# Cycle dependent

#### Sheeja Manchira Krishnan

#### 2024-03-24

In this example we will model the cost effectiveness of lamivudine/zidovudine combination therapy in HIV infection ([Chancellor, 1997] (https://pubmed.ncbi.nlm.nih.gov/10169387/))) and also described in Decision Modelling for Health Economic Evaluation, page 32 (Example 2.5). The solution can be downloaded from https://www.herc.ox.ac.uk/downloads/decision-modelling-for-health-economic-evaluation. We have implemented the scenario in which the effect of combination therapy of lamivudine/zidovudine (as the probability of moving to worse health states and having to accrue more cost) is only seen in first 2 years. This means that the transition probability and costs are dependent on first two cycles (As the cycle length is years).

This model aims to compare costs and utilities of two treatment strategies, mono therapy and combined therapy.

Four states are described, from best to worst health-wise:

A: CD4 cells > 200 and < 500 cells/mm3; B: CD4 < 200 cells/mm3, non-AIDS; C: AIDS; D: Death.

    library(packDAMipd)
    > Registered S3 method overwritten by 'SurvRegCensCov':
    >   method    from
    >   print.src dplyr

Define health states for mono therapy - now the costs are just defined, but not defined any value.

    A <- health_state("A", cost = "cost_health_A + cost_drug ",utility = 1)
    B <- health_state("B", cost = "cost_health_B + cost_drug",utility = 1)
    C <- health_state("C", cost = "cost_health_C + cost_drug",utility = 1)
    D <- health_state("D", cost = 0, utility = 0)

Define allowed transition probabilities and number them. The below matrix is numbered so that the maximum entry in the matrix gives the total number of allowed transitions. Column names and row names are just the names of the health states.

    tmat <- rbind(c(1, 2,3,4), c(NA, 5,6,7),c(NA, NA, 8,9), c(NA,NA,NA,10))
    colnames(tmat) <- rownames(tmat) <- c("A","B" ,"C","D")

Define transition matrix now, but parameters are fine now

    tm <- populate_transition_matrix(4, tmat, c("tpAtoA", "tpAtoB", "tpAtoC",
                                                "tpAtoD",
                                       "tpBtoB", "tpBtoC", "tpBtoD",
                                       "tpCtoC","tpCtoD","tpDtoD" ))
    > [1] "The transition matrix as explained"
    >     transition number probability name  from from state    to to state
    >                 <num>           <char> <int>     <char> <int>   <char>
    >  1:                 1      prob_A_to_A     1          A     1        A
    >  2:                 2      prob_A_to_B     1          A     2        B
    >  3:                 3      prob_A_to_C     1          A     3        C
    >  4:                 4      prob_A_to_D     1          A     4        D
    >  5:                 5      prob_B_to_B     2          B     2        B
    >  6:                 6      prob_B_to_C     2          B     3        C
    >  7:                 7      prob_B_to_D     2          B     4        D
    >  8:                 8      prob_C_to_C     3          C     3        C
    >  9:                 9      prob_C_to_D     3          C     4        D
    > 10:                10      prob_D_to_D     4          D     4        D

Combine the health states and define the strategy. The current strategy ie. control or intervention - here it is mono therapy

    health_states <- combine_state(A,B,C,D)
    mono_strategy <- strategy(tm, health_states, "mono")

Before we run the model, we need to give values to parameters, thus we define the parameter list. We need to make sure that the parameters with numerical values are given first and derived parameters later in the list. The parameters are assigned sequentially as given in the parameter list. So if there is any calculations needed (or using functions), they are defined earlier.

