vignettes/time_dependent.R

In sheejamk/MarkovModel: Decision Analysis Markov Model

## ---- include = FALSE----------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = ">"
)

## ----setup---------------------------------------------------------------
library(MarkovModel)

## ------------------------------------------------------------------------
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)

## ------------------------------------------------------------------------
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")


## ------------------------------------------------------------------------
tm <- transition_matrix(4, tmat, c("tpAtoA","tpAtoB","tpAtoC","tpAtoD",
                                   "tpBtoB", "tpBtoC", "tpBtoD",
                                   "tpCtoC","tpCtoD","tpDtoD" ), colnames(tmat) )

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


## ------------------------------------------------------------------------
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")

## ------------------------------------------------------------------------
mono_markov <-markov_model(mono_strategy, 20,c(1,0,0,0),c(0,0,0,0),discount=c(0.06,0),mono_params)

## ------------------------------------------------------------------------
 #Define function to set the cost to be differnt for first two cycles
define_comb_cost=function(cycle,cost_lami){
  if(cycle==2 || cycle ==3)
    return(cost_lami)
  else
    return(0)
}
 #Define function to set the risk ratio to be differnt for first two cycles

define_rr=function(cycle,rr){
  if(cycle==2 || cycle ==3)
    return(rr)
  else
    return(1)
}


## ------------------------------------------------------------------------
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)


## ------------------------------------------------------------------------
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")


## ------------------------------------------------------------------------
tm <- 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) )


## ------------------------------------------------------------------------
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 ie. control or intervention - here it is combination therapy
comb_strategy <- strategy(tm, health_states, "comb")


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

## ------------------------------------------------------------------------
list_markov <- combine_markov(mono_markov, comb_markov)
mono_cost_20cycles=mono_markov$cost_matrix[nrow(mono_markov$cost_matrix),ncol(mono_markov$cost_matrix)]
mono_cost_20cycles
utiltiy_20cycles=mono_markov$utility_matrix[nrow(mono_markov$utility_matrix),ncol(mono_markov$utility_matrix)]
utiltiy_20cycles

comb_cost_20cycles=comb_markov$cost_matrix[nrow(comb_markov$cost_matrix),ncol(comb_markov$cost_matrix)]
comb_cost_20cycles
utiltiy_20cycles=comb_markov$utility_matrix[nrow(comb_markov$utility_matrix),ncol(comb_markov$utility_matrix)]
utiltiy_20cycles
icer_nmb<-calculate_icer_nmb(list_markov,20000,"mono")
icer_nmb
nmb_all <-list()
prob_ce <-list()
for( i in 1:40){
  threshold = i*1000
  nmb<-calculate_icer_nmb(list_markov,threshold, "mono")[2]
  if(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_ceac(list_markov,threshold_values,"mono")