Help for package golden

Type:

Package

Title:

Framework for Patient-Level Microsimulation of Risk Factor Trajectories & Hazard-Based Events

Version:

0.0.4

Date:

2026-08-24

Description:

Fast, flexible, patient-level microsimulation. Time-stepped simulation with a 'C++' back-end from user-supplied initial population, trajectories, hazards, and corresponding event transitions. User-defined aggregate time series histories are returned together with the final population. Designed for simulation of chronic diseases with continuous and evolving risk factors, but could easily be applied more generally.

License:

MIT + file LICENSE

Imports:

Rcpp (≥ 1.1.0), data.table

LinkingTo:

Rcpp

Suggests:

testthat (≥ 3.0.0), SciViews, knitr, rmarkdown, ggplot2

Config/testthat/edition:

Depends:

R (≥ 3.5)

LazyData:

true

RoxygenNote:

7.3.3

Encoding:

UTF-8

VignetteBuilder:

knitr

NeedsCompilation:

yes

Packaged:

2026-08-24 12:02:09 UTC; pjd

Author:

Pete Dodd

[aut, cre], Robert Chisholm

[aut], University of Sheffield [cph], Horizon Europe [fnd]

Maintainer:

Pete Dodd <p.j.dodd@sheffield.ac.uk>

Repository:

CRAN

Date/Publication:

2026-08-24 13:00:02 UTC

Golden: Framework for Patient-Level Microsimulation of Risk Factor Trajectories & Hazard-Based Events

Description

Author(s)

Maintainer: Pete Dodd p.j.dodd@sheffield.ac.uk (ORCID)

Authors:

Robert Chisholm robert.chisholm@sheffield.ac.uk (ORCID)

Other contributors:

University of Sheffield [copyright holder]
Horizon Europe [funder]

Examples

## Not run: 
# A full example can be found in the vignettes

## End(Not run)

Example BMI distribution

Description

This dataset contains example fits of body mass index (BMI) to gamma distributions for the United States in 2022. The original data were taken from an analysis of the contribution of undernutrition to tuberculosis incidence, which was published in the Lancet Global Health in 2026. The data on which these fits are based is from the NCD Risk Factor Collaboration (NCD-RisC) estimates dataset.

Usage

bmi_fits

Format

A data.frame/data.table with 28 rows and 4 variables:

sex: Biological sex ('char'): Men, Women
acat: Age category in years ('char'): 0-4, 5-9, ..., 85plus
k: Gamma distribution shape parameter ('num'): k > 0
theta: Gamma distribution scale parameter ('num'): theta > 0

Source

https://github.com/petedodd/bmitb

Validate an history column object If validation fails, an exception will be raised.

Description

Validate an history column object If validation fails, an exception will be raised.

Usage

check_column(column, initPop = NULL)

Arguments

column

An S3 object of class "golden_history_column"

initPop

(Optional) data.table to check columns required by functions exist

Value

No return value, called for side effects.

Examples

library(data.table)
dt <- data.table(a = rep(0, 100))
# Create an S3 golden_history_column
col <- new_column("sum_a", sum, c("a"))
# check_column() will not throw an exception
# as col is a valid S3 golden_history_column
# and dt contains column "a"
check_column(col, dt)

Validate an hazard object If validation fails, an exception will be raised.

Description

Validate an hazard object If validation fails, an exception will be raised.

Usage

check_hazard(hazard, initPop = NULL)

Arguments

hazard

An S3 object of class "golden_hazard"

initPop

(Optional) data.table to check columns required by functions exist

Value

No return value, called for side effects.

Examples

library(data.table)
N <- 100
dt <- data.table(a = runif(N, 0, 1), b = rep(0, N))
# Define a hazard function, which returns a vector of equal length uncertainties
test_hazard <- function(a) {
    ret <- (a < 0.5)
}
# Define a transition function, which sets all "b" columns affected by the hazard to 100
test_transition <- function() {
    return (100)
}
# Create an S3 golden_hazard
haz <- new_hazard(
              test_hazard,
              c("a"),
              new_transition(test_transition, c(), "b")
            )
# check_hazard() will not throw an exception
# as haz is a valid S3 golden_hazard
# and dt contains column "a"
check_hazard(haz, dt)

Validate an history object If validation fails, an exception will be raised.

