1L_init-prob_withdother_Tx1arm_1L_init)*(prob_AE_Tx1arm_1L), prob_withdAE_Tx1arm_1L_init, prob_withdother_Tx1arm_1L_init, 0, 0,0,0,0,0 ) InitTransMat_Comp<-rbind(InitTransVec1_Comp,InitTransVec2_Comp,TransVec3_Comp,TransVec4_Comp,TransVec5_Comp, TransVec6_Comp,TransVec7_Comp,TransVec8_Comp,TransVec9_Comp,TransVec10_Comp) rownames(InitTransMat_Comp)<-rownames(TransMat_Comp) colnames(InitTransMat_Comp)<-colnames(TransMat_Comp) rowSums(InitTransMat_Comp) #Novel arm InitTransVec1_Novel<-c((1-prob_withdAE_Tx2arm_1L_init-prob_withdother_Tx2arm_1L_init)*(1-prob_AE_Tx2arm_1L), (1-prob_withdAE_Tx2arm_1L_init-prob_withdother_Tx2arm_1L_init)*(prob_AE_Tx2arm_1L), prob_withdAE_Tx2arm_1L_init, prob_withdother_Tx2arm_1L_init, 0, 0,0,0,0,0 ) InitTransVec2_Novel<-c((1-prob_withdAE_Tx2arm_1L_init-prob_withdother_Tx2arm_1L_init)*(1-prob_AE_Tx2arm_1L), (1-prob_withdAE_Tx2arm_1L_init-prob_withdother_Tx2arm_1L_init)*(prob_AE_Tx2arm_1L), prob_withdAE_Tx2arm_1L_init, prob_withdother_Tx2arm_1L_init, 0, 0,0,0,0,0 ) InitTransMat_Novel<-rbind(InitTransVec1_Novel,InitTransVec2_Novel,TransVec3_Novel,TransVec4_Novel,TransVec5_Novel, TransVec6_Novel,TransVec7_Novel,TransVec8_Novel,TransVec9_Novel,TransVec10_Novel) rownames(InitTransMat_Novel)<-rownames(TransMat_Novel) colnames(InitTransMat_Novel)<-colnames(TransMat_Novel) rowSums(InitTransMat_Novel) } #### #create arrays of matrices to allow time-varying transition probabilities TransArrayComp <- array(data=NA,dim=c(10,10,costANDtracematrices_nrows)) #dim=c(nrow,ncol,nmatrix) TransArrayNovel <- array(data=NA,dim=c(10,10,costANDtracematrices_nrows)) #dim=c(nrow,ncol,nmatrix) for (i in 1:costANDtracematrices_nrows) { TransArrayComp[,,i] <-TransMat_Comp TransArrayNovel[,,i]<-TransMat_Novel } #adapt array if titration is under consideration ### if (cont.titration=="no adjustment for first 4 weeks"){ print('no titration adjustment...') } else { for (i in 1:4) { TransArrayComp[,,i] <-InitTransMat_Comp TransArrayNovel[,,i]<-InitTransMat_Novel } } ### #check rowSums(TransArrayComp[,,4]) rowSums(TransArrayComp[,,8]) rowSums(TransArrayNovel[,,4]) rowSums(TransArrayNovel[,,8]) ########################## #PATIENT FLOW CALCULATIONS #################### #remember, costANDtracematrices_nrows<-105 #COMP trace_matrix_Comp <- matrix(NA, nrow=costANDtracematrices_nrows, ncol=ncol(TransArrayComp)) trace_matrix_Comp[1,] <- InitVector trace_matrix_Comp[2,] <- InitVector %*% TransArrayComp[,,1] for (i in 3:nrow(trace_matrix_Comp)){ trace_matrix_Comp[i,] <- trace_matrix_Comp[i-1,] %*% TransArrayComp[,,i-1] } #hcc = half cycle corrected hcc_trace_matrix_Comp <- matrix(NA, nrow=costANDtracematrices_nrows, ncol=ncol(TransArrayComp)) hcc_trace_matrix_Comp[1,] <- 0.5*InitVector + 0.5*trace_matrix_Comp[2,] for (i in 2:costANDtracematrices_nrows-1){ hcc_trace_matrix_Comp[i,] <- 0.5*trace_matrix_Comp[i,] + 0.5*trace_matrix_Comp[i+1,] } hcc_trace_matrix_Comp[costANDtracematrices_nrows,] <- trace_matrix_Comp[costANDtracematrices_nrows,] trace_costs_Comp <- hcc_trace_matrix_Comp*CostMatrix_Comp trace_qalys_Comp <- sweep(hcc_trace_matrix_Comp, MARGIN=2, UtilityVector_Comp, "*") HealthStateCosts_Comp <- colSums(trace_costs_Comp[1:cont.TimeHorizon,]) HealthStateQALYs_Comp <- colSums(trace_qalys_Comp[1:cont.TimeHorizon,]) total_costs_Comp <- sum(colSums(trace_costs_Comp[1:cont.TimeHorizon,])) total_qalys_Comp <- sum(colSums(trace_qalys_Comp[1:cont.TimeHorizon,])) #NOVEL trace_matrix_Novel <- matrix(NA, nrow=costANDtracematrices_nrows, ncol=ncol(TransArrayNovel)) trace_matrix_Novel[1,] <- InitVector trace_matrix_Novel[2,] <- InitVector %*% TransArrayNovel[,,1] for (i in 3:nrow(trace_matrix_Novel)){ trace_matrix_Novel[i,] <- trace_matrix_Novel[i-1,] %*% TransArrayNovel[,,i-1] } #hcc = half cycle corrected hcc_trace_matrix_Novel <- matrix(NA, nrow=costANDtracematrices_nrows, ncol=ncol(TransArrayNovel)) hcc_trace_matrix_Novel[1,] <- 0.5*InitVector + 0.5*trace_matrix_Novel[2,] for (i in 2:costANDtracematrices_nrows-1){ hcc_trace_matrix_Novel[i,] <- 0.5*trace_matrix_Novel[i,] + 0.5*trace_matrix_Novel[i+1,] } hcc_trace_matrix_Novel[costANDtracematrices_nrows,] <- trace_matrix_Novel[costANDtracematrices_nrows,] trace_costs_Novel <- hcc_trace_matrix_Novel*CostMatrix_Novel trace_qalys_Novel <- sweep(hcc_trace_matrix_Novel, MARGIN=2, UtilityVector_Novel, "*") HealthStateCosts_Novel <- colSums(trace_costs_Novel[1:cont.TimeHorizon,]) HealthStateQALYs_Novel <- colSums(trace_qalys_Novel[1:cont.TimeHorizon,]) total_costs_Novel <- sum(colSums(trace_costs_Novel[1:cont.TimeHorizon,])) total_qalys_Novel <- sum(colSums(trace_qalys_Novel[1:cont.TimeHorizon,])) ICER<-(total_costs_Novel-total_costs_Comp)/(total_qalys_Novel-total_qalys_Comp) ############### #SUMMARY RESULTS ############### total_costs_Comp total_qalys_Comp total_costs_Novel total_qalys_Novel ICER