R/input_parameter_functions.R
In benenns/OUD-Model: Semi-Markov Decision Model
Defines functions
update_param_list
load_all_params
load_mort_params
#' Load mortality data
#'
#' \code{load_mort_data} is used to load age-specific mortality from .csv file
#' into vector.
#'
#' @param file String with the location and name of the file with mortality
#' data.
#' @return
#' A vector with mortality by age.
#' @export
load_mort_params <- function(file.mort = NULL, n_male){
df_lt_can_2018 <- read.csv(file = file.mort)
v_r_mort_by_age_male <- df_lt_can_2018 %>% select(Male) %>% as.matrix()
v_r_mort_by_age_female <- df_lt_can_2018 %>% select(Female) %>% as.matrix()
v_r_mort_by_age <- (v_r_mort_by_age_male * n_male) + (v_r_mort_by_age_female * (1 - n_male)) # weighted mortality
return(v_r_mort_by_age)
}
#' Load all parameters
#'
#' \code{load_all_params} loads all parameters for the decision model from multiple sources and creates a list.
#'
#' @param file.init String with the location and name of the file with initial set of parameters
#' @param file.init_dist String with the location and name of the file with initial distributions
#' @param file.mort String with the location and name of the file with mortality data
#' @param file.death_hr String with the location and name of death hazard ratios
#' @param file.frailty String with the location and name of the file with frailty estimates for subsequent episodes in health states
#' @param file.weibull_scale String with the location and name of the file with weibull scale
#' @param file.weibull_shape String with the location and name of the file with weibull shape
#' @param file.unconditional String with the location and name of the file with empirical destination states
#' @param file.overdose String with the location and name of the file with overdose/fentanyl-related parameters
#' @param file.fentanyl String with the location and name of the file with fentanyl exposure parameters
#' @param file.hiv String with the location and name of the file with HIV seroconversion probabilities
#' @param file.hcv String with the location and name of the file with HCV seroconversion probabilities
#' @param file.costs String with the location and name of the file with costs (excluding crime costs)
#' @param file.crime_costs String with the location and name of the file with age-specific crime costs
#' @param file.qalys String with the location and name of the file with HRQoL weights
#'
#' @return
#' A list of all parameters used for the decision model.
#' @export
load_all_params <- function(file.init = NULL,
file.init_dist = NULL,
file.mort = NULL,
file.death_hr = NULL,
file.frailty = NULL,
file.weibull = NULL,
#file.weibull_scale = NULL,
#file.weibull_shape = NULL,
file.unconditional = NULL,
file.overdose = NULL,
file.fentanyl = NULL,
file.hiv = NULL,
file.hcv = NULL,
file.costs = NULL,
file.crime_costs = NULL,
file.qalys = NULL){ # User defined
#Load files of all baseline model parameters
df_init_params <- read.csv(file = file.init, row.names = 1, header = TRUE) # Initial parameter values
df_init_dist <- read.csv(file = file.init_dist, row.names = 1, header = TRUE) # Initial parameter values
df_death_hr <- read.csv(file = file.death_hr, row.names = 1, header = TRUE) # Mortality hazard ratios
df_frailty <- read.csv(file = file.frailty, row.names = 1, header = TRUE) # Episode frailty params
df_weibull <- read.csv(file = file.weibull, row.names = 1, header = TRUE) # Weibull params
#df_weibull_scale <- read.csv(file = file.weibull_scale, row.names = 1, header = TRUE) # Weibull scale params
#df_weibull_shape <- read.csv(file = file.weibull_shape, row.names = 1, header = TRUE) # Weibull shape params
df_UP <- read.csv(file = file.unconditional, row.names = 1, header = TRUE) # Unconditional transition probs
df_overdose <- read.csv(file = file.overdose, row.names = 1, header = TRUE) # Overdose-fentanyl parameters
df_fentanyl <- read.csv(file = file.fentanyl, row.names = 1, header = TRUE) # Fentanyl exposure parameters
df_hiv <- read.csv(file = file.hiv, row.names = 1, header = TRUE) # HIV seroconversion probs
df_hcv <- read.csv(file = file.hcv, row.names = 1, header = TRUE) # HCV seroconversion probs
df_costs <- read.csv(file = file.costs, row.names = 1, header = TRUE) # All costs excluding crime
df_crime_costs <- read.csv(file = file.crime_costs, row.names = 1, header = TRUE) # Crime costs
df_qalys <- read.csv(file = file.qalys, row.names = 1, header = TRUE) # QALYs
l_params_all <- list(
#### Initial parameters ####
n_age_init = df_init_params["pe", "age_init"], # age at baseline
n_age_max = df_init_params["pe", "age_max"], # maximum age of follow up
n_per = df_init_params["pe", "period_yr"], # periods per year (e.g. 12-months)
n_discount = df_init_params["pe", "discount"], # discount rate
n_cali_per = df_init_params["pe", "cali_per"], # number of calibration periods
n_male = df_init_params["pe", "male_prop"], # % male
n_INJ = df_init_params["pe", "INJ_prop"], # % injection
#n_HIV = df_init_params["pe", "hiv_prop"], # % of HIV-positive individuals
#n_HCV = df_init_params["pe", "hcv_prop"], # % of HCV-positive individuals
#n_COI = df_init_params["pe", "coi_prop"], # % of co-infected individuals
#Injection
n_HIV_INJ = df_init_params["pe", "HIV_prop_INJ"], # % of HIV-positive individuals
n_HCV_INJ = df_init_params["pe", "HCV_prop_INJ"], # % of HCV-positive individuals
n_COI_INJ = df_init_params["pe", "COI_prop_INJ"], # % of co-infected individuals
# Non-injection
n_HIV_NI = df_init_params["pe", "HIV_prop_NI"], # % of HIV-positive individuals
n_HCV_NI = df_init_params["pe", "HCV_prop_NI"], # % of HCV-positive individuals
n_COI_NI = df_init_params["pe", "COI_prop_NI"], # % of co-infected individuals
n_HIV_ART = df_init_params["pe", "ART_prop"], # % of HIV-positive on-ART (used to calculate costs)
n_HCV_DAA = df_init_params["pe", "DAA_prop"], # % of HIV-positive on-ART (used to calculate costs)
#### Initial state distribution ####
v_init_dist = as.vector(df_init_dist["pe", ]),
#### Mortality ####
v_r_mort_by_age = load_mort_params(file = file.mort, n_male = df_init_params["pe", "male_prop"]), # vector of age-specific mortality
#### Hazard ratios for death probability ####
# ***NEW ESTIMATES*** #
# Non-injection
hr_BUP_NI = df_death_hr["pe", "TX"],
hr_BUPC_NI = df_death_hr["pe", "TX"],
hr_MET_NI = df_death_hr["pe", "TX"],
hr_METC_NI = df_death_hr["pe", "TX"],
hr_REL_NI = df_death_hr["pe", "REL"],
hr_ODN_NI = df_death_hr["pe", "REL"],
hr_ABS_NI = df_death_hr["pe", "ABS"],
hr_HIV_NI = df_death_hr["pe", "HIV"],
hr_HCV_NI = df_death_hr["pe", "HCV"],
hr_COI_NI = df_death_hr["pe", "COI"],
# Injection
hr_BUP_INJ = df_death_hr["pe", "TX"],
hr_BUPC_INJ = df_death_hr["pe", "TX"],
hr_MET_INJ = df_death_hr["pe", "TX"],
hr_METC_INJ = df_death_hr["pe", "TX"],
hr_REL_INJ = df_death_hr["pe", "REL"],
hr_ODN_INJ = df_death_hr["pe", "REL"],
hr_ABS_INJ = df_death_hr["pe", "ABS"],
hr_HIV_INJ = df_death_hr["pe", "HIV"],
hr_HCV_INJ = df_death_hr["pe", "HCV"],
hr_COI_INJ = df_death_hr["pe", "COI"],
# Non-injection
#hr_BUP_NI = df_death_hr["pe", "BUP_NI"],
#hr_BUPC_NI = df_death_hr["pe", "BUPC_NI"],
#hr_MET_NI = df_death_hr["pe", "MET_NI"],
#hr_METC_NI = df_death_hr["pe", "METC_NI"],
#hr_REL_NI = df_death_hr["pe", "REL_NI"],
