Package 'gems' reference manual

Package 'gems'

Title:	Generalized Multistate Simulation Model
Description:	Simulate and analyze multistate models with general hazard functions. gems provides functionality for the preparation of hazard functions and parameters, simulation from a general multistate model and predicting future events. The multistate model is not required to be a Markov model and may take the history of previous events into account. In the basic version, it allows to simulate from transition-specific hazard function, whose parameters are multivariable normally distributed.
Authors:	Luisa Salazar Vizcaya, Nello Blaser, Thomas Gsponer
Maintainer:	Luisa Salazar Vizcaya <[email protected]>
License:	GPL-2
Version:	1.1.1
Built:	2025-02-26 03:29:45 UTC
Source:	https://github.com/cran/gems

Title:

Generalized Multistate Simulation Model

Description:

Simulate and analyze multistate models with general hazard functions. gems provides functionality for the preparation of hazard functions and parameters, simulation from a general multistate model and predicting future events. The multistate model is not required to be a Markov model and may take the history of previous events into account. In the basic version, it allows to simulate from transition-specific hazard function, whose parameters are multivariable normally distributed.

Authors:

Luisa Salazar Vizcaya, Nello Blaser, Thomas Gsponer

Maintainer:

Luisa Salazar Vizcaya <[email protected]>

License:

GPL-2

Version:

1.1.1

Built:

2025-02-26 03:29:45 UTC

Source:

https://github.com/cran/gems

Help Index

Class `"ArtCohort"`

Description

Is a S4 class for the artificial cohort generated by simulateCohort.

Usage

## S4 method for signature 'ArtCohort,ANY,ANY,ANY'
x[i, j, ..., drop = TRUE]

## S4 method for signature 'ArtCohort'
update(object, newsize, addbaseline = matrix(NA, nrow =
  newsize - object@size), newInitialStates = rep(1, newsize - object@size))

## S4 method for signature 'ArtCohort'
head(x, ...)

## S4 method for signature 'ArtCohort'
tail(x, ...)

## S4 method for signature 'ArtCohort'
summary(object)
## S4 method for signature 'ArtCohort,ANY,ANY,ANY'
x[i, j, ..., drop = TRUE]

## S4 method for signature 'ArtCohort'
update(object, newsize, addbaseline = matrix(NA, nrow =
  newsize - object@size), newInitialStates = rep(1, newsize - object@size))

## S4 method for signature 'ArtCohort'
head(x, ...)

## S4 method for signature 'ArtCohort'
tail(x, ...)

## S4 method for signature 'ArtCohort'
summary(object)

Arguments

`x`, `object`	an ArtCohort
`i`, `j`, `drop`	same as for `data.frame`
`...`	passed on to `data.frame` method
`newsize`	size of the updated cohort
`addbaseline`	baseline for new part of cohort
`newInitialStates`	initial states for new part of cohort

Slots

states.number: Object of class "numeric": number of states
size: Object of class "numeric": cohort size
baseline: Object of class "matrix": baseline matrix
follow.up: Object of class "numeric": maximum follow-up time
parameters: Object of class "transition.structure": input parameters
parametersCovariances: Object of class "transition.structure": input covariance matrices
timeToTransition: Object of class "matrix": input timeToTransition matrix. logical components
transitionFunctions: Object of class "transition.structure": input hazard functions
time.to.state: Object of class "data.frame": entry times for each patient into each of the states

Objects from the Class

Objects are created by calls to the function simulateCohort.

Author(s)

Luisa Salazar Vizcaya, Nello Blaser, Thomas Gsponer

Examples


showClass("ArtCohort")

showClass("ArtCohort")

transition probabilities

Description

Calculates the cumulative incidence and prediction intervals after first state

Usage

cumulativeIncidence(object, times, M = 100, stateNames = paste("State",
  as.list(1:dim(cohorts)[1])))
cumulativeIncidence(object, times, M = 100, stateNames = paste("State",
  as.list(1:dim(cohorts)[1])))

Arguments

`object`	either the output of `simulateCohort` or the `matrix` with the `probabilities` slot of that output.
`times`	a time vector.
`M`	number of groups for calculating confidence intervals.
`stateNames`	a list with the names of states.

Value

an object of class "PosteriorProbabilities", containing the statenames, timepoints and the cumulative incidence with pointwise prediction intervals over time.

Author(s)

Luisa Salazar Vizcaya, Nello Blaser, Thomas Gsponer

References

Nello Blaser, Luisa Salazar Vizcaya, Janne Estill, Cindy Zahnd, Bindu Kalesan, Matthias Egger, Olivia Keiser, Thomas Gsponer (2015). gems: An R Package for Simulating from Disease Progression Models. Journal of Statistical Software, 64(10), 1-22. URL http://www.jstatsoft.org/v64/i10/.

gems: Generalized Multistate Simulation Model

Description

References

generate template for transition functions

Description

This function simplifies generating the matrix of transition functions.

