find_nmb_setofparams: Function to get NMB for a set of parameter This is the inner...

In sheejamk/packEVPI: Estimate Expected Value of Perfect Information

Function to get NMB for a set of parameter This is the inner loop of the two stage Monte Carlo process

Description

Function to get NMB for a set of parameter This is the inner loop of the two stage Monte Carlo process

Usage

find_nmb_setofparams(
  params_interest,
  value_params_interest,
  param_file,
  colnames_paramdistr,
  list_markov,
  threshold,
  comparator = NULL
)

Arguments

params_interest	the main parameter of interest
value_params_interest	value for the parameter of interest
param_file	all parameters required to run the model,provided with name of 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
comparator	optional parameter if need ICER as the output

Details

For the given set of parameters find the nmb from the packDAMipd function calculate_icer_nmb

Value

icer_nmb ICER and NMBs for the set of parameters and given strategies

Examples

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_file <- system.file("extdata", "table_param.csv",
package = "packEVPI")
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))
 param_interest <- "tp_well_dis_co"
 colnames_paramdistr  <- c("Param1_name", "Param1_value", "Param2_name",
 "Param2_value")
 value_param_interest <- 0.2
 threshold <- 2000
 res <- find_nmb_setofparams(param_interest, value_param_interest,
 param_file, colnames_paramdistr, list_markov, threshold)

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