#hr_ODN_NI = df_death_hr["pe", "ODN_NI"],
#hr_ABS_NI = df_death_hr["pe", "ABS_NI"],
#hr_HIV_NI = df_death_hr["pe", "HIV_NI"],
#hr_HCV_NI = df_death_hr["pe", "HCV_NI"],
#hr_COI_NI = df_death_hr["pe", "COI_NI"],
# Injection
#hr_BUP_INJ = df_death_hr["pe", "BUP_INJ"],
#hr_BUPC_INJ = df_death_hr["pe", "BUPC_INJ"],
#hr_MET_INJ = df_death_hr["pe", "MET_INJ"],
#hr_METC_INJ = df_death_hr["pe", "METC_INJ"],
#hr_REL_INJ = df_death_hr["pe", "REL_INJ"],
#hr_ODN_INJ = df_death_hr["pe", "ODN_INJ"],
#hr_ABS_INJ = df_death_hr["pe", "ABS_INJ"],
#hr_HIV_INJ = df_death_hr["pe", "HIV_INJ"],
#hr_HCV_INJ = df_death_hr["pe", "HCV_INJ"],
#hr_COI_INJ = df_death_hr["pe", "COI_INJ"],
#### Frailty estimates for successive episodes, injection vs. non-injection, concurrent opioid use ####
# ***NEW ESTIMATES*** #
# Episodes
p_frailty_BUP_1 = 1,
p_frailty_BUP_2 = df_frailty["pe", "BUP_2"],
p_frailty_BUP_3 = df_frailty["pe", "BUP_3"],
p_frailty_MET_1 = 1,
p_frailty_MET_2 = df_frailty["pe", "MET_2"],
p_frailty_MET_3 = df_frailty["pe", "MET_3"],
p_frailty_REL_1 = 1,
p_frailty_REL_2 = df_frailty["pe", "REL_2"],
p_frailty_REL_3 = df_frailty["pe", "REL_3"],
p_frailty_ABS_1 = 1,
p_frailty_ABS_2 = df_frailty["pe", "ABS_2"],
p_frailty_ABS_3 = df_frailty["pe", "ABS_3"],
# Injection vs. non-injection
p_frailty_BUP_INJ = df_frailty["pe", "BUP_INJ"],
p_frailty_MET_INJ = df_frailty["pe", "MET_INJ"],
p_frailty_REL_INJ = df_frailty["pe", "REL_INJ"],
p_frailty_ABS_INJ = df_frailty["pe", "ABS_INJ"],
# Concurrent opioid use
p_frailty_BUPC = df_frailty["pe", "BUPC"],
p_frailty_METC = df_frailty["pe", "METC"],
# ***OLD ESTIMATES*** #
# Non-injection
#p_frailty_BUP_NI_1 = 1,
#p_frailty_BUP_NI_2 = df_frailty["pe", "BUP_NI_2"],
#p_frailty_BUP_NI_3 = df_frailty["pe", "BUP_NI_3"],
#p_frailty_BUPC_NI_1 = 1,
#p_frailty_BUPC_NI_2 = df_frailty["pe", "BUPC_NI_2"],
#p_frailty_BUPC_NI_3 = df_frailty["pe", "BUPC_NI_3"],
#p_frailty_MET_NI_1 = 1,
#p_frailty_MET_NI_2 = df_frailty["pe", "MET_NI_2"],
#p_frailty_MET_NI_3 = df_frailty["pe", "MET_NI_3"],
#p_frailty_METC_NI_1 = 1,
#p_frailty_METC_NI_2 = df_frailty["pe", "METC_NI_2"],
#p_frailty_METC_NI_3 = df_frailty["pe", "METC_NI_3"],
#p_frailty_ABS_NI_1 = 1,
#p_frailty_ABS_NI_2 = df_frailty["pe", "ABS_NI_2"],
#p_frailty_ABS_NI_3 = df_frailty["pe", "ABS_NI_3"],
#p_frailty_REL_NI_1 = 1,
#p_frailty_REL_NI_2 = df_frailty["pe", "REL_NI_2"],
#p_frailty_REL_NI_3 = df_frailty["pe", "REL_NI_3"],
# Injection
#p_frailty_BUP_INJ_1 = 1,
#p_frailty_BUP_INJ_2 = df_frailty["pe", "BUP_INJ_2"],
#p_frailty_BUP_INJ_3 = df_frailty["pe", "BUP_INJ_3"],
#p_frailty_BUPC_INJ_1 = 1,
#p_frailty_BUPC_INJ_2 = df_frailty["pe", "BUPC_INJ_2"],
#p_frailty_BUPC_INJ_3 = df_frailty["pe", "BUPC_INJ_3"],
#p_frailty_MET_INJ_1 = 1,
#p_frailty_MET_INJ_2 = df_frailty["pe", "MET_INJ_2"],
#p_frailty_MET_INJ_3 = df_frailty["pe", "MET_INJ_3"],
#p_frailty_METC_INJ_1 = 1,
#p_frailty_METC_INJ_2 = df_frailty["pe", "METC_INJ_2"],
#p_frailty_METC_INJ_3 = df_frailty["pe", "METC_INJ_3"],
#p_frailty_ABS_INJ_1 = 1,
#p_frailty_ABS_INJ_2 = df_frailty["pe", "ABS_INJ_2"],
#p_frailty_ABS_INJ_3 = df_frailty["pe", "ABS_INJ_3"],
#p_frailty_REL_INJ_1 = 1,
#p_frailty_REL_INJ_2 = df_frailty["pe", "REL_INJ_2"],
#p_frailty_REL_INJ_3 = df_frailty["pe", "REL_INJ_3"],
#### Load weibull parameters ####
# From OPTIMA trial
# Shape
p_weibull_shape_BUP = df_weibull["pe", "BUP_shape_1"],
p_weibull_shape_MET = df_weibull["pe", "MET_shape_1"],
p_weibull_shape_REL = df_weibull["pe", "REL_shape_1"],
p_weibull_shape_ABS = df_weibull["pe", "ABS_shape_1"],
# scale
p_weibull_scale_BUP = df_weibull["pe", "BUP_scale_1"],
p_weibull_scale_MET = df_weibull["pe", "MET_scale_1"],
p_weibull_scale_REL = df_weibull["pe", "REL_scale_1"],
p_weibull_scale_ABS = df_weibull["pe", "ABS_scale_1"],
# Parameters for episode 1, 2, 3+
# BUP
# Scale
#p_weibull_scale_BUP_1 = df_weibull["pe", "BUP_scale_1"],
#p_weibull_scale_BUP_2 = df_weibull["pe", "BUP_scale_2"],
#p_weibull_scale_BUP_3 = df_weibull["pe", "BUP_scale_3"],
# Shape
#p_weibull_shape_BUP_1 = df_weibull["pe", "BUP_shape_1"],
#p_weibull_shape_BUP_2 = df_weibull["pe", "BUP_shape_2"],
#p_weibull_shape_BUP_3 = df_weibull["pe", "BUP_shape_3"],
# MET
# Scale
#p_weibull_scale_MET_1 = df_weibull["pe", "MET_scale_1"],
#p_weibull_scale_MET_2 = df_weibull["pe", "MET_scale_2"],
#p_weibull_scale_MET_3 = df_weibull["pe", "MET_scale_3"],
# Shape
#p_weibull_shape_MET_1 = df_weibull["pe", "MET_shape_1"],
#p_weibull_shape_MET_2 = df_weibull["pe", "MET_shape_2"],
#p_weibull_shape_MET_3 = df_weibull["pe", "MET_shape_3"],
# REL
# Scale
#p_weibull_scale_REL_1 = df_weibull["pe", "REL_scale_1"],
#p_weibull_scale_REL_2 = df_weibull["pe", "REL_scale_2"],
#p_weibull_scale_REL_3 = df_weibull["pe", "REL_scale_3"],
# Shape
#p_weibull_shape_REL_1 = df_weibull["pe", "REL_shape_1"],
#p_weibull_shape_REL_2 = df_weibull["pe", "REL_shape_2"],
#p_weibull_shape_REL_3 = df_weibull["pe", "REL_shape_3"],
# ABS
# Scale
#p_weibull_scale_ABS_1 = df_weibull["pe", "ABS_scale_1"],
#p_weibull_scale_ABS_2 = df_weibull["pe", "ABS_scale_2"],
#p_weibull_scale_ABS_3 = df_weibull["pe", "ABS_scale_3"],
# Shape
#p_weibull_shape_ABS_1 = df_weibull["pe", "ABS_shape_1"],
#p_weibull_shape_ABS_2 = df_weibull["pe", "ABS_shape_2"],
#p_weibull_shape_ABS_3 = df_weibull["pe", "ABS_shape_3"],
# Weibull scale
#p_weibull_scale_BUP_NI = df_weibull_scale["pe", "BUP_NI"],
#p_weibull_scale_BUPC_NI = df_weibull_scale["pe", "BUP_NI"], # same estimates for concurrent use (adjustment with frailty)
#p_weibull_scale_MET_NI = df_weibull_scale["pe", "MET_NI"],
#p_weibull_scale_METC_NI = df_weibull_scale["pe", "MET_NI"], # same estimates for concurrent use (adjustment with frailty)
#p_weibull_scale_REL_NI = df_weibull_scale["pe", "REL_NI"],
#p_weibull_scale_ABS_NI = df_weibull_scale["pe", "ABS_NI"],
#
#p_weibull_scale_BUP_INJ = df_weibull_scale["pe", "BUP_INJ"],
#p_weibull_scale_BUPC_INJ = df_weibull_scale["pe", "BUP_INJ"], # same estimates for concurrent use (adjustment with frailty)
#p_weibull_scale_MET_INJ = df_weibull_scale["pe", "MET_INJ"],
#p_weibull_scale_METC_INJ = df_weibull_scale["pe", "MET_INJ"], # same estimates for concurrent use (adjustment with frailty)
#p_weibull_scale_REL_INJ = df_weibull_scale["pe", "REL_INJ"],
#p_weibull_scale_ABS_INJ = df_weibull_scale["pe", "ABS_INJ"],
# Weibull shape
#p_weibull_shape_BUP_NI = df_weibull_shape["pe", "BUP_NI"],
#p_weibull_shape_BUPC_NI = df_weibull_shape["pe", "BUP_NI"], # same estimates for concurrent use (adjustment with frailty)
#p_weibull_shape_MET_NI = df_weibull_shape["pe", "MET_NI"],
#p_weibull_shape_METC_NI = df_weibull_shape["pe", "MET_NI"], # same estimates for concurrent use (adjustment with frailty)
#p_weibull_shape_REL_NI = df_weibull_shape["pe", "REL_NI"],
#p_weibull_shape_ABS_NI = df_weibull_shape["pe", "ABS_NI"],
#
#p_weibull_shape_BUP_INJ = df_weibull_shape["pe", "BUP_INJ"],
#p_weibull_shape_BUPC_INJ = df_weibull_shape["pe", "BUP_INJ"], # same estimates for concurrent use (adjustment with frailty)
#p_weibull_shape_MET_INJ = df_weibull_shape["pe", "MET_INJ"],
#p_weibull_shape_METC_INJ = df_weibull_shape["pe", "MET_INJ"], # same estimates for concurrent use (adjustment with frailty)
#p_weibull_shape_REL_INJ = df_weibull_shape["pe", "REL_INJ"],
#p_weibull_shape_ABS_INJ = df_weibull_shape["pe", "ABS_INJ"],