Description

Validate an history object If validation fails, an exception will be raised.

Usage

check_history(history, initPop = NULL)

Arguments

history

An S3 object of class "golden_history"

initPop

(Optional) data.table to check columns required by functions exist

Value

No return value, called for side effects.

Examples

library(data.table)
dt <- data.table(a = rep(0, 100))
# Create an S3 golden_history, containing 1 golden_history_column
hist <- new_history(new_column("sum_a", sum, c("a")))
# check_history() will not throw an exception
# as hist is a valid S3 golden_history
# and dt contains column "a" used by the column
check_history(hist, dt)

Validate the configuration passed to run_simulation() If validation fails, an exception will be raised.

Description

Validate the configuration passed to run_simulation() If validation fails, an exception will be raised.

Usage

check_parameters(parameters, initPop = NULL)

Arguments

parameters

An golden_parameters S3 object to be validated

initPop

data.frame which contains the columns required by parameters

Value

No return value, called for side effects.

Examples

library(data.table)
N <- 100
dt <- data.table(a = runif(N, 0, 1), b = rep(0, N))
# Define a hazard function, which returns a vector of equal length uncertainties
test_hazard <- function(a) {
    ret <- (a < 0.5)
}
# Define a transition function, which sets all "b" columns affected by the hazard to 100
test_transition <- function() {
    return (100)
}
# Create an S3 golden_hazard
haz <- new_hazard(
              test_hazard,
              c("a"),
              new_transition(test_transition, c(), "b")
            )
# Define a trajectory function, which adds 2 to all members of the input vector
test_trajectory <- function(a) {
    return (a + 2)
}
# Create an S3 golden_trajectory
trj <- new_trajectory(test_trajectory, c("b"), "b")
# Create an S3 golden_history, containing 1 golden_history_column
hist <- new_history(new_column("sum_a", sum, c("a")))
# Create an S3 golden_parameters
params <- new_parameters(
  hazards = haz,
  trajectories = trj,
  steps = 10,
  debug = FALSE,
  history = hist
)
# check_parameters() will not throw an exception
# as params is a valid S3 golden_parameters
# and dt contains columns "a" and "b"
check_parameters(params, dt)

Validate an trajectory object If validation fails, an exception will be raised.

Description

Validate an trajectory object If validation fails, an exception will be raised.

Usage

check_trajectory(trajectory, initPop = NULL)

Arguments

trajectory

An S3 object of class "golden_trajectory"

initPop

(Optional) data.table to check columns required by functions exist

Value

No return value, called for side effects.

Examples

library(data.table)
dt <- data.table(b = rep(0, 100))
# Define a trajectory function, which adds 2 to all members of the input vector
test_trajectory <- function(a) {
    return (a + 2)
}
# Create an S3 golden_trajectory
trj <- new_trajectory(test_trajectory, c("b"), "b")
# check_trajectory() will not throw an exception
# as trj is a valid S3 golden_trajectory
# and dt contains column "b"
check_trajectory(trj, dt)

Validate an transition object If validation fails, an exception will be raised.

Description

Validate an transition object If validation fails, an exception will be raised.

Usage

check_transition(transition, initPop = NULL)

Arguments

transition

An S3 object of class "golden_transition"

initPop

(Optional) data.table to check columns required by functions exist

Value

No return value, called for side effects.