    mono_params = define_parameters(cost_zido = 2278,
    cost_direct_med_A = 1701,
    cost_comm_care_A = 1055,
    cost_direct_med_B = 1774,
    cost_comm_care_B = 1278,
    cost_direct_med_C = 6948,
    cost_comm_care_C = 2059,
    tpAtoA = 1251/(1251 + 483),
    tpAtoB = 350/(350 + 1384),
    tpAtoC = 116/(116 + 1618),
    tpAtoD = 17/(17 + 1717),
    tpBtoB = 731/(731 + 527),
    tpBtoC = 512/(512 + 746),
    tpBtoD = 15/(15 + 1243),
    tpCtoC = 1312/(1312 + 437),
    tpCtoD = 437/(437 + 1312),
    tpDtoD = 1,
    cost_health_A = "cost_direct_med_A+ cost_comm_care_A",
    cost_health_B = "cost_direct_med_B+ cost_comm_care_B",
    cost_health_C = "cost_direct_med_C+ cost_comm_care_C",
    cost_drug = "cost_zido")

We run the model with strategy mono_strategy for 20 cycles with initial state values 1,0,0,0 corresponding to A,B, C and D. Costs and qualys are discounted following the values given in discount. Parameter values will have any parameter defined as health state values or transition probabilities, but not their values were assigned

    mono_markov <- markov_model(mono_strategy, 20, initial_state = c(1,0,0,0),
                                discount = c(0.06,0), mono_params, FALSE, FALSE,
                                method = "half cycle correction")

Now for combination therapy, the transition probabilities and cost are dependent on the years or the cycle number. Thus we need to define functions that given the correct output to calculate the probabilities and costs as a function of cycle

     #Define function to set the cost to be different for first two cycles
    define_comb_cost = function(cycle, cost_lami) {
      if (cycle == 0  || cycle == 1 || cycle == 2)
        return(cost_lami)
      else
        return(0)
    }
     #Define function to set the risk ratio to be different for first two cycles

    define_rr = function(cycle,rr) {
      if (cycle == 1 || cycle == 2)
        return(rr)
      else
        return(1)
    }

Define health states - now the costs are just defined, but not defined any value.

    A <-  health_state("A", cost = "cost_health_A + cost_drug",utility = 1)
    B <- health_state("B", cost = "cost_health_B + cost_drug",utility = 1)
    C <- health_state("C", cost = "cost_health_C + cost_drug",utility = 1)
    D <- health_state("D", cost = 0, utility = 0)

Define allowed transition probabilities and number them. The below matrix is numbered so that the maximum entry in the matrix gives the total number of allowed transitions. Column names and row names are just the names of the health states.

    tmat <- rbind(c(1, 2,3,4), c(NA, 5,6,7),c(NA, NA, 8,9), c(NA,NA,NA,10))
    colnames(tmat) <- rownames(tmat) <- c("A","B" ,"C","D")

Define transition matrix now, but parameters are changed to indicate that they are dependent on value of “rr”

    tm <- populate_transition_matrix(4, tmat, c("tpAtoA_rr","tpAtoB_rr","tpAtoC_rr","tpAtoD_rr",
                                       "tpBtoB_rr", "tpBtoC_rr", "tpBtoD_rr",
                                       "tpCtoC_rr","tpCtoD_rr","tpDtoD_rr" ), colnames(tmat) )
    > [1] "The transition matrix as explained"
    >     transition number probability name  from from state    to to state
    >                 <num>           <char> <int>     <char> <int>   <char>
    >  1:                 1      prob_A_to_A     1          A     1        A
    >  2:                 2      prob_A_to_B     1          A     2        B
    >  3:                 3      prob_A_to_C     1          A     3        C
    >  4:                 4      prob_A_to_D     1          A     4        D
    >  5:                 5      prob_B_to_B     2          B     2        B
    >  6:                 6      prob_B_to_C     2          B     3        C
    >  7:                 7      prob_B_to_D     2          B     4        D
    >  8:                 8      prob_C_to_C     3          C     3        C
    >  9:                 9      prob_C_to_D     3          C     4        D
    > 10:                10      prob_D_to_D     4          D     4        D

Before we run the model, we need to give values to parameters, thus we define the parameter list.