#### Unconditional transition probabilities ####
# Non-Injection
# From BUP
p_BUP_BUPC_NI = df_UP["BUP_NI", "BUPC_NI"],
p_BUP_MET_NI = df_UP["BUP_NI", "MET_NI"],
p_BUP_METC_NI = df_UP["BUP_NI", "METC_NI"],
p_BUP_ABS_NI = df_UP["BUP_NI", "ABS_NI"],
p_BUP_REL_NI = df_UP["BUP_NI", "REL_NI"],
# From BUPC
p_BUPC_BUP_NI = df_UP["BUPC_NI", "BUP_NI"],
p_BUPC_MET_NI = df_UP["BUPC_NI", "MET_NI"],
p_BUPC_METC_NI = df_UP["BUPC_NI", "METC_NI"],
p_BUPC_ABS_NI = df_UP["BUPC_NI", "ABS_NI"],
p_BUPC_REL_NI = df_UP["BUPC_NI", "REL_NI"],
# From MET
p_MET_METC_NI = df_UP["MET_NI", "METC_NI"],
p_MET_BUP_NI = df_UP["MET_NI", "BUP_NI"],
p_MET_BUPC_NI = df_UP["MET_NI", "BUPC_NI"],
p_MET_ABS_NI = df_UP["MET_NI", "ABS_NI"],
p_MET_REL_NI = df_UP["MET_NI", "REL_NI"],
# From METC
p_METC_MET_NI = df_UP["METC_NI", "MET_NI"],
p_METC_BUP_NI = df_UP["METC_NI", "BUP_NI"],
p_METC_BUPC_NI = df_UP["METC_NI", "BUPC_NI"],
p_METC_ABS_NI = df_UP["METC_NI", "ABS_NI"],
p_METC_REL_NI = df_UP["METC_NI", "REL_NI"],
# From ABS
p_ABS_REL_NI = df_UP["ABS_NI", "REL_NI"],
p_ABS_MET_NI = df_UP["ABS_NI", "MET_NI"],
p_ABS_METC_NI = df_UP["ABS_NI", "METC_NI"],
p_ABS_BUP_NI = df_UP["ABS_NI", "BUP_NI"],
p_ABS_BUPC_NI = df_UP["ABS_NI", "BUPC_NI"],
# From REL
p_REL_MET_NI = df_UP["REL_NI", "MET_NI"],
p_REL_METC_NI = df_UP["REL_NI", "METC_NI"],
p_REL_BUP_NI = df_UP["REL_NI", "BUP_NI"],
p_REL_BUPC_NI = df_UP["REL_NI", "BUPC_NI"],
p_REL_ABS_NI = df_UP["REL_NI", "ABS_NI"],
# From OD
p_ODN_MET_NI = df_UP["ODN_NI", "MET_NI"],
p_ODN_METC_NI = df_UP["ODN_NI", "METC_NI"],
p_ODN_BUP_NI = df_UP["ODN_NI", "BUP_NI"],
p_ODN_BUPC_NI = df_UP["ODN_NI", "BUPC_NI"],
p_ODN_ABS_NI = df_UP["ODN_NI", "ABS_NI"],
p_ODN_REL_NI = df_UP["ODN_NI", "REL_NI"],
# Inj
# From BUP & BUPC
p_BUP_BUPC_INJ = df_UP["BUP_INJ", "BUPC_INJ"],
p_BUP_MET_INJ = df_UP["BUP_INJ", "MET_INJ"],
p_BUP_METC_INJ = df_UP["BUP_INJ", "METC_INJ"],
p_BUP_ABS_INJ = df_UP["BUP_INJ", "ABS_INJ"],
p_BUP_REL_INJ = df_UP["BUP_INJ", "REL_INJ"],
p_BUPC_BUP_INJ = df_UP["BUPC_INJ", "BUP_INJ"],
p_BUPC_MET_INJ = df_UP["BUPC_INJ", "MET_INJ"],
p_BUPC_METC_INJ = df_UP["BUPC_INJ", "METC_INJ"],
p_BUPC_ABS_INJ = df_UP["BUPC_INJ", "ABS_INJ"],
p_BUPC_REL_INJ = df_UP["BUPC_INJ", "REL_INJ"],
# From MET & METC
p_MET_METC_INJ = df_UP["MET_INJ", "METC_INJ"],
p_MET_BUP_INJ = df_UP["MET_INJ", "BUP_INJ"],
p_MET_BUPC_INJ = df_UP["MET_INJ", "BUPC_INJ"],
p_MET_ABS_INJ = df_UP["MET_INJ", "ABS_INJ"],
p_MET_REL_INJ = df_UP["MET_INJ", "REL_INJ"],
p_METC_MET_INJ = df_UP["METC_INJ", "MET_INJ"],
p_METC_BUP_INJ = df_UP["METC_INJ", "BUP_INJ"],
p_METC_BUPC_INJ = df_UP["METC_INJ", "BUPC_INJ"],
p_METC_ABS_INJ = df_UP["METC_INJ", "ABS_INJ"],
p_METC_REL_INJ = df_UP["METC_INJ", "REL_INJ"],
# From ABS
p_ABS_REL_INJ = df_UP["ABS_INJ", "REL_INJ"],
p_ABS_MET_INJ = df_UP["ABS_INJ", "MET_INJ"],
p_ABS_METC_INJ = df_UP["ABS_INJ", "METC_INJ"],
p_ABS_BUP_INJ = df_UP["ABS_INJ", "BUP_INJ"],
p_ABS_BUPC_INJ = df_UP["ABS_INJ", "BUPC_INJ"],
# From REL
p_REL_MET_INJ = df_UP["REL_INJ", "MET_INJ"],
p_REL_METC_INJ = df_UP["REL_INJ", "METC_INJ"],
p_REL_BUP_INJ = df_UP["REL_INJ", "BUP_INJ"],
p_REL_BUPC_INJ = df_UP["REL_INJ", "BUPC_INJ"],
p_REL_ABS_INJ = df_UP["REL_INJ", "ABS_INJ"],
# From OD
p_ODN_MET_INJ = df_UP["ODN_INJ", "MET_INJ"],
p_ODN_METC_INJ = df_UP["ODN_INJ", "METC_INJ"],
p_ODN_BUP_INJ = df_UP["ODN_INJ", "BUP_INJ"],
p_ODN_BUPC_INJ = df_UP["ODN_INJ", "BUPC_INJ"],
p_ODN_ABS_INJ = df_UP["ODN_INJ", "ABS_INJ"],
p_ODN_REL_INJ = df_UP["ODN_INJ", "REL_INJ"],
#### Overdose ####
# Overdose parameters loaded as rates and converted to probabilities in model
# Includes additional calibration related parameters
# Base overdose params
n_TX_OD = df_overdose["pe", "TX_OD"],
n_TXC_OD = df_overdose["pe", "TXC_OD"],
n_REL_OD = df_overdose["pe", "REL_OD"],
n_ABS_OD = df_overdose["pe", "ABS_OD"],
# Gamma shape parameter (prior)
n_TX_OD_shape = df_overdose["shape", "TX_OD"],
n_TXC_OD_shape = df_overdose["shape", "TXC_OD"],
n_REL_OD_shape = df_overdose["shape", "REL_OD"],
n_ABS_OD_low = df_overdose["low", "ABS_OD"],
# Gamma scale parameter (prior)
n_TX_OD_scale = df_overdose["scale", "TX_OD"],
n_TXC_OD_scale = df_overdose["scale", "TXC_OD"],
n_REL_OD_scale = df_overdose["scale", "REL_OD"],
n_ABS_OD_high = df_overdose["high", "ABS_OD"],
# Overdose transition multipliers
# First 4 weeks of treatment and relapse
# Treatment states
#n_TX_OD_mult = df_overdose["pe", "TX_OD_mult"],
#n_TX_OD_mult_shape = df_overdose["shape", "TX_OD_mult"],
#n_TX_OD_mult_scale = df_overdose["scale", "TX_OD_mult"],
# BUP
n_BUP_OD_mult = df_overdose["pe", "BUP_OD_mult"],
n_BUP_OD_mult_shape = df_overdose["shape", "BUP_OD_mult"],
n_BUP_OD_mult_scale = df_overdose["scale", "BUP_OD_mult"],
# MET
n_MET_OD_mult = df_overdose["pe", "MET_OD_mult"],
n_MET_OD_mult_shape = df_overdose["shape", "MET_OD_mult"],
n_MET_OD_mult_scale = df_overdose["scale", "MET_OD_mult"],
# Treatment + concurrent opioid
n_TXC_OD_mult = df_overdose["pe", "TXC_OD_mult"],
n_TXC_OD_mult_shape = df_overdose["shape", "TXC_OD_mult"],
n_TXC_OD_mult_scale = df_overdose["scale", "TXC_OD_mult"],
# Relapse
n_REL_OD_mult = df_overdose["pe", "REL_OD_mult"],
n_REL_OD_mult_shape = df_overdose["shape", "REL_OD_mult"],
n_REL_OD_mult_scale = df_overdose["scale", "REL_OD_mult"],
# Abstinence
n_ABS_OD_mult = df_overdose["pe", "ABS_OD_mult"],
n_ABS_OD_mult_shape = df_overdose["shape", "ABS_OD_mult"],
n_ABS_OD_mult_scale = df_overdose["scale", "ABS_OD_mult"],
# Injection (vs. non-injection)
n_INJ_OD_mult = df_overdose["pe", "INJ_OD_mult"],
n_INJ_OD_mult_shape = df_overdose["shape", "INJ_OD_mult"],
n_INJ_OD_mult_scale = df_overdose["scale", "INJ_OD_mult"],
# Fatal overdose (conditional on overdose)
n_fatal_OD = df_overdose["pe", "fatal_OD"],
n_fatal_OD_shape = df_overdose["shape", "fatal_OD"],
n_fatal_OD_scale = df_overdose["scale", "fatal_OD"],
# Fentanyl
# Rate of fentanyl overdose
#n_fent_OD = df_overdose["pe", "fent_OD_rate"],
# Fentanyl rate multiplier
n_fent_OD_mult = df_overdose["pe", "fent_OD_mult"],
n_fent_OD_mult_shape = df_overdose["shape", "fent_OD_mult"],
n_fent_OD_mult_scale = df_overdose["scale", "fent_OD_mult"],
n_fent_OD_mult_low = df_overdose["low", "fent_OD_mult"],
n_fent_OD_mult_high = df_overdose["high", "fent_OD_mult"],
# Probability of fentanyl exposure
#p_fent_exp = df_overdose["pe", "fent_exp_prob"],
p_ni_fent_reduction = df_overdose["pe", "ni_fent_reduction"],
#p_REL_fent_reduction = df_overdose["pe", "REL_fent_reduction"],
#p_TX_fent_reduction = df_overdose["pe", "TX_fent_reduction"],
#p_TXC_fent_reduction = df_overdose["pe", "TXC_fent_reduction"],
#p_ABS_fent_reduction = df_overdose["pe", "ABS_fent_reduction"],
# Projected growth rate in fentanyl exposure (person-month)
#n_fent_growth_rate = df_fentanyl["pe", "fent_growth"],
#p_fent_exp_base = df_fentanyl["2018", "AVG"],
#p_fent_exp = df_fentanyl["2020", "CAN"],
# Overall - Fentanyl prevalence
p_fent_exp_2018 = df_fentanyl["2018", "pe"],
p_fent_exp_2019 = df_fentanyl["2019", "pe"],
p_fent_exp_2020 = df_fentanyl["2020", "pe"],
# BC - Fentanyl prevalence
#p_fent_exp_2018_bc = df_fentanyl["2018", "BC"],
#p_fent_exp_2019_bc = df_fentanyl["2019", "BC"],
#p_fent_exp_2020_bc = df_fentanyl["2020", "BC"],
# AB - Fentanyl prevalence
#p_fent_exp_2018_ab = df_fentanyl["2018", "AB"],
#p_fent_exp_2019_ab = df_fentanyl["2019", "AB"],
#p_fent_exp_2020_ab = df_fentanyl["2020", "AB"],