Examples

library(data.table)
dt <- data.table(b = rep(0, 100))
# Define a transition function, which sets all columns affected by the hazard to 100
test_transition <- function() {
    return (100)
}
# Define an S3 golden_transition
trn <- new_transition(test_transition, c(), "b")
# check_transition() will not throw an exception
# as trn is a valid S3 golden_transition
# and dt contains column "b"
check_transition(trn, dt)

Create a new cohort

Description

Temporary testing method, probably replaced in future with R's simdata package or similar

Usage

create_cohort(demog, N)

Arguments

demog

Demographic information containing columns AgeGrp/PopMale/PopFemale/PopTotal

N

Size of the population to generate

Value

A sample population data.table, with columns male/age/bmi/death

Examples

library(data.table)
demog <- data.table(
  AgeGrp = c(0, 1, 2, 3),
  PopMale = c(1000, 1100, 1050, 980),
  PopFemale = c(950, 1020, 1005, 970)
)
demog[, PopTotal := PopMale + PopFemale]
cohort <- create_cohort(demog, 100)

Example globorisk coefficients

Description

Example data on cardiovascular disease (CVD) risk from the Globorisk model, which is a model for estimating 10-year risk of fatal and non-fatal CVD events, see:

https://doi.org/10.1016/S2213-8587(17)30015-3 https://doi.org/10.1016/S2213-8587(15)00081-9

These data are available in the 'globorisk' R package, see the source link below. This subset is for the United States, and contains the coefficients for the risk prediction equations.

The exact extraction of these data from the 'globorisk' package is in the 'data-raw/dataprep.R' file in this package.

Usage

globorisk_coefs

Format

A data.frame/data.table with 1 rows and 18 variables:

type: Risk model type ('char', set to "office").
main_sbpc: Coefficient for centered and scaled systolic blood pressure ('num').
main_tcc: Coefficient for centered and scaled total cholesterol ('num').
main__Idm_1: Variable not used in this model, but included as 'NA' for completeness ('num').
main_smok: Variable not used in this model, but included as 'NA' for completeness ('num').
main_sexdm: Variable not used in this model, but included as 'NA' for completeness ('num').
main_sexsmok: Variable not used in this model, but included as 'NA' for completeness ('num').
tvc_sbpc: Coefficient for centered and scaled systolic blood pressure by age interaction term ('num').
tvc_tcc: Variable not used in this model, but included as 'NA' for completeness ('num').
tvc_dm: Variable not used in this model, but included as 'NA' for completeness ('num').
tvc_smok: Variable not used in this model, but included as 'NA' for completeness ('num').
main_bmi5c: Coefficient for centered and scaled body mass index (BMI) ('num').
main_smokc: Coefficient for centered and scaled smoking status ('num').
main_sexsmokc: Coefficient for centered and scaled smoking by sex interaction term ('num').
tvc_smokc: Coefficient for centered and scaled smoking status by age interaction term ('num').
tvc_bmi5c: Coefficient for centered and scaled BMI by age interaction term ('num').
main_sbpsexc: Variable not used in this model, but included as 'NA' for completeness ('num').
lac: Globorisk use Local Area Countries flag ('num', set to 0).

Source

https://github.com/boyercb/globorisk

Example globorisk baseline hazard

Description

Example data on cardiovascular disease (CVD) risk from the Globorisk model, which is a model for estimating 10-year risk of fatal and non-fatal CVD events, see:

https://doi.org/10.1016/S2213-8587(17)30015-3 https://doi.org/10.1016/S2213-8587(15)00081-9

These data are available in the 'globorisk' R package, see the source link below. This subset is for the United States in 2000, and contains the baseline hazard.

The exact extraction of these data from the 'globorisk' package is in the 'data-raw/dataprep.R' file in this package.

Usage

globorisk_cvdr

Format

A data.frame/data.table with 20 rows and 4 variables:

agec: Age category ('int'): 1,...,10.
sex: Sex ('int'): 0, 1.
cvd_0: Baseline hazard per year for CVD events ('num').
agesex: Age and sex concatenated category ('char'): "1_0", "1_1", ..., "10_1".

Source

https://github.com/boyercb/globorisk

Example globorisk reference values

Description

Example data on cardiovascular disease (CVD) risk from the Globorisk model, which is a model for estimating 10-year risk of fatal and non-fatal CVD events, see:

https://doi.org/10.1016/S2213-8587(17)30015-3 https://doi.org/10.1016/S2213-8587(15)00081-9

These data are available in the 'globorisk' R package, see the source link below. This subset is for the United States, and contains the reference values for centering.