Usage

generateHazardMatrix(statesNumber)
generateHazardMatrix(statesNumber)

Arguments

statesNumber

the number of states to be considered.

Value

a transition.structure of dimension $N \times N$ , where $N$ is the number of states and with value "impossible" for all potential transitions.

Author(s)

Luisa Salazar Vizcaya, Nello Blaser, Thomas Gsponer

References

generate a template for parameter covariances

Description

This function simplifies generating the matrix of parameter covariances from a matrix of mean parameters.

Usage

generateParameterCovarianceMatrix(mu)
generateParameterCovarianceMatrix(mu)

Arguments

`mu`	a `transition.structure` of dimension $N \times N$ , whose components `list` the mean values for the parameters in the `transitionFunction`.

Value

a transition.structure of dimension $N \times N$ of covariance matrices for the parameters.

Author(s)

Luisa Salazar Vizcaya, Nello Blaser, Thomas Gsponer

References

generate a template for mean parameters

Description

This function simplifies generating the matrix of mean parameters from a matrix of transition functions.

Usage

generateParameterMatrix(hf)
generateParameterMatrix(hf)

Arguments

`hf`	a `transition.structure` of dimension $N \times N$ , where $N$ is the number of states.

Value

a transition.structure of dimension $N \times N$ , whose components are lists of the right length for the parameters in the corresponding hazard function hf.

Author(s)

Luisa Salazar Vizcaya, Nello Blaser, Thomas Gsponer

References

Class `"PosteriorProbabilities"`

Description

This S4 class summarizes the posterior probabilities over time for objects of class "ArtCohort"

Usage

## S4 method for signature 'PosteriorProbabilities,ANY,ANY,ANY'
x[i, j, ..., drop = TRUE]

## S4 method for signature 'PosteriorProbabilities'
plot(x, ci = FALSE, main = paste(x@type,
  "after starting in State", x@states[1], "at time 0"),
  states = 1:dim(x@probabilities)[2], lwd = c(2, 2), col = c("blue",
  "green3"), lty = c(1, 2), xlab = "Time", ylab = "Probability", ...)

## S4 method for signature 'PosteriorProbabilities'
head(x, ...)

## S4 method for signature 'PosteriorProbabilities'
tail(x, ...)
## S4 method for signature 'PosteriorProbabilities,ANY,ANY,ANY'
x[i, j, ..., drop = TRUE]

## S4 method for signature 'PosteriorProbabilities'
plot(x, ci = FALSE, main = paste(x@type,
  "after starting in State", x@states[1], "at time 0"),
  states = 1:dim(x@probabilities)[2], lwd = c(2, 2), col = c("blue",
  "green3"), lty = c(1, 2), xlab = "Time", ylab = "Probability", ...)

## S4 method for signature 'PosteriorProbabilities'
head(x, ...)

## S4 method for signature 'PosteriorProbabilities'
tail(x, ...)

Arguments

`x`	the PosteriorProbabilities object
`i`, `j`, `drop`	same as for a `"data.frame"`
`...`	arguments passed on to main method
`ci`	should confidence intervals be displayed
`main`, `xlab`, `ylab`	same as any plot
`states`	which states to display
`lwd`, `col`, `lty`	vectors of lentht 2, with first component for the point estimate and second component for the confidence interval

Slots

states: Object of class "character": names of states
times: Object of class "numeric": time points at which probabilities are evaluated
probabilities: Object of class "matrix": posterior Probabilities to be in each state at each time
lower: Object of class "matrix": lower prediction bound to be in each state at each time
upper: Object of class "matrix": upper prediction bound to be in each state at each time
type: Object of class "character": describes type of probability

Objects from the Class

Objects are created by calls to the function simulateCohort.

Author(s)

Luisa Salazar Vizcaya, Nello Blaser, Thomas Gsponer

Examples


showClass("PosteriorProbabilities")

showClass("PosteriorProbabilities")

Simulate cohort

Description

Simulates a cohort of patients from a set of functions associated to each possible transition in a multistate model. The multistate model is not required to be a Markov model and may take the history of previous events into account. In the basic version, it allows to simulate from transition-specific hazard function, whose parameters are multivariable normally distributed. For each state, all transition-specific hazard functions and their parameters need to be specified. For simulating one transition, all possible event times are simulated and the minimum is chosen. Then simulation continues from the corresponding state until an absorbing state of time to is reached.