# ON - Fentanyl prevalence
#p_fent_exp_2018_on = df_fentanyl["2018", "ON"],
#p_fent_exp_2019_on = df_fentanyl["2019", "ON"],
#p_fent_exp_2020_on = df_fentanyl["2020", "ON"],
# QC - Fentanyl prevalence
#p_fent_exp_2018_qc = df_fentanyl["2018", "QC"],
#p_fent_exp_2019_qc = df_fentanyl["2019", "QC"],
#p_fent_exp_2020_qc = df_fentanyl["2020", "QC"],
# Naloxone
p_witness = df_overdose["pe", "witness_prob"],
p_attended = df_overdose["pe", "attended_prob"],
p_NX_used = df_overdose["pe", "NX_prob"],
p_NX_success = df_overdose["pe", "NX_success_prob"],
#### Seroconversion ####
# HIV Seroconversion
# From negative
# Non-injection
p_HIV_NI = df_hiv["pe", "HIV_NI"],
p_HIV_BUP_NI = df_hiv["pe", "HIV_NI"],
p_HIV_BUPC_NI = df_hiv["pe", "HIV_NI"],
p_HIV_MET_NI = df_hiv["pe", "HIV_NI"],
p_HIV_METC_NI = df_hiv["pe", "HIV_NI"],
p_HIV_REL_NI = df_hiv["pe", "HIV_NI"],
p_HIV_ODN_NI = df_hiv["pe", "HIV_NI"],
p_HIV_ABS_NI = df_hiv["pe", "HIV_NI"],
# Injection
p_HIV_BUP_INJ = df_hiv["pe", "HIV_TX_INJ"],
p_HIV_MET_INJ = df_hiv["pe", "HIV_TX_INJ"],
p_HIV_BUPC_INJ = df_hiv["pe", "HIV_TXC_INJ"],
p_HIV_METC_INJ = df_hiv["pe", "HIV_TXC_INJ"],
p_HIV_REL_INJ = df_hiv["pe", "HIV_REL_INJ"],
p_HIV_ODN_INJ = df_hiv["pe", "HIV_REL_INJ"],
p_HIV_ABS_INJ = df_hiv["pe", "HIV_NI"],
# Co-infection conditional on HCV
# Non-injection
p_HCV_HIV_BUP_NI = df_hiv["pe", "COI_HIV_NI"],
p_HCV_HIV_BUPC_NI = df_hiv["pe", "COI_HIV_NI"],
p_HCV_HIV_MET_NI = df_hiv["pe", "COI_HIV_NI"],
p_HCV_HIV_METC_NI = df_hiv["pe", "COI_HIV_NI"],
p_HCV_HIV_REL_NI = df_hiv["pe", "COI_HIV_NI"],
p_HCV_HIV_ODN_NI = df_hiv["pe", "COI_HIV_NI"],
p_HCV_HIV_ABS_NI = df_hiv["pe", "COI_HIV_NI"],
# Injection
p_HCV_HIV_BUP_INJ = df_hiv["pe", "COI_HIV_TX_INJ"],
p_HCV_HIV_MET_INJ = df_hiv["pe", "COI_HIV_TX_INJ"],
p_HCV_HIV_BUPC_INJ = df_hiv["pe", "COI_HIV_TXC_INJ"],
p_HCV_HIV_METC_INJ = df_hiv["pe", "COI_HIV_TXC_INJ"],
p_HCV_HIV_REL_INJ = df_hiv["pe", "COI_HIV_REL_INJ"],
p_HCV_HIV_ODN_INJ = df_hiv["pe", "COI_HIV_REL_INJ"],
p_HCV_HIV_ABS_INJ = df_hiv["pe", "COI_HIV_NI"], # ABS same as non-injection
# HCV Seroconversion
# From negative
# Non-injection
p_HCV_BUP_NI = df_hcv["pe", "HCV_NI"],
p_HCV_BUPC_NI = df_hcv["pe", "HCV_NI"],
p_HCV_MET_NI = df_hcv["pe", "HCV_NI"],
p_HCV_METC_NI = df_hcv["pe", "HCV_NI"],
p_HCV_REL_NI = df_hcv["pe", "HCV_NI"],
p_HCV_ODN_NI = df_hcv["pe", "HCV_NI"],
p_HCV_ABS_NI = df_hcv["pe", "HCV_NI"],
# Injection
p_HCV_BUP_INJ = df_hcv["pe", "HCV_TX_INJ"],
p_HCV_MET_INJ = df_hcv["pe", "HCV_TX_INJ"],
p_HCV_BUPC_INJ = df_hcv["pe", "HCV_TXC_INJ"],
p_HCV_METC_INJ = df_hcv["pe", "HCV_TXC_INJ"],
p_HCV_REL_INJ = df_hcv["pe", "HCV_REL_INJ"],
p_HCV_ODN_INJ = df_hcv["pe", "HCV_REL_INJ"],
p_HCV_ABS_INJ = df_hcv["pe", "HCV_NI"], # ABS same as non-injection
# Co-infection conditional on HIV
# Non-injection
p_HIV_HCV_BUP_NI = df_hcv["pe", "COI_HCV_NI"],
p_HIV_HCV_BUPC_NI = df_hcv["pe", "COI_HCV_NI"],
p_HIV_HCV_MET_NI = df_hcv["pe", "COI_HCV_NI"],
p_HIV_HCV_METC_NI = df_hcv["pe", "COI_HCV_NI"],
p_HIV_HCV_REL_NI = df_hcv["pe", "COI_HCV_NI"],
p_HIV_HCV_ODN_NI = df_hcv["pe", "COI_HCV_NI"],
p_HIV_HCV_ABS_NI = df_hcv["pe", "COI_HCV_NI"],
# Injection
p_HIV_HCV_BUP_INJ = df_hcv["pe", "COI_HCV_TX_INJ"],
p_HIV_HCV_MET_INJ = df_hcv["pe", "COI_HCV_TX_INJ"],
p_HIV_HCV_BUPC_INJ = df_hcv["pe", "COI_HCV_TXC_INJ"],
p_HIV_HCV_METC_INJ = df_hcv["pe", "COI_HCV_TXC_INJ"],
p_HIV_HCV_REL_INJ = df_hcv["pe", "COI_HCV_REL_INJ"],
p_HIV_HCV_ODN_INJ = df_hcv["pe", "COI_HCV_REL_INJ"],
p_HIV_HCV_ABS_INJ = df_hcv["pe", "COI_HCV_NI"],
#### Costs ####
# Treatment Costs
c_BUP_TX = df_costs["pe", "BUP_TX"],
c_MET_TX = df_costs["pe", "MET_TX"],
c_OD_TX = df_costs["pe", "OD_TX"],
# HRU Costs
# Modify if age-specific
c_BUP_NI_HRU = df_costs["pe", "BUP_NI_HRU"],
c_BUPC_NI_HRU = df_costs["pe", "BUPC_NI_HRU"],
c_MET_NI_HRU = df_costs["pe", "MET_NI_HRU"],
c_METC_NI_HRU = df_costs["pe", "METC_NI_HRU"],
c_REL_NI_HRU = df_costs["pe", "REL_NI_HRU"],
c_ODN_NI_HRU = df_costs["pe", "ODN_NI_HRU"],
c_ODF_NI_HRU = df_costs["pe", "ODF_NI_HRU"],
c_ABS_NI_HRU = df_costs["pe", "ABS_NI_HRU"],
c_BUP_INJ_HRU = df_costs["pe", "BUP_INJ_HRU"],
c_BUPC_INJ_HRU = df_costs["pe", "BUPC_INJ_HRU"],
c_MET_INJ_HRU = df_costs["pe", "MET_INJ_HRU"],
c_METC_INJ_HRU = df_costs["pe", "METC_INJ_HRU"],
c_REL_INJ_HRU = df_costs["pe", "REL_INJ_HRU"],
c_ODN_INJ_HRU = df_costs["pe", "ODN_INJ_HRU"],
c_ODF_INJ_HRU = df_costs["pe", "ODF_INJ_HRU"],
c_ABS_INJ_HRU = df_costs["pe", "ABS_INJ_HRU"],
# HIV/HCV Costs
c_HIV_HRU = df_costs["pe", "HIV_HRU"],
c_HIV_ART = df_costs["pe", "HIV_ART"],
c_HCV_HRU = df_costs["pe", "HCV_HRU"],
c_HCV_DAA = df_costs["pe", "HCV_DAA"],
# Overdose Costs
c_OD_NX = df_costs["pe", "OD_NX"],
c_OD_AMB = df_costs["pe", "OD_AMB"],
# Crime Costs
c_BUP_NI_crime = df_crime_costs["pe", "BUP"],
c_BUPC_NI_crime = df_crime_costs["pe", "BUPC"],
c_MET_NI_crime = df_crime_costs["pe", "MET"],
c_METC_NI_crime = df_crime_costs["pe", "METC"],
c_REL_NI_crime = df_crime_costs["pe", "REL"],
c_ODN_NI_crime = df_crime_costs["pe", "REL"],
c_ODF_NI_crime = 0,
c_ABS_NI_crime = df_crime_costs["pe", "ABS"],
c_BUP_INJ_crime = df_crime_costs["pe", "BUP"],
c_BUPC_INJ_crime = df_crime_costs["pe", "BUPC"],
c_MET_INJ_crime = df_crime_costs["pe", "MET"],
c_METC_INJ_crime = df_crime_costs["pe", "METC"],
c_REL_INJ_crime = df_crime_costs["pe", "REL"],
c_ODN_INJ_crime = df_crime_costs["pe", "REL"],
c_ODF_INJ_crime = 0,
c_ABS_INJ_crime = df_crime_costs["pe", "ABS"],
#### QALYs ####
# HIV/HCV negative
u_BUP_NI_NEG = df_qalys["pe", "BUP"],
u_BUPC_NI_NEG = df_qalys["pe", "BUPC"],
u_MET_NI_NEG = df_qalys["pe", "MET"],
u_METC_NI_NEG = df_qalys["pe", "METC"],
u_REL_NI_NEG = df_qalys["pe", "REL"],
u_ODN_NI_NEG = df_qalys["pe", "ODN"],
u_ODF_NI_NEG = df_qalys["pe", "ODF"],
u_ABS_NI_NEG = df_qalys["pe", "ABS"],
u_BUP_INJ_NEG = df_qalys["pe", "BUP"],
u_BUPC_INJ_NEG = df_qalys["pe", "BUPC"],
u_MET_INJ_NEG = df_qalys["pe", "MET"],
u_METC_INJ_NEG = df_qalys["pe", "METC"],
u_REL_INJ_NEG = df_qalys["pe", "REL"],
u_ODN_INJ_NEG = df_qalys["pe", "ODN"],
u_ODF_INJ_NEG = df_qalys["pe", "ODF"],
u_ABS_INJ_NEG = df_qalys["pe", "ABS"],
u_HIV_mult = df_qalys["pe", "HIV_mult"], # HIV multiplier for negative states
u_HCV_mult = df_qalys["pe", "HCV_mult"], # HCV multiplier for negative states
u_COI_mult = df_qalys["pe", "COI_mult"]
) # Close list
return(l_params_all) # Return full parameter list
}
#' Update parameters
#'
#' \code{update_param_list} is used to update list of all parameters with new
#' values for specific parameters.
#'
#' @param l_params_all List with all parameters of decision model
#' @param params_updated Parameters for which values need to be updated
#' @return
#' A modifed list with all parameters updated.
#' @export
update_param_list <- function(l_params_all, params_updated){
if (typeof(params_updated)!="list"){
params_updated <- split(unname(params_updated),names(params_updated)) #convert the named vector to a list
}
l_params_all <- modifyList(l_params_all, params_updated) #update the values
return(l_params_all)
}
benenns/OUD-Model documentation built on Dec. 15, 2024, 3:52 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions update_param_list load_all_params load_mort_params
#' Load mortality data
#'
#' \code{load_mort_data} is used to load age-specific mortality from .csv file
#' into vector.