The exact extraction of these data from the 'globorisk' package is in the 'data-raw/dataprep.R' file in this package.

Usage

globorisk_rf

Format

A data.frame/data.table with 18 rows and 9 variables:

iso: ISO country code ('char'): "USA".
agec: Age category ('int'): 1,...,10.
sex: Sex ('int'): 0, 1.
mean_sbp: Systolic blood pressure mean value for centering ('num').
mean_tc: Total cholesterol mean value for centering ('num').
mean_dm: Diabetes mean value for centering ('num').
mean_smk: Smoking status mean value for centering ('num').
mean_bmi: Body mass index mean value for centering ('num').
agesex: Age and sex concatenated category ('char'): "1_0", "1_1", ..., "9_1".

Source

https://github.com/boyercb/globorisk

Example life table data

Description

Example life table data for the United States from 2000 to 2100, with age-specific mortality rates for each sex and in total. The original data were taken from the World Population Prospects 2024 revision, which is published by the United Nations Department of Economic and Social Affairs, Population Division.

https://population.un.org/wpp/

The data on which these life tables are based is from the WPP 2024 revision, as made available through the 'wpp2024' R package available from the source below. The exact extraction of these data from the WPP 2024 revision is in the 'data-raw/dataprep.R' file in this package.

Usage

lifetable_data

Format

A data.frame/data.table with 10,201 rows and 5 variables:

year: The year of the life table data ('integer'), ranging from 2000 to 2100.
age: The age group for the life table data ('integer'), ranging from 0 to 100.
mxM: Actuarial mortality for men; age-specific hazard of death ('num').
mxF: Actuarial mortality for women; age-specific hazard of death ('num').
mxB: Actuarial mortality for both sexes combined; age-specific hazard of death ('num').

Source

https://github.com/PPgp/wpp2024

Create a new golden_history_column

Description

Create a new golden_history_column

Usage

new_column(name, fn, args, filter_fn = NULL, filter_args = NULL)

Arguments

name

Name of the column in the output data-table

fn

Reduction function, which converts the input columns to a single value

args

Names of columns and special variables to be passed to fn

filter_fn

(Optional) Filter function, which returns a bool vector denoting which rows should be reduced

filter_args

(Optional) Names of columns and special variables to be passed to filter_fn. Required if filter_fn is

Value

An object of class "golden_history_column"

Examples

# Create an S3 golden_history_column named "sum_a", using sum(). with column "a"
col <- new_column("sum_a", sum, c("a"))

Create a new hazard object

Description

Create a new hazard object

Usage

new_hazard(
  fn,
  args,
  transitions,
  freq = 1,
  first = 1,
  last = 2147483647,
  name = NULL
)

Arguments

fn

Function which calculates the hazard likelihood

args

Character vector of parameter names expected by fn

transitions

Transition object(s) to be applied where the hazard is successful

freq

(Optional) The frequency of hazard execution, hazards always execute on first step

first

(Optional) First step the hazard should be enabled (initial step is index 1)

last

(Optional) Last step the hazard should be enabled (initial step is index 1)

name

(Optional) Name used in error messages and similar. Defaults to an automatic name

Value

An object of class "golden_hazard"

Examples

# Define a hazard function, which returns a vector of equal length uncertainties
test_hazard <- function(a) {
    ret <- (a < 0.5)
}
# Define a transition function, which sets all "b" columns affected by the hazard to 100
test_transition <- function() {
    return (100)
}
# Create an S3 golden_hazard
haz <- new_hazard(
              test_hazard,
              c("a"),
              new_transition(test_transition, c(), "b")
            )

Create a new golden_history

Description

Create a new golden_history

Usage

new_history(columns, frequency = 1)

Arguments

columns

golden_history_column S3 object(s)

frequency

The number of simulation steps per history collection.