Usage

simulateCohort(transitionFunctions, parameters, cohortSize = 1000,
  parameterCovariances = FALSE, timeToTransition = array(FALSE, dim =
  dim([email protected])), baseline = matrix(NA, nrow =
  cohortSize), initialState = rep(1, cohortSize),
  absorbing = [email protected], to = 100,
  report.every = 100, sampler.steps = 1000)
simulateCohort(transitionFunctions, parameters, cohortSize = 1000,
  parameterCovariances = FALSE, timeToTransition = array(FALSE, dim =
  dim(transitionFunctions@list.matrix)), baseline = matrix(NA, nrow =
  cohortSize), initialState = rep(1, cohortSize),
  absorbing = transitionFunctions@states.number, to = 100,
  report.every = 100, sampler.steps = 1000)

Arguments

`transitionFunctions`	a `transition.structure` of dimension $N \times N$ that contains the hazard functions
`parameters`	a `transition.structure` of dimension $N \times N$ that contains the parameters
`cohortSize`	a `numeric` indicating the number of patients to be simulated.
`parameterCovariances`	a `transition.structure` of dimension $N \times N$ of covariance matrices for the `parameters`.
`timeToTransition`	a `logical` `matrix`; TRUE for all transitions whose `transitionFunction` is specified as the time until transition instead of as a hazard function or as a `character`.
`baseline`	a `matrix` or `data.frame` of dimension $cohortSize \times M$ with $M$ baseline characteristics of subjects to be simulated.
`initialState`	a `numeric` of length `cohortSize` with the initial state for each subject simulated.
`absorbing`	a `numeric` containing all absorbing states.
`to`	final time of the simulation.
`report.every`	a `numeric` to check progress of simulation.
`sampler.steps`	a `numeric` indicating number of steps for discretization of hazard functions

Details

The transitionFunctions contains hazard functions or time to event function associated to each possible transition. The elements of this list can be either expressed as an explicit R function or as a character ("impossible", "Weibull", "multWeibull", "exponential") in order to express impossible transitions or parametric forms for the distributions of time to event. If the functions should depend on time, baseline characteristics or be history-dependent, the function arguments t, bl or history can be used. Time t refers to the time since entry into the current state. For the time since the initial state, use t+sum(history).

The components of the parameters argument list the mean values for the parameters in the transitionFunction. If the corresponding transitionFunction is a function, the parameters should appear in the same order as in the function, leaving out t, bl and history. If the corresponding transitionFunction is the character "Weibull", the first argument is the shape and the second one the scale. If the corresponding transitionFunction is the character "multWeibull", specify weights, shapes, scales in this order.

Note that when using the parameterCovariances argument it is the users responsibility to ensure that the functions are parametrized such that parameters for each transition are multivariate normally distributed and mutually independent.

Value

an object of class "ArtCohort" with time.to.state slot of dimension $cohortSize \times N$ with entry times for each patient into each of the states.

Author(s)

Luisa Salazar Vizcaya, Nello Blaser, Thomas Gsponer

References

Examples


# Here is an example model with 3 states and 2 possible transitions. 
 
# number of states in the model
statesNumber <- 3 

# cohort size
cohortSize <- 100

# specification of hazard functions
hazardf <- generateHazardMatrix(statesNumber)
hazardf[[1,2]] <- function(t, r1, r2) 
{
  ifelse(t<=2, r1 , r2)
}
hazardf[[2,3]] <- "Weibull" 

# list of parameters for the hazard functions
mu <- generateParameterMatrix(hazardf) 
mu[[1,2]] <- list(0.33,  0.03) # r1, r2 
mu[[2,3]] <- list(1,0.84) # shape, scale

# time
maxTime <- 10

# simulate the cohort
cohort <- simulateCohort(
  transitionFunctions = hazardf,
  parameters = mu,
  cohortSize = cohortSize,
  to=maxTime)

# output
head(cohort)

# transition probability
tr <- transitionProbabilities(cohort, times=seq(0,4,.1))
plot(tr, ci=FALSE)

# cumulative incidence
inc <- cumulativeIncidence(cohort, times=seq(0,4,.1))
plot(inc, ci=FALSE, states=c(2,3))

# Here is an example model with 3 states and 2 possible transitions. 
 
# number of states in the model
statesNumber <- 3 

# cohort size
cohortSize <- 100

# specification of hazard functions
hazardf <- generateHazardMatrix(statesNumber)
hazardf[[1,2]] <- function(t, r1, r2) 
{
  ifelse(t<=2, r1 , r2)
}
hazardf[[2,3]] <- "Weibull" 

