IndivCtstmTrans: Transitions for an individual-level continuous time state...
In hesim: Health Economic Simulation Modeling and Decision Analysis
IndivCtstmTrans R Documentation
Transitions for an individual-level continuous time state transition model
Description
Simulate health state transitions in an individual-level continuous time state
transition model using parameters from a multi-state model.
Format
An R6::R6Class object.
Super class
hesim::CtstmTrans -> IndivCtstmTrans
Public fields
paramsAn object of class params_surv or params_surv_list.
input_dataInput data used to simulate health state transitions
by sample from the probabilistic sensitivity analysis (PSA), treatment strategy and patient.
Must be an object of class input_mats. If params contains parameters from
a list of models (i.e., of class params_surv_list), then input_data
must contain a unique row for each treatment strategy
and patient; if params contains parameters from a joint model
(i.e., of class params_surv), then input_data must contain a unique
row for each treatment strategy, patient, and transition.
trans_matA transition matrix describing the states and transitions
in a multi-state model in the format from the mstate package.
See the documentation for the argument "trans" in mstate::msprep.
start_stateA scalar or vector denoting the starting health state.
Default is the first health state. If a vector, must be equal to the number of simulated patients.
start_ageA scalar or vector denoting the starting age of each patient
in the simulation. Default is 38. If a vector, must be equal to the number of simulated patients.
death_stateThe death state in trans_mat. Used with max_age
in sim_disease as patients transition to this state upon reaching maximum age.
By default, it is set to the final absorbing state (i.e., a row in trans_mat with all NAs).
clock"reset" for a clock-reset model, "forward" for a clock-forward model,
and "mix" for a mixture of clock-reset and clock-forward models. A clock-reset model
is a semi-Markov model in which transition rates depend on time since entering a state.
A clock-forward model is a Markov model in which transition rates depend on time
since entering the initial state. If "mix" is used, then
reset_states must be specified.
reset_statesA vector denoting the states in which time resets.
Hazard functions are always a function of elapsed time since either the
start of the model or from when time was previously reset. Only used if
clock = "mix".
Methods
Public methods
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Inherited methods
Method new()
Create a new IndivCtstmTrans object.
Usage
IndivCtstmTrans$new(
params,
input_data,
trans_mat,
start_state = 1,
start_age = 38,
death_state = NULL,
clock = c("reset", "forward", "mix"),
reset_states = NULL
)
Arguments
paramsThe params field.
input_dataThe input_data field.
trans_matThe trans_mat field.
start_stateThe start_state field.
start_ageThe start_age field.
death_stateThe death_state field.
clockThe clock field.
reset_statesThe reset_states field.
Returns
A new IndivCtstmTrans object.
Method sim_disease()
Simulate disease progression (i.e., individual trajectories through a
multi-state model using an individual patient simulation).
Usage
IndivCtstmTrans$sim_disease(max_t = 100, max_age = 100, progress = NULL)
Arguments
max_tA scalar or vector denoting the length of time to simulate the model.
If a vector, must be equal to the number of simulated patients.
max_ageA scalar or vector denoting the maximum age to simulate
each patient until. If a vector, must be equal to the number of simulated patients.
progressAn integer, specifying the PSA iteration (i.e., sample) that
should be printed every progress PSA iterations. For example, if progress = 2,
then every second PSA iteration is printed. Default is NULL, in which case
no output is printed.
Returns
An object of class disprog.
Method sim_stateprobs()
Simulate health state probabilities from a disprog object.
Usage
IndivCtstmTrans$sim_stateprobs(t, disprog = NULL, ...)
Arguments
tA numeric vector of times.
disprogA disprog object. If
NULL, then this will be simulated prior to computing state probabilities
using IndivCtstm$sim_disease().
...Additional arguments to pass to IndivCtstm$sim_disease() if
disprog = NULL.
Returns
An object of class stateprobs.
Method check()
Input validation for class. Checks that fields are the correct type.
Usage
IndivCtstmTrans$check()
Method clone()
The objects of this class are cloneable with this method.
Usage
IndivCtstmTrans$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
See Also
IndivCtstmTrans objects are conveniently created from either
fitted models or parameter objects with create_IndivCtstmTrans().
A complete economic model can be implemented with the IndivCtstm class.
Examples
library("flexsurv")
# Simulation data
strategies <- data.frame(strategy_id = c(1, 2, 3))
patients <- data.frame(patient_id = seq(1, 3),
age = c(45, 50, 60),
female = c(0, 0, 1))
# Multi-state model with transition specific models
tmat <- rbind(c(NA, 1, 2),
c(NA, NA, 3),
c(NA, NA, NA))
fits <- vector(length = max(tmat, na.rm = TRUE), mode = "list")
for (i in 1:length(fits)){
fits[[i]] <- flexsurvreg(Surv(years, status) ~ 1,
data = bosms3[bosms3$trans == i, ],
dist = "exp")
}
fits <- flexsurvreg_list(fits)
# Simulation model
hesim_dat <- hesim_data(strategies = strategies,
patients = patients)
fits_data <- expand(hesim_dat)
transmod <- create_IndivCtstmTrans(fits, input_data = fits_data,
trans_mat = tmat,
n = 2)
head(transmod$hazard(c(1, 2, 3)))
head(transmod$cumhazard(c(1, 2, 3)))
## Simulate disease progression and state probabilities together
transmod$sim_stateprobs(t = c(0, 5, 10))[t == 5]
## Simulate disease progression and state probabilities separately
disprog <- transmod$sim_disease(max_t = 10)
transmod$sim_stateprobs(t = c(0, 5, 10), disprog = disprog)[t == 5]
hesim documentation built on Sept. 4, 2022, 1:06 a.m.