Value

An object of class "golden_history"

Examples

# Create an S3 golden_history, containing 1 golden_history_column
hist <- new_history(new_column("sum_a", sum, c("a")))

Create a new golden_parameters

Description

Create a new golden_parameters

Usage

new_parameters(
  hazards = list(),
  trajectories = list(),
  steps,
  random_seed = 0,
  debug = TRUE,
  print_timing = TRUE,
  history = NULL
)

Arguments

hazards

golden_hazard S3 object(s)

trajectories

golden_trajectory S3 object(s)

steps

Number of steps to run

random_seed

Seed to be used for random generation. If set 0, current R random state will be used.

debug

(TRUE/FALSE) flag indicating whether validation checks are enabled. These catch NaN, but reduce performance

print_timing

(TRUE/FALSE) flag indicating whether a per-function timing report should be printed after the simulation, this will always be suppressed for fast (<= 1 second) simulations.

history

golden_history S3 object representing the columns of data to be aggregated during simulation

Value

An object of class "golden_parameters"

Examples

# Define a hazard function, which returns a vector of equal length uncertainties
test_hazard <- function(a) {
    ret <- (a < 0.5)
}
# Define a transition function, which sets all "b" columns affected by the hazard to 100
test_transition <- function() {
    return (100)
}
# Create an S3 golden_hazard
haz <- new_hazard(
              test_hazard,
              c("a"),
              new_transition(test_transition, c(), "b")
            )
# Define a trajectory function, which adds 2 to all members of the input vector
test_trajectory <- function(a) {
    return (a + 2)
}
# Create an S3 golden_trajectory
trj <- new_trajectory(test_trajectory, c("b"), "b")
# Create an S3 golden_history, containing 1 golden_history_column
hist <- new_history(new_column("sum_a", sum, c("a")))
# Create an S3 golden_parameters
params <- new_parameters(
  hazards = haz,
  trajectories = trj,
  steps = 10,
  debug = FALSE,
  history = hist
)

Create a new trajectory object

Description

Create a new trajectory object

Usage

new_trajectory(fn, args, states, name = NULL)

Arguments

fn

Function defining the trajectory function

args

Character vector of parameter names expected by fn

states

Name(s) of the column(s) where the result(s) of the trajectory function is to be stored

name

(Optional) Name used in error messages and similar. Defaults to an automatic name

Value

An object of class "golden_trajectory"

Note

If a list if passed to states, fn must return a list of equal length

Examples

# Define a trajectory function, which adds 2 to all members of the input vector
test_trajectory <- function(a) {
    return (a + 2)
}
# Create an S3 golden_trajectory
trj <- new_trajectory(test_trajectory, c("b"), "b")

Create a new transition object

Description

Create a new transition object

Usage

new_transition(fn, args, states, name = NULL)

Arguments

fn

Function defining the transition functions

args

Character vector of parameter names expected by fn

states

Name(s) of the column(s) where the result of the transition function is to be stored

name

(Optional) Name used in error messages and similar. Defaults to an automatic name

Value

An object of class "golden_transition"

Examples

# Define a transition function, which sets all columns affected by the hazard to 100
test_transition <- function() {
    return (100)
}
# Define an S3 golden_transition
trn <- new_transition(test_transition, c(), "b")

Example population structure

Description

Example population data for the United States for 2000, with age-specific populations in thousands. The original data were taken from the World Population Prospects 2024 revision, which is published by the United Nations Department of Economic and Social Affairs, Population Division.

https://population.un.org/wpp/

The data on which these population data are based is from the WPP 2024 revision, as made available through the 'wpp2024' R package available from the source below. The exact extraction of these data from the WPP 2024 revision is in the 'data-raw/dataprep.R' file in this package.

Usage

pop_snapshot

Format

A matrix with 101 rows and 2 columns:

popM: Named first matrix colum: the male population in thousands for each age group, with rows for ages 0 to 100 years.
popF: Named second matrix column: the female population in thousands for each age group, with rows for ages 0 to 100 years.

Source

https://github.com/PPgp/wpp2024

Print the contents of a golden_hazard type S3 object

Description

Print the contents of a golden_hazard type S3 object

Usage

## S3 method for class 'golden_hazard'
print(x, ..., indent = 0L)

Arguments

x

The object to be printed

...

Not used. Included for S3 method compatibility.

indent

(Optional) The level the printing is indented, useful if nested within another S3 object

Value

No return value, called for side effects.