# list of parameters for the hazard functions
mu <- generateParameterMatrix(hazardf) 
mu[[1,2]] <- list(0.33,  0.03) # r1, r2 
mu[[2,3]] <- list(1,0.84) # shape, scale

# time
maxTime <- 10

# simulate the cohort
cohort <- simulateCohort(
  transitionFunctions = hazardf,
  parameters = mu,
  cohortSize = cohortSize,
  to=maxTime)

# output
head(cohort)

# transition probability
tr <- transitionProbabilities(cohort, times=seq(0,4,.1))
plot(tr, ci=FALSE)

# cumulative incidence
inc <- cumulativeIncidence(cohort, times=seq(0,4,.1))
plot(inc, ci=FALSE, states=c(2,3))

tavi data set

Description

The simulated data set for each patient contains data for kidney injuries, bleeding complications and the combined endpoint of stroke or death. The data was simulated from the original data following the steps described in the package vignette.

Format

A data frame with 194 observations on the following 7 variables.

id: a character vector that contains the patient id's
kidney: a numeric vector; indicator variable that show if an event has occurred
kidney.dur: a numeric vector; times at which the events occurred or the patients were censored
bleeding: a numeric vector; indicator variable that show if an event has occurred
bleeding.dur: a numeric vector; times at which the events occurred or the patients were censored
death: a numeric vector; indicator variable that show if an event has occurred
death.dur: a numeric vector; times at which the events occurred or the patients were censored

References

Examples


head(data(tavi))

head(data(tavi))

Class `"transition.structure"`

Description

This S4 class provides a structure to specify different characteristics of transitions, such as transition functions functions, parameters or parameter covariances.

Usage

## S4 method for signature 'transition.structure'
x[[i, j, ..., exact = TRUE]]

## S4 replacement method for signature 'transition.structure'
x[[i, j]] <- value

possibleTransitions(object)

## S4 method for signature 'transition.structure'
possibleTransitions(object)

## S4 method for signature 'transition.structure'
print(x)
## S4 method for signature 'transition.structure'
x[[i, j, ..., exact = TRUE]]

## S4 replacement method for signature 'transition.structure'
x[[i, j]] <- value

possibleTransitions(object)

## S4 method for signature 'transition.structure'
possibleTransitions(object)

## S4 method for signature 'transition.structure'
print(x)

Arguments

`x`, `object`	the `transition.structure`
`i`, `j`	same as for `matrix`
`exact`, `value`, `...`	passed on to `list` method

Slots

states.number: Object of class "numeric": number of states
list.matrix: Object of class "matrix": a list with two dimensions, where list element [i,j] correspond to transitions from i to j

Objects from the Class

Objects are created by calls to the functions generateHazardMatrix, generateParameterMatrix, generateParameterCovarianceMatrix.

Author(s)

Luisa Salazar Vizcaya, Nello Blaser, Thomas Gsponer

Examples


  showClass("transition.structure")

showClass("transition.structure")

transition probabilities

Description

Calculates the probabilities and prediction intervals after first state

Usage

transitionProbabilities(object, times, M = 100, stateNames = paste("State",
  as.list(1:dim(cohorts)[1])))
transitionProbabilities(object, times, M = 100, stateNames = paste("State",
  as.list(1:dim(cohorts)[1])))

Arguments

`object`	either the output of `simulateCohort` or the `matrix` with the `probabilities` slot of that output.
`times`	a time vector.
`M`	number of groups for calculating confidence intervals.
`stateNames`	a list with the names of states.

Value

an object of class "PosteriorProbabilities", containing the statenames, timepoints and the transition probabilities with pointwise prediction intervals over time.

Author(s)

Luisa Salazar Vizcaya, Nello Blaser, Thomas Gsponer

Package 'gems'

Help Index

Class "ArtCohort"

Description

Usage

Arguments

Slots

Objects from the Class

Author(s)

See Also

Examples

transition probabilities

Description

Usage

Arguments

Value

Author(s)

References

See Also

gems: Generalized Multistate Simulation Model

Description

References

generate template for transition functions

Description

Usage

Arguments

Value

Author(s)

References

See Also

generate a template for parameter covariances

Description

Usage

Arguments

Value

Author(s)

References

See Also

generate a template for mean parameters

Description

Usage

Arguments

Value

Author(s)

References

See Also

Class "PosteriorProbabilities"

Description

Usage

Arguments

Slots

Objects from the Class

Author(s)

See Also

Examples

Simulate cohort

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

tavi data set

Description

Format

References

Examples

Class "transition.structure"

Description

Usage

Arguments

Slots

Objects from the Class

Author(s)

See Also

Examples

transition probabilities

Class `"ArtCohort"`

Class `"PosteriorProbabilities"`

Class `"transition.structure"`