estimate_evpi_evppi: Function to estimate the expected value of partial perfect...

In sheejamk/packEVPI: Estimate Expected Value of Perfect Information

Function to estimate the expected value of partial perfect information This is the outer loop of the two stage Monte Carlo process

Description

Function to estimate the expected value of partial perfect information This is the outer loop of the two stage Monte Carlo process

Usage

estimate_evpi_evppi(
  params_interest,
  param_file,
  colnames_paramdistr,
  list_markov,
  threshold,
  outer_iterations,
  inner_iterations,
  comparator = NULL
)

Arguments

params_interest	the main parameter of interest
param_file	all parameters required to run the model,provided with name of the parameter, distribution and parameters that define the probability distribution
colnames_paramdistr	column names where parameter distribution and values are defined
list_markov	list of markov models to estimate the NMB
threshold	threshold values of WTP
outer_iterations	number of iterations for outer loop
inner_iterations	number of iterations for inner loop
comparator	optional parameter if need ICER as the output

Value

result evpi and evppi for a single param

Source

https://www.sciencedirect.com/science/article/pii/S1098301510605888

Examples

param_file <- system.file("extdata", "table_param.csv", package = "packEVPI")
colnames_paramdistr  <- c("Param1_name", "Param1_value", "Param2_name",
 "Param2_value")
 params_interest <- "tp_well_dis_co"
 outer_iterations <- 1
 inner_iterations <- 2
threshold <- 2000
well <- packDAMipd::health_state("well", cost = "cost_well_co", utility = 1)
 disabled <- packDAMipd::health_state("disabled", cost = "cost_dis_co",
 utility = "utility_dis_co")
 dead <- packDAMipd::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 <- packDAMipd::populate_transition_matrix(3, tmat,
 c("tp_well_well_co","tp_well_dis_co","tp_well_dead", "tp_dis_dis_co",
 "tp_dis_dead", "tp_dead_dead"),colnames(tmat))
 health_states <- packDAMipd::combine_state(well, disabled, dead)
 this.strategy <- packDAMipd::strategy(tm, health_states, "control")
 param_list <- packDAMipd::define_parameters(
 tp_well_dis_co = packDAMipd::get_parameter_read("tp_well_dis_co",
 param_file),
 tp_well_dis_in =  packDAMipd::get_parameter_read("tp_well_dis_in",
 param_file),
 tp_well_dead =  packDAMipd::get_parameter_read("tp_well_dead", param_file),
 tp_dis_dead =  packDAMipd::get_parameter_read("tp_dis_dead", param_file),
 tp_dead_dead =  1,
 cost_well_co =  packDAMipd::get_parameter_read("cost_well_co", param_file),
 cost_well_in =  packDAMipd::get_parameter_read("cost_well_in", param_file),
 cost_dis_co =  packDAMipd::get_parameter_read("cost_dis_co", param_file),
 cost_dis_in =  packDAMipd::get_parameter_read("cost_dis_in", param_file),
 utility_dis_co =  packDAMipd::get_parameter_read("utility_dis_co",
 param_file),
 utility_dis_in =  packDAMipd::get_parameter_read("utility_dis_in",
 param_file),
 tp_well_well_co = "1-(tp_well_dis_co + tp_well_dead)",
 tp_well_well_in = "1-(tp_well_dis_in + tp_well_dead)",
 tp_dis_dis_co = "1-( tp_dis_dead)",
 tp_dis_dis_in = "1-( tp_dis_dead)")
 this_markov <- packDAMipd::markov_model(this.strategy, 24, c(1000, 0, 0),
 discount = c(0, 0),method = "half cycle correction", param_list)
 well <- packDAMipd::health_state("well", cost = "cost_well_in", utility = 1)
 disabled <- packDAMipd::health_state("disabled", cost = "cost_dis_in",
 utility = "utility_dis_in")
 dead <- packDAMipd::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 <- packDAMipd::populate_transition_matrix(3, tmat, c("tp_well_well_in",
 "tp_well_dis_in", "tp_well_dead", "tp_dis_dis_in","tp_dis_dead",
 "tp_dead_dead"), colnames(tmat))
 health_states <- packDAMipd::combine_state(well, disabled, dead)
 this.strategy <- packDAMipd::strategy(tm, health_states, "intervention")
 sec_markov <- packDAMipd::markov_model(this.strategy, 24, c(1000, 0, 0),
  discount = c(0, 0), method = "half cycle correction", param_list)
  list_markov <- packDAMipd::combine_markov(list(this_markov, sec_markov))
 res <- estimate_evpi_evppi(params_interest, param_file, colnames_paramdistr,
 list_markov, threshold, outer_iterations, inner_iterations)

sheejamk/packEVPI documentation built on April 7, 2023, 8:48 a.m.