Examples

# Define a hazard function, which returns a vector of equal length uncertainties
test_hazard <- function(a) {
    ret <- (a < 0.5)
}
# Define a transition function, which sets all "b" columns affected by the hazard to 100
test_transition <- function() {
    return (100)
}
# Create an S3 golden_hazard
haz <- new_hazard(
              test_hazard,
              c("a"),
              new_transition(test_transition, c(), "b")
            )
print(haz)

Print the contents of a golden_history type S3 object

Description

Print the contents of a golden_history type S3 object

Usage

## S3 method for class 'golden_history'
print(x, ..., indent = 0L)

Arguments

x

The object to be printed

...

Not used. Included for S3 method compatibility.

indent

(Optional) The level the printing is indented, useful if nested within another S3 object

Value

No return value, called for side effects.

Examples

# Create an S3 golden_history
hist <- new_history(new_column("sum_a", sum, c("a")))
print(hist)

Print the contents of a golden_history_column type S3 object

Description

Print the contents of a golden_history_column type S3 object

Usage

## S3 method for class 'golden_history_column'
print(x, ..., indent = 0L)

Arguments

x

The object to be printed

...

Not used. Included for S3 method compatibility.

indent

(Optional) The level the printing is indented, useful if nested within another S3 object

Value

No return value, called for side effects.

Examples

# Create an S3 golden_history_column
col <- new_column("sum_a", sum, c("a"))
print(col)

Print the contents of a golden_parameters type S3 object

Description

Print the contents of a golden_parameters type S3 object

Usage

## S3 method for class 'golden_parameters'
print(x, ..., indent = 0)

Arguments

x

The object to be printed

...

Not used. Included for S3 method compatibility.

indent

(Optional) The level the printing is indented, useful if nested within another S3 object

Value

No return value, called for side effects.

Examples

# Define a hazard function, which returns a vector of equal length uncertainties
test_hazard <- function(a) {
    ret <- (a < 0.5)
}
# Define a transition function, which sets all "b" columns affected by the hazard to 100
test_transition <- function() {
    return (100)
}
# Create an S3 golden_hazard
haz <- new_hazard(
              test_hazard,
              c("a"),
              new_transition(test_transition, c(), "b")
            )
# Define a trajectory function, which adds 2 to all members of the input vector
test_trajectory <- function(a) {
    return (a + 2)
}
# Create an S3 golden_trajectory
trj <- new_trajectory(test_trajectory, c("b"), "b")
# Create an S3 golden_history, containing 1 golden_history_column
hist <- new_history(new_column("sum_a", sum, c("a")))
# Create an S3 golden_parameters
params <- new_parameters(
  hazards = haz,
  trajectories = trj,
  steps = 10,
  debug = FALSE,
  history = hist
)
print(params)

Print the contents of a golden_timing type S3 object

Description

Print the contents of a golden_timing type S3 object

Usage

## S3 method for class 'golden_timing'
print(x, ...)

Arguments

x

The object to be printed

...

Not used. Included for S3 method compatibility.

Value

No return value, called for side effects.

Examples

library(data.table)
N <- 100
dt <- data.table(a = runif(N, 0, 1), b = rep(0, N))
# Define a hazard function, which returns a vector of equal length uncertainties
test_hazard <- function(a) {
    ret <- (a < 0.5)
}
# Define a transition function, which sets all "b" columns affected by the hazard to 100
test_transition <- function() {
    return (100)
}
# Create an S3 golden_hazard
haz <- new_hazard(
              test_hazard,
              c("a"),
              new_transition(test_transition, c(), "b")
            )
# Define a trajectory function, which adds 2 to all members of the input vector
test_trajectory <- function(a) {
    return (a + 2)
}
# Define an S3 golden_trajectory
trj <- new_trajectory(test_trajectory, c("b"), "b")
# Create an S3 golden_history, containing 1 golden_history_column
hist <- new_history(new_column("sum_a", sum, c("a")))
# Define an S3 golden_parameters
params <- new_parameters(
  hazards = haz,
  trajectories = trj,
  steps = 10,
  debug = FALSE,
  history = hist
)
# Run the simulation to collect results
results <- run_simulation(dt, params)
print(results$timing)

Print the contents of a golden_trajectory type S3 object

Description

Print the contents of a golden_trajectory type S3 object

Usage

## S3 method for class 'golden_trajectory'
print(x, ..., indent = 0L)

Arguments

x

The object to be printed

...