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| IndivCtstmTrans | R Documentation |
Transitions for an individual-level continuous time state transition model
Description
Simulate health state transitions in an individual-level continuous time state transition model using parameters from a multi-state model.
Format
An R6::R6Class object.
Super class
hesim::CtstmTrans -> IndivCtstmTrans
Public fields
paramsAn object of class
params_survorparams_surv_list.input_dataInput data used to simulate health state transitions by sample from the probabilistic sensitivity analysis (PSA), treatment strategy and patient. Must be an object of class
input_mats. Ifparamscontains parameters from a list of models (i.e., of classparams_surv_list), theninput_datamust contain a unique row for each treatment strategy and patient; ifparamscontains parameters from a joint model (i.e., of classparams_surv), theninput_datamust contain a unique row for each treatment strategy, patient, and transition.trans_matA transition matrix describing the states and transitions in a multi-state model in the format from the
mstatepackage. See the documentation for the argument"trans"inmstate::msprep.start_stateA scalar or vector denoting the starting health state. Default is the first health state. If a vector, must be equal to the number of simulated patients.
start_ageA scalar or vector denoting the starting age of each patient in the simulation. Default is 38. If a vector, must be equal to the number of simulated patients.
death_stateThe death state in
trans_mat. Used withmax_ageinsim_diseaseas patients transition to this state upon reaching maximum age. By default, it is set to the final absorbing state (i.e., a row intrans_matwith all NAs).clock"reset" for a clock-reset model, "forward" for a clock-forward model, and "mix" for a mixture of clock-reset and clock-forward models. A clock-reset model is a semi-Markov model in which transition rates depend on time since entering a state. A clock-forward model is a Markov model in which transition rates depend on time since entering the initial state. If
"mix"is used, thenreset_statesmust be specified.reset_statesA vector denoting the states in which time resets. Hazard functions are always a function of elapsed time since either the start of the model or from when time was previously reset. Only used if
clock = "mix".
Methods
Public methods
Inherited methods
Method new()
Create a new IndivCtstmTrans object.
Usage
IndivCtstmTrans$new(
params,
input_data,
trans_mat,
start_state = 1,
start_age = 38,
death_state = NULL,
clock = c("reset", "forward", "mix"),
reset_states = NULL
)Arguments
paramsThe
paramsfield.input_dataThe
input_datafield.trans_matThe
trans_matfield.start_stateThe
start_statefield.start_ageThe
start_agefield.death_stateThe
death_statefield.clockThe
clockfield.reset_statesThe
reset_statesfield.
Returns
A new IndivCtstmTrans object.
Method sim_disease()
Simulate disease progression (i.e., individual trajectories through a multi-state model using an individual patient simulation).
Usage
IndivCtstmTrans$sim_disease(max_t = 100, max_age = 100, progress = NULL)
Arguments
max_tA scalar or vector denoting the length of time to simulate the model. If a vector, must be equal to the number of simulated patients.
max_ageA scalar or vector denoting the maximum age to simulate each patient until. If a vector, must be equal to the number of simulated patients.
progressAn integer, specifying the PSA iteration (i.e., sample) that should be printed every progress PSA iterations. For example, if progress = 2, then every second PSA iteration is printed. Default is NULL, in which case no output is printed.
Returns
An object of class disprog.
Method sim_stateprobs()
Simulate health state probabilities from a disprog object.
Usage
IndivCtstmTrans$sim_stateprobs(t, disprog = NULL, ...)
Arguments
tA numeric vector of times.
disprogA disprog object. If
NULL, then this will be simulated prior to computing state probabilities usingIndivCtstm$sim_disease()....Additional arguments to pass to
IndivCtstm$sim_disease()ifdisprog = NULL.
Returns
An object of class stateprobs.
Method check()
Input validation for class. Checks that fields are the correct type.
Usage
IndivCtstmTrans$check()
Method clone()
The objects of this class are cloneable with this method.
Usage
IndivCtstmTrans$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
See Also
IndivCtstmTrans objects are conveniently created from either
fitted models or parameter objects with create_IndivCtstmTrans().
A complete economic model can be implemented with the IndivCtstm class.
Examples
library("flexsurv")
# Simulation data
strategies <- data.frame(strategy_id = c(1, 2, 3))
patients <- data.frame(patient_id = seq(1, 3),
age = c(45, 50, 60),
female = c(0, 0, 1))
# Multi-state model with transition specific models
tmat <- rbind(c(NA, 1, 2),
c(NA, NA, 3),
c(NA, NA, NA))
fits <- vector(length = max(tmat, na.rm = TRUE), mode = "list")
for (i in 1:length(fits)){
fits[[i]] <- flexsurvreg(Surv(years, status) ~ 1,
data = bosms3[bosms3$trans == i, ],
dist = "exp")
}
fits <- flexsurvreg_list(fits)
# Simulation model
hesim_dat <- hesim_data(strategies = strategies,
patients = patients)
fits_data <- expand(hesim_dat)
transmod <- create_IndivCtstmTrans(fits, input_data = fits_data,
trans_mat = tmat,
n = 2)
head(transmod$hazard(c(1, 2, 3)))
head(transmod$cumhazard(c(1, 2, 3)))
## Simulate disease progression and state probabilities together
transmod$sim_stateprobs(t = c(0, 5, 10))[t == 5]
## Simulate disease progression and state probabilities separately
disprog <- transmod$sim_disease(max_t = 10)
transmod$sim_stateprobs(t = c(0, 5, 10), disprog = disprog)[t == 5]
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