#'
#' @param file String with the location and name of the file with mortality
#' data.
#' @return
#' A vector with mortality by age.
#' @export
load_mort_params <- function(file.mort = NULL, n_male){
df_lt_can_2018 <- read.csv(file = file.mort)
v_r_mort_by_age_male <- df_lt_can_2018 %>% select(Male) %>% as.matrix()
v_r_mort_by_age_female <- df_lt_can_2018 %>% select(Female) %>% as.matrix()
v_r_mort_by_age <- (v_r_mort_by_age_male * n_male) + (v_r_mort_by_age_female * (1 - n_male)) # weighted mortality
return(v_r_mort_by_age)
}
#' Load all parameters
#'
#' \code{load_all_params} loads all parameters for the decision model from multiple sources and creates a list.
#'
#' @param file.init String with the location and name of the file with initial set of parameters
#' @param file.init_dist String with the location and name of the file with initial distributions
#' @param file.mort String with the location and name of the file with mortality data
#' @param file.death_hr String with the location and name of death hazard ratios
#' @param file.frailty String with the location and name of the file with frailty estimates for subsequent episodes in health states
#' @param file.weibull_scale String with the location and name of the file with weibull scale
#' @param file.weibull_shape String with the location and name of the file with weibull shape
#' @param file.unconditional String with the location and name of the file with empirical destination states
#' @param file.overdose String with the location and name of the file with overdose/fentanyl-related parameters
#' @param file.fentanyl String with the location and name of the file with fentanyl exposure parameters
#' @param file.hiv String with the location and name of the file with HIV seroconversion probabilities
#' @param file.hcv String with the location and name of the file with HCV seroconversion probabilities
#' @param file.costs String with the location and name of the file with costs (excluding crime costs)
#' @param file.crime_costs String with the location and name of the file with age-specific crime costs
#' @param file.qalys String with the location and name of the file with HRQoL weights
#'
#' @return
#' A list of all parameters used for the decision model.
#' @export
load_all_params <- function(file.init = NULL,
file.init_dist = NULL,
file.mort = NULL,
file.death_hr = NULL,
file.frailty = NULL,
file.weibull = NULL,
#file.weibull_scale = NULL,
#file.weibull_shape = NULL,
file.unconditional = NULL,
file.overdose = NULL,
file.fentanyl = NULL,
file.hiv = NULL,
file.hcv = NULL,
file.costs = NULL,
file.crime_costs = NULL,
file.qalys = NULL){ # User defined
#Load files of all baseline model parameters
df_init_params <- read.csv(file = file.init, row.names = 1, header = TRUE) # Initial parameter values
df_init_dist <- read.csv(file = file.init_dist, row.names = 1, header = TRUE) # Initial parameter values
df_death_hr <- read.csv(file = file.death_hr, row.names = 1, header = TRUE) # Mortality hazard ratios
df_frailty <- read.csv(file = file.frailty, row.names = 1, header = TRUE) # Episode frailty params
df_weibull <- read.csv(file = file.weibull, row.names = 1, header = TRUE) # Weibull params
#df_weibull_scale <- read.csv(file = file.weibull_scale, row.names = 1, header = TRUE) # Weibull scale params
#df_weibull_shape <- read.csv(file = file.weibull_shape, row.names = 1, header = TRUE) # Weibull shape params
df_UP <- read.csv(file = file.unconditional, row.names = 1, header = TRUE) # Unconditional transition probs
df_overdose <- read.csv(file = file.overdose, row.names = 1, header = TRUE) # Overdose-fentanyl parameters
df_fentanyl <- read.csv(file = file.fentanyl, row.names = 1, header = TRUE) # Fentanyl exposure parameters
df_hiv <- read.csv(file = file.hiv, row.names = 1, header = TRUE) # HIV seroconversion probs
df_hcv <- read.csv(file = file.hcv, row.names = 1, header = TRUE) # HCV seroconversion probs
df_costs <- read.csv(file = file.costs, row.names = 1, header = TRUE) # All costs excluding crime
df_crime_costs <- read.csv(file = file.crime_costs, row.names = 1, header = TRUE) # Crime costs
df_qalys <- read.csv(file = file.qalys, row.names = 1, header = TRUE) # QALYs
l_params_all <- list(
#### Initial parameters ####
n_age_init = df_init_params["pe", "age_init"], # age at baseline
n_age_max = df_init_params["pe", "age_max"], # maximum age of follow up
n_per = df_init_params["pe", "period_yr"], # periods per year (e.g. 12-months)
n_discount = df_init_params["pe", "discount"], # discount rate
n_cali_per = df_init_params["pe", "cali_per"], # number of calibration periods
n_male = df_init_params["pe", "male_prop"], # % male
n_INJ = df_init_params["pe", "INJ_prop"], # % injection
#n_HIV = df_init_params["pe", "hiv_prop"], # % of HIV-positive individuals
#n_HCV = df_init_params["pe", "hcv_prop"], # % of HCV-positive individuals
#n_COI = df_init_params["pe", "coi_prop"], # % of co-infected individuals
#Injection
n_HIV_INJ = df_init_params["pe", "HIV_prop_INJ"], # % of HIV-positive individuals
n_HCV_INJ = df_init_params["pe", "HCV_prop_INJ"], # % of HCV-positive individuals
n_COI_INJ = df_init_params["pe", "COI_prop_INJ"], # % of co-infected individuals
# Non-injection
n_HIV_NI = df_init_params["pe", "HIV_prop_NI"], # % of HIV-positive individuals
n_HCV_NI = df_init_params["pe", "HCV_prop_NI"], # % of HCV-positive individuals
n_COI_NI = df_init_params["pe", "COI_prop_NI"], # % of co-infected individuals
n_HIV_ART = df_init_params["pe", "ART_prop"], # % of HIV-positive on-ART (used to calculate costs)
n_HCV_DAA = df_init_params["pe", "DAA_prop"], # % of HIV-positive on-ART (used to calculate costs)
#### Initial state distribution ####
v_init_dist = as.vector(df_init_dist["pe", ]),
#### Mortality ####
v_r_mort_by_age = load_mort_params(file = file.mort, n_male = df_init_params["pe", "male_prop"]), # vector of age-specific mortality
#### Hazard ratios for death probability ####
# ***NEW ESTIMATES*** #
# Non-injection
hr_BUP_NI = df_death_hr["pe", "TX"],
hr_BUPC_NI = df_death_hr["pe", "TX"],
hr_MET_NI = df_death_hr["pe", "TX"],
hr_METC_NI = df_death_hr["pe", "TX"],
hr_REL_NI = df_death_hr["pe", "REL"],
hr_ODN_NI = df_death_hr["pe", "REL"],
hr_ABS_NI = df_death_hr["pe", "ABS"],
hr_HIV_NI = df_death_hr["pe", "HIV"],
hr_HCV_NI = df_death_hr["pe", "HCV"],
hr_COI_NI = df_death_hr["pe", "COI"],
# Injection
hr_BUP_INJ = df_death_hr["pe", "TX"],
hr_BUPC_INJ = df_death_hr["pe", "TX"],
hr_MET_INJ = df_death_hr["pe", "TX"],
hr_METC_INJ = df_death_hr["pe", "TX"],
hr_REL_INJ = df_death_hr["pe", "REL"],
hr_ODN_INJ = df_death_hr["pe", "REL"],
hr_ABS_INJ = df_death_hr["pe", "ABS"],
hr_HIV_INJ = df_death_hr["pe", "HIV"],
hr_HCV_INJ = df_death_hr["pe", "HCV"],
hr_COI_INJ = df_death_hr["pe", "COI"],
# Non-injection
#hr_BUP_NI = df_death_hr["pe", "BUP_NI"],
#hr_BUPC_NI = df_death_hr["pe", "BUPC_NI"],
#hr_MET_NI = df_death_hr["pe", "MET_NI"],
#hr_METC_NI = df_death_hr["pe", "METC_NI"],
#hr_REL_NI = df_death_hr["pe", "REL_NI"],
#hr_ODN_NI = df_death_hr["pe", "ODN_NI"],
#hr_ABS_NI = df_death_hr["pe", "ABS_NI"],
#hr_HIV_NI = df_death_hr["pe", "HIV_NI"],
#hr_HCV_NI = df_death_hr["pe", "HCV_NI"],