Not used. Included for S3 method compatibility.

indent

(Optional) The level the printing is indented, useful if nested within another S3 object

Value

No return value, called for side effects.

Examples

# Define a trajectory function, which adds 2 to all members of the input vector
test_trajectory <- function(a) {
    return (a + 2)
}
# Create an S3 golden_trajectory
trj <- new_trajectory(test_trajectory, c("b"), "b")
print(trj)

Print the contents of a golden_transition type S3 object

Description

Print the contents of a golden_transition type S3 object

Usage

## S3 method for class 'golden_transition'
print(x, ..., indent = 0L)

Arguments

x

The object to be printed

...

Not used. Included for S3 method compatibility.

indent

(Optional) The level the printing is indented, useful if nested within another S3 object

Value

No return value, called for side effects.

Examples

# Define a transition function, which sets all columns affected by the hazard to 100
test_transition <- function() {
    return (100)
}
# Define an S3 golden_transition
trn <- new_transition(test_transition, c(), "b")
print(trn)

Execute a patient trajectory simulation

Description

Execute a patient trajectory simulation

Usage

run_simulation(initPop, parameters)

Arguments

initPop

data.table containing initial population for simulation

parameters

Simulation configuration

Value

An list containing final population, history and timing data.tables

Examples

library(data.table)
N <- 100
dt <- data.table(a = runif(N, 0, 1), b = rep(0, N))
# Define a hazard function, which returns a vector of equal length uncertainties
test_hazard <- function(a) {
    ret <- (a < 0.5)
}
# Define a transition function, which sets all "b" columns affected by the hazard to 100
test_transition <- function() {
    return (100)
}
# Create an S3 golden_hazard
haz <- new_hazard(
              test_hazard,
              c("a"),
              new_transition(test_transition, c(), "b")
            )
# Define a trajectory function, which adds 2 to all members of the input vector
test_trajectory <- function(a) {
    return (a + 2)
}
# Define an S3 golden_trajectory
trj <- new_trajectory(test_trajectory, c("b"), "b")
# Create an S3 golden_history, containing 1 golden_history_column
hist <- new_history(new_column("sum_a", sum, c("a")))
# Define an S3 golden_parameters
params <- new_parameters(
  hazards = haz,
  trajectories = trj,
  steps = 10,
  debug = FALSE,
  history = hist
)
# Run the simulation to collect results
results <- run_simulation(dt, params)

Package {golden}

Golden: Framework for Patient-Level Microsimulation of Risk Factor Trajectories & Hazard-Based Events

Description

Author(s)

Examples

Example BMI distribution

Description

Usage

Format

Source

Validate an history column object If validation fails, an exception will be raised.

Description

Usage

Arguments

Value

Examples

Validate an hazard object If validation fails, an exception will be raised.

Description

Usage

Arguments

Value

Examples

Validate an history object If validation fails, an exception will be raised.

Description

Usage

Arguments

Value

Examples

Validate the configuration passed to run_simulation() If validation fails, an exception will be raised.

Description

Usage

Arguments

Value

Examples

Validate an trajectory object If validation fails, an exception will be raised.

Description

Usage

Arguments

Value

Examples

Validate an transition object If validation fails, an exception will be raised.

Description

Usage

Arguments

Value

Examples

Create a new cohort

Description

Usage

Arguments

Value

Examples

Example globorisk coefficients

Description

Usage

Format

Source

Example globorisk baseline hazard

Description

Usage

Format

Source

Example globorisk reference values

Description

Usage

Format

Source

Example life table data

Description

Usage

Format

Source

Create a new golden_history_column

Description

Usage

Arguments

Value

Examples

Create a new hazard object

Description