calculate_icer_nmb: Estimation of ICER and NMB

In packDAMipd: Decision Analysis Modelling Package with Parameters Estimation Ability from Individual Patient Level Data

Description

Estimation of ICER and NMB

Usage

calculate_icer_nmb(list_markov, threshold, comparator = NULL)

Arguments

list_markov	list of Markov model objects with their Markov trace, cost matrix and utility matrix
threshold	threshold value of WTP
comparator	the strategy to be compared with

Value

ICER and NMB for all the strategies compared to comparator

Examples

well <- health_state("well", cost = 0, utility = 1)
disabled <- health_state("disabled", cost = 100, utility = 1)
dead <- health_state("dead", cost = 0, utility = 0)
tmat <- rbind(c(1, 2, 3), c(NA, 4, 5), c(NA, NA, 6))
colnames(tmat) <- rownames(tmat) <- c("well", "disabled", "dead")
tm <- populate_transition_matrix(3, tmat, c(0.6, 0.2, 0.2, 0.6, 0.4, 1),
colnames(tmat))
health_states <- combine_state(well, disabled, dead)
this.strategy <- strategy(tm, health_states, "control")
this_markov <- markov_model(this.strategy, 24, c(1000, 0, 0), c(0,0))
well <- health_state("well", cost = 0, utility = 1)
disabled <- health_state("disabled", cost = 10, utility = 0.5)
dead <- health_state("dead", cost = 0, utility = 0)
tmat <- rbind(c(1, 2, 3), c(NA, 4, 5), c(NA, NA, 6))
colnames(tmat) <- rownames(tmat) <- c("well", "disabled", "dead")
tm <- populate_transition_matrix(3, tmat, c(0.4, 0.4, 0.2, 0.6, 0.4, 1),
colnames(tmat))
health_states <- combine_state(well, disabled, dead)
this.strategy <- strategy(tm, health_states, "intervention")
sec_markov <- markov_model(this.strategy, 24, c(1000, 0, 0), c(0,0))
list_markov <- combine_markov(this_markov, sec_markov)
calculate_icer_nmb(list_markov, 20000, comparator = "control")

packDAMipd documentation built on March 3, 2021, 5:07 p.m.