#hr_COI_NI = df_death_hr["pe", "COI_NI"],
# Injection
#hr_BUP_INJ = df_death_hr["pe", "BUP_INJ"],
#hr_BUPC_INJ = df_death_hr["pe", "BUPC_INJ"],
#hr_MET_INJ = df_death_hr["pe", "MET_INJ"],
#hr_METC_INJ = df_death_hr["pe", "METC_INJ"],
#hr_REL_INJ = df_death_hr["pe", "REL_INJ"],
#hr_ODN_INJ = df_death_hr["pe", "ODN_INJ"],
#hr_ABS_INJ = df_death_hr["pe", "ABS_INJ"],
#hr_HIV_INJ = df_death_hr["pe", "HIV_INJ"],
#hr_HCV_INJ = df_death_hr["pe", "HCV_INJ"],
#hr_COI_INJ = df_death_hr["pe", "COI_INJ"],
#### Frailty estimates for successive episodes, injection vs. non-injection, concurrent opioid use ####
# ***NEW ESTIMATES*** #
# Episodes
p_frailty_BUP_1 = 1,
p_frailty_BUP_2 = df_frailty["pe", "BUP_2"],
p_frailty_BUP_3 = df_frailty["pe", "BUP_3"],
p_frailty_MET_1 = 1,
p_frailty_MET_2 = df_frailty["pe", "MET_2"],
p_frailty_MET_3 = df_frailty["pe", "MET_3"],
p_frailty_REL_1 = 1,
p_frailty_REL_2 = df_frailty["pe", "REL_2"],
p_frailty_REL_3 = df_frailty["pe", "REL_3"],
p_frailty_ABS_1 = 1,
p_frailty_ABS_2 = df_frailty["pe", "ABS_2"],
p_frailty_ABS_3 = df_frailty["pe", "ABS_3"],
# Injection vs. non-injection
p_frailty_BUP_INJ = df_frailty["pe", "BUP_INJ"],
p_frailty_MET_INJ = df_frailty["pe", "MET_INJ"],
p_frailty_REL_INJ = df_frailty["pe", "REL_INJ"],
p_frailty_ABS_INJ = df_frailty["pe", "ABS_INJ"],
# Concurrent opioid use
p_frailty_BUPC = df_frailty["pe", "BUPC"],
p_frailty_METC = df_frailty["pe", "METC"],
# ***OLD ESTIMATES*** #
# Non-injection
#p_frailty_BUP_NI_1 = 1,
#p_frailty_BUP_NI_2 = df_frailty["pe", "BUP_NI_2"],
#p_frailty_BUP_NI_3 = df_frailty["pe", "BUP_NI_3"],
#p_frailty_BUPC_NI_1 = 1,
#p_frailty_BUPC_NI_2 = df_frailty["pe", "BUPC_NI_2"],
#p_frailty_BUPC_NI_3 = df_frailty["pe", "BUPC_NI_3"],
#p_frailty_MET_NI_1 = 1,
#p_frailty_MET_NI_2 = df_frailty["pe", "MET_NI_2"],
#p_frailty_MET_NI_3 = df_frailty["pe", "MET_NI_3"],
#p_frailty_METC_NI_1 = 1,
#p_frailty_METC_NI_2 = df_frailty["pe", "METC_NI_2"],
#p_frailty_METC_NI_3 = df_frailty["pe", "METC_NI_3"],
#p_frailty_ABS_NI_1 = 1,
#p_frailty_ABS_NI_2 = df_frailty["pe", "ABS_NI_2"],
#p_frailty_ABS_NI_3 = df_frailty["pe", "ABS_NI_3"],
#p_frailty_REL_NI_1 = 1,
#p_frailty_REL_NI_2 = df_frailty["pe", "REL_NI_2"],
#p_frailty_REL_NI_3 = df_frailty["pe", "REL_NI_3"],
# Injection
#p_frailty_BUP_INJ_1 = 1,
#p_frailty_BUP_INJ_2 = df_frailty["pe", "BUP_INJ_2"],
#p_frailty_BUP_INJ_3 = df_frailty["pe", "BUP_INJ_3"],
#p_frailty_BUPC_INJ_1 = 1,
#p_frailty_BUPC_INJ_2 = df_frailty["pe", "BUPC_INJ_2"],
#p_frailty_BUPC_INJ_3 = df_frailty["pe", "BUPC_INJ_3"],
#p_frailty_MET_INJ_1 = 1,
#p_frailty_MET_INJ_2 = df_frailty["pe", "MET_INJ_2"],
#p_frailty_MET_INJ_3 = df_frailty["pe", "MET_INJ_3"],
#p_frailty_METC_INJ_1 = 1,
#p_frailty_METC_INJ_2 = df_frailty["pe", "METC_INJ_2"],
#p_frailty_METC_INJ_3 = df_frailty["pe", "METC_INJ_3"],
#p_frailty_ABS_INJ_1 = 1,
#p_frailty_ABS_INJ_2 = df_frailty["pe", "ABS_INJ_2"],
#p_frailty_ABS_INJ_3 = df_frailty["pe", "ABS_INJ_3"],
#p_frailty_REL_INJ_1 = 1,
#p_frailty_REL_INJ_2 = df_frailty["pe", "REL_INJ_2"],
#p_frailty_REL_INJ_3 = df_frailty["pe", "REL_INJ_3"],
#### Load weibull parameters ####
# From OPTIMA trial
# Shape
p_weibull_shape_BUP = df_weibull["pe", "BUP_shape_1"],
p_weibull_shape_MET = df_weibull["pe", "MET_shape_1"],
p_weibull_shape_REL = df_weibull["pe", "REL_shape_1"],
p_weibull_shape_ABS = df_weibull["pe", "ABS_shape_1"],
# scale
p_weibull_scale_BUP = df_weibull["pe", "BUP_scale_1"],
p_weibull_scale_MET = df_weibull["pe", "MET_scale_1"],
p_weibull_scale_REL = df_weibull["pe", "REL_scale_1"],
p_weibull_scale_ABS = df_weibull["pe", "ABS_scale_1"],
# Parameters for episode 1, 2, 3+
# BUP
# Scale
#p_weibull_scale_BUP_1 = df_weibull["pe", "BUP_scale_1"],
#p_weibull_scale_BUP_2 = df_weibull["pe", "BUP_scale_2"],
#p_weibull_scale_BUP_3 = df_weibull["pe", "BUP_scale_3"],
# Shape
#p_weibull_shape_BUP_1 = df_weibull["pe", "BUP_shape_1"],
#p_weibull_shape_BUP_2 = df_weibull["pe", "BUP_shape_2"],
#p_weibull_shape_BUP_3 = df_weibull["pe", "BUP_shape_3"],
# MET
# Scale
#p_weibull_scale_MET_1 = df_weibull["pe", "MET_scale_1"],
#p_weibull_scale_MET_2 = df_weibull["pe", "MET_scale_2"],
#p_weibull_scale_MET_3 = df_weibull["pe", "MET_scale_3"],
# Shape
#p_weibull_shape_MET_1 = df_weibull["pe", "MET_shape_1"],
#p_weibull_shape_MET_2 = df_weibull["pe", "MET_shape_2"],
#p_weibull_shape_MET_3 = df_weibull["pe", "MET_shape_3"],
# REL
# Scale
#p_weibull_scale_REL_1 = df_weibull["pe", "REL_scale_1"],
#p_weibull_scale_REL_2 = df_weibull["pe", "REL_scale_2"],
#p_weibull_scale_REL_3 = df_weibull["pe", "REL_scale_3"],
# Shape
#p_weibull_shape_REL_1 = df_weibull["pe", "REL_shape_1"],
#p_weibull_shape_REL_2 = df_weibull["pe", "REL_shape_2"],
#p_weibull_shape_REL_3 = df_weibull["pe", "REL_shape_3"],
# ABS
# Scale
#p_weibull_scale_ABS_1 = df_weibull["pe", "ABS_scale_1"],
#p_weibull_scale_ABS_2 = df_weibull["pe", "ABS_scale_2"],
#p_weibull_scale_ABS_3 = df_weibull["pe", "ABS_scale_3"],
# Shape
#p_weibull_shape_ABS_1 = df_weibull["pe", "ABS_shape_1"],
#p_weibull_shape_ABS_2 = df_weibull["pe", "ABS_shape_2"],
#p_weibull_shape_ABS_3 = df_weibull["pe", "ABS_shape_3"],
# Weibull scale
#p_weibull_scale_BUP_NI = df_weibull_scale["pe", "BUP_NI"],
#p_weibull_scale_BUPC_NI = df_weibull_scale["pe", "BUP_NI"], # same estimates for concurrent use (adjustment with frailty)
#p_weibull_scale_MET_NI = df_weibull_scale["pe", "MET_NI"],
#p_weibull_scale_METC_NI = df_weibull_scale["pe", "MET_NI"], # same estimates for concurrent use (adjustment with frailty)
#p_weibull_scale_REL_NI = df_weibull_scale["pe", "REL_NI"],
#p_weibull_scale_ABS_NI = df_weibull_scale["pe", "ABS_NI"],
#
#p_weibull_scale_BUP_INJ = df_weibull_scale["pe", "BUP_INJ"],
#p_weibull_scale_BUPC_INJ = df_weibull_scale["pe", "BUP_INJ"], # same estimates for concurrent use (adjustment with frailty)
#p_weibull_scale_MET_INJ = df_weibull_scale["pe", "MET_INJ"],
#p_weibull_scale_METC_INJ = df_weibull_scale["pe", "MET_INJ"], # same estimates for concurrent use (adjustment with frailty)
#p_weibull_scale_REL_INJ = df_weibull_scale["pe", "REL_INJ"],
#p_weibull_scale_ABS_INJ = df_weibull_scale["pe", "ABS_INJ"],
# Weibull shape
#p_weibull_shape_BUP_NI = df_weibull_shape["pe", "BUP_NI"],
#p_weibull_shape_BUPC_NI = df_weibull_shape["pe", "BUP_NI"], # same estimates for concurrent use (adjustment with frailty)
#p_weibull_shape_MET_NI = df_weibull_shape["pe", "MET_NI"],
#p_weibull_shape_METC_NI = df_weibull_shape["pe", "MET_NI"], # same estimates for concurrent use (adjustment with frailty)
#p_weibull_shape_REL_NI = df_weibull_shape["pe", "REL_NI"],
#p_weibull_shape_ABS_NI = df_weibull_shape["pe", "ABS_NI"],
#
#p_weibull_shape_BUP_INJ = df_weibull_shape["pe", "BUP_INJ"],
#p_weibull_shape_BUPC_INJ = df_weibull_shape["pe", "BUP_INJ"], # same estimates for concurrent use (adjustment with frailty)
#p_weibull_shape_MET_INJ = df_weibull_shape["pe", "MET_INJ"],
#p_weibull_shape_METC_INJ = df_weibull_shape["pe", "MET_INJ"], # same estimates for concurrent use (adjustment with frailty)
#p_weibull_shape_REL_INJ = df_weibull_shape["pe", "REL_INJ"],
#p_weibull_shape_ABS_INJ = df_weibull_shape["pe", "ABS_INJ"],
