# R/FrenchDementia.R In floguillet/MCSPCD: Monte Carlo Simulation for Projections of epidemiological burden of Chronic Disease

```#' Computation of Health Indicators for dementia in France
#'
#' This function computes many health indicators under several scenarios of intervention in risk factor distribution for a given year with all french data.
#'
#' @param t year of the projections for health indicators.
#' @param intervention 0 = no change; 1 = reduction by two of risk factor distribution; 2 = risk factor distribution considered as null. Default is \code{0}.
#' @param year_intervention year of the intervention in risk factor distribution takes place. Default is \code{NULL}.
#' @param nb_people number of people whose trajectory will be simulated for each generation. Default is \code{100}.
#' @param nb_iter number of iterations for the algorithm. Default is \code{0}.
#' @param gender gender for computation. \code{"W"} for women and \code{"M"} for men. Default is \code{"W"}.
#' @param a01_cst \code{TRUE} without reduction for a01 during time. \code{FALSE} with a reduction for a01 during time. Default is \code{TRUE}.
#' @param theta02_cst \code{TRUE} without increase for theta02. \code{FALSE} with an increase for theta02. Default is \code{FALSE}.
#' @param Ncpus The number of processors available. Default is \code{"1"}.
#'
#' @return a list containing the health indicators
#'
#' @export
#'
#' @examples
#' FrenchDementia(t = 2040,
#' intervention = 1,
#' year_intervention = 2020,
#' nb_people = 10000,
#' nb_iter = 100,
#' gender = "W",
#' a01_cst = TRUE,
#' theta02_cst = FALSE,
#' Ncpus = 1)
FrenchDementia <- function (t,
intervention = 0,
year_intervention = NULL,
nb_people = 100,
nb_iter = 0,
gender = "W",
a01_cst = TRUE,
theta02_cst = FALSE,
Ncpus = 1)

{
t_FR <- t;
intervention_FR <- intervention;
year_intervention_FR <- year_intervention;
nb_people_FR <- nb_people;
nb_iter_FR <- nb_iter;
gender_FR <- gender;
Ncpus_FR <- Ncpus;

if (a01_cst == TRUE) {
data_for_a01_values <- a01_constant_values
data_for_a01 <- a01_constant
} else {
if (a01_cst == FALSE) {
data_for_a01_values <- a01_reduction_values
data_for_a01 <- a01_reduction
}
}

if (theta02_cst == TRUE) {
data_for_theta02_values <- theta02_1_values
data_for_theta02 <- theta02_1
} else {
if (theta02_cst == FALSE) {
data_for_theta02_values <- theta02_increase_values
data_for_theta02 <- theta02_increase
}
}

FR_indicators <- estimHI(t = t_FR,
intervention = intervention_FR,
year_intervention = year_intervention_FR,
nb_people = nb_people_FR,
nb_iter = nb_iter_FR,
data_pop = pop,
gender = gender_FR,
data_a01_values = data_for_a01_values,
data_a02_values = a02_constant_values,
data_theta01_values = theta01_cas_1_6_values,
data_theta02_values = data_for_theta02_values,
data_theta12_values = data_for_theta02_values,
data_prev_values = prevconso_values,
data_incid_values = incidconso_values,
data_rr_DvsND_values = rr_DvsND_values,
data_a01 = data_for_a01,
data_a02 = a02_constant,
data_theta01 = theta01_cas_1_6_sans_var,
data_theta02 = data_for_theta02,
data_theta12 = data_for_theta02,
data_prev = prevconso,
data_incid = incidconso,
data_rr_DvsND = rr_DvsND,
Ncpus = Ncpus_FR);

return(FR_indicators)
}
```
floguillet/MCSPCD documentation built on Dec. 16, 2019, 2:08 a.m.