Transitions for an individual-level continuous time state transition model
Simulate health state transitions in an individual-level continuous time state transition model using parameters from a multi-state model.
An R6::R6Class object.
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]
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.
hesim::CtstmTrans -> IndivCtstmTrans
params: An object of class params_surv or params_surv_list.
input_data: Input 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_mat: A 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_state: A 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_age: A 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_state: The 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, "mix" for a mixture of clock-reset and clock-forward models by state, and "mixt" for a mixture of clock-reset and clock-forward models by transition. 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. If "mixt" is used, then transition_types must be specified.
reset_states: A 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".
transition_types: A vector denoting the type of transition. The vector is of the same length as the number of transitions and takes values "reset", "time" or "age" for hazards that are functions of reset time, time since study entry or age, respectively. Only used if clock = "mixt".
new()Create a new IndivCtstmTrans object.
IndivCtstmTrans$new(
params,
input_data,
trans_mat,
start_state = 1,
start_age = 38,
death_state = NULL,
clock = c("reset", "forward", "mix", "mixt"),
reset_states = NULL,
transition_types = NULL
)
params: The params field.
input_data: The input_data field.
trans_mat: The trans_mat field.
start_state: The start_state field.
start_age: The start_age field.
death_state: The death_state field.
clock: The clock field.
reset_states: The reset_states field.
transition_types: The transition_types field.
A new IndivCtstmTrans object.
sim_disease()Simulate disease progression (i.e., individual trajectories through a multi-state model using an individual patient simulation).
IndivCtstmTrans$sim_disease(max_t = 100, max_age = 100, progress = NULL)
max_t: A 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_age: A scalar or vector denoting the maximum age to simulate each patient until. If a vector, must be equal to the number of simulated patients.
progress: An 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.
An object of class disprog.
sim_stateprobs()Simulate health state probabilities from a disprog object.
IndivCtstmTrans$sim_stateprobs(t, disprog = NULL, ...)
t: A numeric vector of times.
disprog: A disprog object. If NULL, then this will be simulated prior to computing state probabilities using IndivCtstm$sim_disease().
...: Additional arguments to pass to IndivCtstm$sim_disease() if disprog = NULL.
An object of class stateprobs.
check()Input validation for class. Checks that fields are the correct type.
IndivCtstmTrans$check()
clone()The objects of this class are cloneable with this method.
IndivCtstmTrans$clone(deep = FALSE)
deep: Whether to make a deep clone.
Useful links