#### Unconditional transition probabilities ####
# Non-Injection
# From BUP
p_BUP_BUPC_NI = df_UP["BUP_NI", "BUPC_NI"],
p_BUP_MET_NI = df_UP["BUP_NI", "MET_NI"],
p_BUP_METC_NI = df_UP["BUP_NI", "METC_NI"],
p_BUP_ABS_NI = df_UP["BUP_NI", "ABS_NI"],
p_BUP_REL_NI = df_UP["BUP_NI", "REL_NI"],
# From BUPC
p_BUPC_BUP_NI = df_UP["BUPC_NI", "BUP_NI"],
p_BUPC_MET_NI = df_UP["BUPC_NI", "MET_NI"],
p_BUPC_METC_NI = df_UP["BUPC_NI", "METC_NI"],
p_BUPC_ABS_NI = df_UP["BUPC_NI", "ABS_NI"],
p_BUPC_REL_NI = df_UP["BUPC_NI", "REL_NI"],
# From MET
p_MET_METC_NI = df_UP["MET_NI", "METC_NI"],
p_MET_BUP_NI = df_UP["MET_NI", "BUP_NI"],
p_MET_BUPC_NI = df_UP["MET_NI", "BUPC_NI"],
p_MET_ABS_NI = df_UP["MET_NI", "ABS_NI"],
p_MET_REL_NI = df_UP["MET_NI", "REL_NI"],
# From METC
p_METC_MET_NI = df_UP["METC_NI", "MET_NI"],
p_METC_BUP_NI = df_UP["METC_NI", "BUP_NI"],
p_METC_BUPC_NI = df_UP["METC_NI", "BUPC_NI"],
p_METC_ABS_NI = df_UP["METC_NI", "ABS_NI"],
p_METC_REL_NI = df_UP["METC_NI", "REL_NI"],
# From ABS
p_ABS_REL_NI = df_UP["ABS_NI", "REL_NI"],
p_ABS_MET_NI = df_UP["ABS_NI", "MET_NI"],
p_ABS_METC_NI = df_UP["ABS_NI", "METC_NI"],
p_ABS_BUP_NI = df_UP["ABS_NI", "BUP_NI"],
p_ABS_BUPC_NI = df_UP["ABS_NI", "BUPC_NI"],
# From REL
p_REL_MET_NI = df_UP["REL_NI", "MET_NI"],
p_REL_METC_NI = df_UP["REL_NI", "METC_NI"],
p_REL_BUP_NI = df_UP["REL_NI", "BUP_NI"],
p_REL_BUPC_NI = df_UP["REL_NI", "BUPC_NI"],
p_REL_ABS_NI = df_UP["REL_NI", "ABS_NI"],
# From OD
p_ODN_MET_NI = df_UP["ODN_NI", "MET_NI"],
p_ODN_METC_NI = df_UP["ODN_NI", "METC_NI"],
p_ODN_BUP_NI = df_UP["ODN_NI", "BUP_NI"],
p_ODN_BUPC_NI = df_UP["ODN_NI", "BUPC_NI"],
p_ODN_ABS_NI = df_UP["ODN_NI", "ABS_NI"],
p_ODN_REL_NI = df_UP["ODN_NI", "REL_NI"],
# Inj
# From BUP & BUPC
p_BUP_BUPC_INJ = df_UP["BUP_INJ", "BUPC_INJ"],
p_BUP_MET_INJ = df_UP["BUP_INJ", "MET_INJ"],
p_BUP_METC_INJ = df_UP["BUP_INJ", "METC_INJ"],
p_BUP_ABS_INJ = df_UP["BUP_INJ", "ABS_INJ"],
p_BUP_REL_INJ = df_UP["BUP_INJ", "REL_INJ"],
p_BUPC_BUP_INJ = df_UP["BUPC_INJ", "BUP_INJ"],
p_BUPC_MET_INJ = df_UP["BUPC_INJ", "MET_INJ"],
p_BUPC_METC_INJ = df_UP["BUPC_INJ", "METC_INJ"],
p_BUPC_ABS_INJ = df_UP["BUPC_INJ", "ABS_INJ"],
p_BUPC_REL_INJ = df_UP["BUPC_INJ", "REL_INJ"],
# From MET & METC
p_MET_METC_INJ = df_UP["MET_INJ", "METC_INJ"],
p_MET_BUP_INJ = df_UP["MET_INJ", "BUP_INJ"],
p_MET_BUPC_INJ = df_UP["MET_INJ", "BUPC_INJ"],
p_MET_ABS_INJ = df_UP["MET_INJ", "ABS_INJ"],
p_MET_REL_INJ = df_UP["MET_INJ", "REL_INJ"],
p_METC_MET_INJ = df_UP["METC_INJ", "MET_INJ"],
p_METC_BUP_INJ = df_UP["METC_INJ", "BUP_INJ"],
p_METC_BUPC_INJ = df_UP["METC_INJ", "BUPC_INJ"],
p_METC_ABS_INJ = df_UP["METC_INJ", "ABS_INJ"],
p_METC_REL_INJ = df_UP["METC_INJ", "REL_INJ"],
# From ABS
p_ABS_REL_INJ = df_UP["ABS_INJ", "REL_INJ"],
p_ABS_MET_INJ = df_UP["ABS_INJ", "MET_INJ"],
p_ABS_METC_INJ = df_UP["ABS_INJ", "METC_INJ"],
p_ABS_BUP_INJ = df_UP["ABS_INJ", "BUP_INJ"],
p_ABS_BUPC_INJ = df_UP["ABS_INJ", "BUPC_INJ"],
# From REL
p_REL_MET_INJ = df_UP["REL_INJ", "MET_INJ"],
p_REL_METC_INJ = df_UP["REL_INJ", "METC_INJ"],
p_REL_BUP_INJ = df_UP["REL_INJ", "BUP_INJ"],
p_REL_BUPC_INJ = df_UP["REL_INJ", "BUPC_INJ"],
p_REL_ABS_INJ = df_UP["REL_INJ", "ABS_INJ"],
# From OD
p_ODN_MET_INJ = df_UP["ODN_INJ", "MET_INJ"],
p_ODN_METC_INJ = df_UP["ODN_INJ", "METC_INJ"],
p_ODN_BUP_INJ = df_UP["ODN_INJ", "BUP_INJ"],
p_ODN_BUPC_INJ = df_UP["ODN_INJ", "BUPC_INJ"],
p_ODN_ABS_INJ = df_UP["ODN_INJ", "ABS_INJ"],
p_ODN_REL_INJ = df_UP["ODN_INJ", "REL_INJ"],
#### Overdose ####
# Overdose parameters loaded as rates and converted to probabilities in model
# Includes additional calibration related parameters
# Base overdose params
n_TX_OD = df_overdose["pe", "TX_OD"],
n_TXC_OD = df_overdose["pe", "TXC_OD"],
n_REL_OD = df_overdose["pe", "REL_OD"],
n_ABS_OD = df_overdose["pe", "ABS_OD"],
# Gamma shape parameter (prior)
n_TX_OD_shape = df_overdose["shape", "TX_OD"],
n_TXC_OD_shape = df_overdose["shape", "TXC_OD"],
n_REL_OD_shape = df_overdose["shape", "REL_OD"],
n_ABS_OD_low = df_overdose["low", "ABS_OD"],
# Gamma scale parameter (prior)
n_TX_OD_scale = df_overdose["scale", "TX_OD"],
n_TXC_OD_scale = df_overdose["scale", "TXC_OD"],
n_REL_OD_scale = df_overdose["scale", "REL_OD"],
n_ABS_OD_high = df_overdose["high", "ABS_OD"],
# Overdose transition multipliers
# First 4 weeks of treatment and relapse
# Treatment states
#n_TX_OD_mult = df_overdose["pe", "TX_OD_mult"],
#n_TX_OD_mult_shape = df_overdose["shape", "TX_OD_mult"],
#n_TX_OD_mult_scale = df_overdose["scale", "TX_OD_mult"],
# BUP
n_BUP_OD_mult = df_overdose["pe", "BUP_OD_mult"],
n_BUP_OD_mult_shape = df_overdose["shape", "BUP_OD_mult"],
n_BUP_OD_mult_scale = df_overdose["scale", "BUP_OD_mult"],
# MET
n_MET_OD_mult = df_overdose["pe", "MET_OD_mult"],
n_MET_OD_mult_shape = df_overdose["shape", "MET_OD_mult"],
n_MET_OD_mult_scale = df_overdose["scale", "MET_OD_mult"],
# Treatment + concurrent opioid
n_TXC_OD_mult = df_overdose["pe", "TXC_OD_mult"],
n_TXC_OD_mult_shape = df_overdose["shape", "TXC_OD_mult"],
n_TXC_OD_mult_scale = df_overdose["scale", "TXC_OD_mult"],
# Relapse
n_REL_OD_mult = df_overdose["pe", "REL_OD_mult"],
n_REL_OD_mult_shape = df_overdose["shape", "REL_OD_mult"],
n_REL_OD_mult_scale = df_overdose["scale", "REL_OD_mult"],
# Abstinence
n_ABS_OD_mult = df_overdose["pe", "ABS_OD_mult"],
n_ABS_OD_mult_shape = df_overdose["shape", "ABS_OD_mult"],
n_ABS_OD_mult_scale = df_overdose["scale", "ABS_OD_mult"],
# Injection (vs. non-injection)
n_INJ_OD_mult = df_overdose["pe", "INJ_OD_mult"],
n_INJ_OD_mult_shape = df_overdose["shape", "INJ_OD_mult"],
n_INJ_OD_mult_scale = df_overdose["scale", "INJ_OD_mult"],
# Fatal overdose (conditional on overdose)
n_fatal_OD = df_overdose["pe", "fatal_OD"],
n_fatal_OD_shape = df_overdose["shape", "fatal_OD"],
n_fatal_OD_scale = df_overdose["scale", "fatal_OD"],
# Fentanyl
# Rate of fentanyl overdose
#n_fent_OD = df_overdose["pe", "fent_OD_rate"],
# Fentanyl rate multiplier
n_fent_OD_mult = df_overdose["pe", "fent_OD_mult"],
n_fent_OD_mult_shape = df_overdose["shape", "fent_OD_mult"],
n_fent_OD_mult_scale = df_overdose["scale", "fent_OD_mult"],
n_fent_OD_mult_low = df_overdose["low", "fent_OD_mult"],
n_fent_OD_mult_high = df_overdose["high", "fent_OD_mult"],
# Probability of fentanyl exposure
#p_fent_exp = df_overdose["pe", "fent_exp_prob"],
p_ni_fent_reduction = df_overdose["pe", "ni_fent_reduction"],
#p_REL_fent_reduction = df_overdose["pe", "REL_fent_reduction"],
#p_TX_fent_reduction = df_overdose["pe", "TX_fent_reduction"],
#p_TXC_fent_reduction = df_overdose["pe", "TXC_fent_reduction"],
#p_ABS_fent_reduction = df_overdose["pe", "ABS_fent_reduction"],
# Projected growth rate in fentanyl exposure (person-month)
#n_fent_growth_rate = df_fentanyl["pe", "fent_growth"],
#p_fent_exp_base = df_fentanyl["2018", "AVG"],
#p_fent_exp = df_fentanyl["2020", "CAN"],
# Overall - Fentanyl prevalence
p_fent_exp_2018 = df_fentanyl["2018", "pe"],
p_fent_exp_2019 = df_fentanyl["2019", "pe"],
p_fent_exp_2020 = df_fentanyl["2020", "pe"],
# BC - Fentanyl prevalence
#p_fent_exp_2018_bc = df_fentanyl["2018", "BC"],
#p_fent_exp_2019_bc = df_fentanyl["2019", "BC"],
#p_fent_exp_2020_bc = df_fentanyl["2020", "BC"],
# AB - Fentanyl prevalence
#p_fent_exp_2018_ab = df_fentanyl["2018", "AB"],