    comb_params <- define_parameters(cost_zido = 2278,
                         cost_direct_med_A = 1701,
                         cost_comm_care_A = 1055,
                         cost_direct_med_B = 1774,
                         cost_comm_care_B = 1278,
                         cost_direct_med_C = 6948,
                         cost_comm_care_C = 2059,
                         tpAtoA = 1251/(1251 + 483),
                         tpAtoB = 350/(350 + 1384),
                         tpAtoC = 116/(116 + 1618),
                         tpAtoD = 17/(17 + 1717),
                         tpBtoB = 731/(731 + 527),
                         tpBtoC = 512/(512 + 746),
                         tpBtoD = 15/(15 + 1243),
                         tpCtoC = 1312/(1312 + 437),
                         tpCtoD = 437/(437 + 1312),
                         tpDtoD = 1,
                         rr = 0.509,
                         cost_lami = 2086.50,
                         rr_cycle = "define_rr(markov_cycle,rr)",
                         tpAtoA_rr = "1-tpAtoB*rr_cycle-tpAtoC*rr_cycle-tpAtoD*rr_cycle",
                         tpAtoB_rr = "tpAtoB*rr_cycle",
                         tpAtoC_rr = "tpAtoC*rr_cycle",
                         tpAtoD_rr = "tpAtoD*rr_cycle",
                         tpBtoB_rr = "1-tpBtoC*rr_cycle-tpBtoD*rr_cycle",
                         tpBtoC_rr = "tpBtoC*rr_cycle",
                         tpBtoD_rr = "tpBtoD*rr_cycle",
                         tpCtoC_rr = "1-tpCtoD*rr_cycle",
                         tpCtoD_rr = "tpCtoD*rr_cycle",
                         tpDtoD_rr = 1,
                         cost_health_A = "cost_direct_med_A + cost_comm_care_A",
                         cost_health_B = "cost_direct_med_B + cost_comm_care_B",
                         cost_health_C = "cost_direct_med_C + cost_comm_care_C",
                         cost_lami_cycle = "define_comb_cost(markov_cycle,cost_lami)",
                         cost_drug = "cost_zido + cost_lami_cycle")

    # Combine the health states
    health_states <- combine_state(A,B,C,D)
    #The current strategy i.e. control or intervention - here it is combination
    #therapy
    comb_strategy <- strategy(tm, health_states, "comb")

We run the model with strategy mono_strategy for 20 cycles with initial state values 1,0,0,0 corresponding to A,B, C and D. Costs and qualys are discounted following the values given in discount. Parameter values will have any parameter defined as health state values or transition probabilities, but not their values were assigned

    comb_markov <- markov_model(comb_strategy, 20, c(1, 0,0,0), discount = c(0.06,0.0),comb_params,FALSE,FALSE)

Now we combine the Markov models given by and and Estimate ICER and NMB for the list of Markov models given by lost_Markov using the comparator “mono” and threshold values as 1000 GBP. We also plot cost effectiveness acceptability curve using plot_nmb_lambda method. The published report suggests that for mono therapy the accumulated cost is £44,663 while that for the combination therapy is £50,602. The utility values for mono therapy and combination therapy are 7.99 and 8.94 respectively. This provides the ICER value of £6,276.

    list_markov <- combine_markov(mono_markov, comb_markov)
    icer_nmb <- calculate_icer_nmb(list_markov,20000,"mono")
    icer_nmb
    >    Strategy             Cost           Effect              NMB         Inc_Cost
    >      <char>           <char>           <char>           <char>           <char>
    > 1:     mono 49697.4535636967 8.99120664584089 130126.679353121             <NA>
    > 2:     comb 57722.1513312037 9.93738889164347 141025.626501666 8024.69776750702
    >           Inc_Effect            ICER
    >               <char>          <char>
    > 1:              <NA>            <NA>
    > 2: 0.946182245802582 8481.1333156016

    nmb_all <- list()
    prob_ce <- list()
    for (i in 1:40) {
      threshold = i*1000
      nmb <- calculate_icer_nmb(list_markov,threshold, "mono")
      if (nmb[2,"NMB"] > 0) {
          prob = 1
      }else{
         prob = 0
      }
      prob_ce <- append(prob_ce,prob)
      nmb_all <- append(nmb_all, nmb)
    }
    threshold_values <- seq(1000,40000,1000)
    # plot_nmb_lambda(list_markov,threshold_values,"mono")