#p_fent_exp_2019_ab = df_fentanyl["2019", "AB"],
#p_fent_exp_2020_ab = df_fentanyl["2020", "AB"],
# ON - Fentanyl prevalence
#p_fent_exp_2018_on = df_fentanyl["2018", "ON"],
#p_fent_exp_2019_on = df_fentanyl["2019", "ON"],
#p_fent_exp_2020_on = df_fentanyl["2020", "ON"],
# QC - Fentanyl prevalence
#p_fent_exp_2018_qc = df_fentanyl["2018", "QC"],
#p_fent_exp_2019_qc = df_fentanyl["2019", "QC"],
#p_fent_exp_2020_qc = df_fentanyl["2020", "QC"],
# Naloxone
p_witness = df_overdose["pe", "witness_prob"],
p_attended = df_overdose["pe", "attended_prob"],
p_NX_used = df_overdose["pe", "NX_prob"],
p_NX_success = df_overdose["pe", "NX_success_prob"],
#### Seroconversion ####
# HIV Seroconversion
# From negative
# Non-injection
p_HIV_NI = df_hiv["pe", "HIV_NI"],
p_HIV_BUP_NI = df_hiv["pe", "HIV_NI"],
p_HIV_BUPC_NI = df_hiv["pe", "HIV_NI"],
p_HIV_MET_NI = df_hiv["pe", "HIV_NI"],
p_HIV_METC_NI = df_hiv["pe", "HIV_NI"],
p_HIV_REL_NI = df_hiv["pe", "HIV_NI"],
p_HIV_ODN_NI = df_hiv["pe", "HIV_NI"],
p_HIV_ABS_NI = df_hiv["pe", "HIV_NI"],
# Injection
p_HIV_BUP_INJ = df_hiv["pe", "HIV_TX_INJ"],
p_HIV_MET_INJ = df_hiv["pe", "HIV_TX_INJ"],
p_HIV_BUPC_INJ = df_hiv["pe", "HIV_TXC_INJ"],
p_HIV_METC_INJ = df_hiv["pe", "HIV_TXC_INJ"],
p_HIV_REL_INJ = df_hiv["pe", "HIV_REL_INJ"],
p_HIV_ODN_INJ = df_hiv["pe", "HIV_REL_INJ"],
p_HIV_ABS_INJ = df_hiv["pe", "HIV_NI"],
# Co-infection conditional on HCV
# Non-injection
p_HCV_HIV_BUP_NI = df_hiv["pe", "COI_HIV_NI"],
p_HCV_HIV_BUPC_NI = df_hiv["pe", "COI_HIV_NI"],
p_HCV_HIV_MET_NI = df_hiv["pe", "COI_HIV_NI"],
p_HCV_HIV_METC_NI = df_hiv["pe", "COI_HIV_NI"],
p_HCV_HIV_REL_NI = df_hiv["pe", "COI_HIV_NI"],
p_HCV_HIV_ODN_NI = df_hiv["pe", "COI_HIV_NI"],
p_HCV_HIV_ABS_NI = df_hiv["pe", "COI_HIV_NI"],
# Injection
p_HCV_HIV_BUP_INJ = df_hiv["pe", "COI_HIV_TX_INJ"],
p_HCV_HIV_MET_INJ = df_hiv["pe", "COI_HIV_TX_INJ"],
p_HCV_HIV_BUPC_INJ = df_hiv["pe", "COI_HIV_TXC_INJ"],
p_HCV_HIV_METC_INJ = df_hiv["pe", "COI_HIV_TXC_INJ"],
p_HCV_HIV_REL_INJ = df_hiv["pe", "COI_HIV_REL_INJ"],
p_HCV_HIV_ODN_INJ = df_hiv["pe", "COI_HIV_REL_INJ"],
p_HCV_HIV_ABS_INJ = df_hiv["pe", "COI_HIV_NI"], # ABS same as non-injection
# HCV Seroconversion
# From negative
# Non-injection
p_HCV_BUP_NI = df_hcv["pe", "HCV_NI"],
p_HCV_BUPC_NI = df_hcv["pe", "HCV_NI"],
p_HCV_MET_NI = df_hcv["pe", "HCV_NI"],
p_HCV_METC_NI = df_hcv["pe", "HCV_NI"],
p_HCV_REL_NI = df_hcv["pe", "HCV_NI"],
p_HCV_ODN_NI = df_hcv["pe", "HCV_NI"],
p_HCV_ABS_NI = df_hcv["pe", "HCV_NI"],
# Injection
p_HCV_BUP_INJ = df_hcv["pe", "HCV_TX_INJ"],
p_HCV_MET_INJ = df_hcv["pe", "HCV_TX_INJ"],
p_HCV_BUPC_INJ = df_hcv["pe", "HCV_TXC_INJ"],
p_HCV_METC_INJ = df_hcv["pe", "HCV_TXC_INJ"],
p_HCV_REL_INJ = df_hcv["pe", "HCV_REL_INJ"],
p_HCV_ODN_INJ = df_hcv["pe", "HCV_REL_INJ"],
p_HCV_ABS_INJ = df_hcv["pe", "HCV_NI"], # ABS same as non-injection
# Co-infection conditional on HIV
# Non-injection
p_HIV_HCV_BUP_NI = df_hcv["pe", "COI_HCV_NI"],
p_HIV_HCV_BUPC_NI = df_hcv["pe", "COI_HCV_NI"],
p_HIV_HCV_MET_NI = df_hcv["pe", "COI_HCV_NI"],
p_HIV_HCV_METC_NI = df_hcv["pe", "COI_HCV_NI"],
p_HIV_HCV_REL_NI = df_hcv["pe", "COI_HCV_NI"],
p_HIV_HCV_ODN_NI = df_hcv["pe", "COI_HCV_NI"],
p_HIV_HCV_ABS_NI = df_hcv["pe", "COI_HCV_NI"],
# Injection
p_HIV_HCV_BUP_INJ = df_hcv["pe", "COI_HCV_TX_INJ"],
p_HIV_HCV_MET_INJ = df_hcv["pe", "COI_HCV_TX_INJ"],
p_HIV_HCV_BUPC_INJ = df_hcv["pe", "COI_HCV_TXC_INJ"],
p_HIV_HCV_METC_INJ = df_hcv["pe", "COI_HCV_TXC_INJ"],
p_HIV_HCV_REL_INJ = df_hcv["pe", "COI_HCV_REL_INJ"],
p_HIV_HCV_ODN_INJ = df_hcv["pe", "COI_HCV_REL_INJ"],
p_HIV_HCV_ABS_INJ = df_hcv["pe", "COI_HCV_NI"],
#### Costs ####
# Treatment Costs
c_BUP_TX = df_costs["pe", "BUP_TX"],
c_MET_TX = df_costs["pe", "MET_TX"],
c_OD_TX = df_costs["pe", "OD_TX"],
# HRU Costs
# Modify if age-specific
c_BUP_NI_HRU = df_costs["pe", "BUP_NI_HRU"],
c_BUPC_NI_HRU = df_costs["pe", "BUPC_NI_HRU"],
c_MET_NI_HRU = df_costs["pe", "MET_NI_HRU"],
c_METC_NI_HRU = df_costs["pe", "METC_NI_HRU"],
c_REL_NI_HRU = df_costs["pe", "REL_NI_HRU"],
c_ODN_NI_HRU = df_costs["pe", "ODN_NI_HRU"],
c_ODF_NI_HRU = df_costs["pe", "ODF_NI_HRU"],
c_ABS_NI_HRU = df_costs["pe", "ABS_NI_HRU"],
c_BUP_INJ_HRU = df_costs["pe", "BUP_INJ_HRU"],
c_BUPC_INJ_HRU = df_costs["pe", "BUPC_INJ_HRU"],
c_MET_INJ_HRU = df_costs["pe", "MET_INJ_HRU"],
c_METC_INJ_HRU = df_costs["pe", "METC_INJ_HRU"],
c_REL_INJ_HRU = df_costs["pe", "REL_INJ_HRU"],
c_ODN_INJ_HRU = df_costs["pe", "ODN_INJ_HRU"],
c_ODF_INJ_HRU = df_costs["pe", "ODF_INJ_HRU"],
c_ABS_INJ_HRU = df_costs["pe", "ABS_INJ_HRU"],
# HIV/HCV Costs
c_HIV_HRU = df_costs["pe", "HIV_HRU"],
c_HIV_ART = df_costs["pe", "HIV_ART"],
c_HCV_HRU = df_costs["pe", "HCV_HRU"],
c_HCV_DAA = df_costs["pe", "HCV_DAA"],
# Overdose Costs
c_OD_NX = df_costs["pe", "OD_NX"],
c_OD_AMB = df_costs["pe", "OD_AMB"],
# Crime Costs
c_BUP_NI_crime = df_crime_costs["pe", "BUP"],
c_BUPC_NI_crime = df_crime_costs["pe", "BUPC"],
c_MET_NI_crime = df_crime_costs["pe", "MET"],
c_METC_NI_crime = df_crime_costs["pe", "METC"],
c_REL_NI_crime = df_crime_costs["pe", "REL"],
c_ODN_NI_crime = df_crime_costs["pe", "REL"],
c_ODF_NI_crime = 0,
c_ABS_NI_crime = df_crime_costs["pe", "ABS"],
c_BUP_INJ_crime = df_crime_costs["pe", "BUP"],
c_BUPC_INJ_crime = df_crime_costs["pe", "BUPC"],
c_MET_INJ_crime = df_crime_costs["pe", "MET"],
c_METC_INJ_crime = df_crime_costs["pe", "METC"],
c_REL_INJ_crime = df_crime_costs["pe", "REL"],
c_ODN_INJ_crime = df_crime_costs["pe", "REL"],
c_ODF_INJ_crime = 0,
c_ABS_INJ_crime = df_crime_costs["pe", "ABS"],
#### QALYs ####
# HIV/HCV negative
u_BUP_NI_NEG = df_qalys["pe", "BUP"],
u_BUPC_NI_NEG = df_qalys["pe", "BUPC"],
u_MET_NI_NEG = df_qalys["pe", "MET"],
u_METC_NI_NEG = df_qalys["pe", "METC"],
u_REL_NI_NEG = df_qalys["pe", "REL"],
u_ODN_NI_NEG = df_qalys["pe", "ODN"],
u_ODF_NI_NEG = df_qalys["pe", "ODF"],
u_ABS_NI_NEG = df_qalys["pe", "ABS"],
u_BUP_INJ_NEG = df_qalys["pe", "BUP"],
u_BUPC_INJ_NEG = df_qalys["pe", "BUPC"],
u_MET_INJ_NEG = df_qalys["pe", "MET"],
u_METC_INJ_NEG = df_qalys["pe", "METC"],
u_REL_INJ_NEG = df_qalys["pe", "REL"],
u_ODN_INJ_NEG = df_qalys["pe", "ODN"],
u_ODF_INJ_NEG = df_qalys["pe", "ODF"],
u_ABS_INJ_NEG = df_qalys["pe", "ABS"],
u_HIV_mult = df_qalys["pe", "HIV_mult"], # HIV multiplier for negative states
u_HCV_mult = df_qalys["pe", "HCV_mult"], # HCV multiplier for negative states
u_COI_mult = df_qalys["pe", "COI_mult"]
) # Close list
return(l_params_all) # Return full parameter list
}
#' Update parameters
#'
#' \code{update_param_list} is used to update list of all parameters with new
#' values for specific parameters.
#'
#' @param l_params_all List with all parameters of decision model
#' @param params_updated Parameters for which values need to be updated
#' @return
#' A modifed list with all parameters updated.
#' @export
update_param_list <- function(l_params_all, params_updated){
if (typeof(params_updated)!="list"){
params_updated <- split(unname(params_updated),names(params_updated)) #convert the named vector to a list
}
l_params_all <- modifyList(l_params_all, params_updated) #update the values
return(l_params_all)
}
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.