Individual-level continuous time state transition model
Simulate outcomes from an individual-level continuous time state transition model (CTSTM). The class supports "clock-reset" (i.e., semi-Markov), "clock-forward" (i.e., Markov), and mixtures of clock-reset and clock-forward multi-state models as described in IndivCtstmTrans.
An R6::R6Class object.
library("flexsurv") # Treatment strategies, target population, and model structure strategies <- data.frame(strategy_id = c(1, 2)) patients <- data.frame(patient_id = seq(1, 3), age = c(45, 50, 60), female = c(0, 0, 1)) states <- data.frame(state_id = c(1, 2)) hesim_dat <- hesim_data(strategies = strategies, patients = patients, states = states) # Parameter estimation ## Multi-state model tmat <- rbind(c(NA, 1, 2), c(3, NA, 4), c(NA, NA, NA)) fits <- vector(length = max(tmat, na.rm = TRUE), mode = "list") surv_dat <- data.frame(mstate3_exdata$transitions) for (i in 1:length(fits)){ fits[[i]] <- flexsurvreg(Surv(years, status) ~ factor(strategy_id), data = surv_dat, subset = (trans == i), dist = "weibull") } fits <- flexsurvreg_list(fits) ## Utility utility_tbl <- stateval_tbl(data.frame(state_id = states$state_id, mean = mstate3_exdata$utility$mean, se = mstate3_exdata$utility$se), dist = "beta") ## Costs drugcost_tbl <- stateval_tbl(data.frame(strategy_id = strategies$strategy_id, est = mstate3_exdata$costs$drugs$costs), dist = "fixed") medcost_tbl <- stateval_tbl(data.frame(state_id = states$state_id, mean = mstate3_exdata$costs$medical$mean, se = mstate3_exdata$costs$medical$se), dist = "gamma") # Economic model n_samples = 2 ## Construct model ### Transitions transmod_data <- expand(hesim_dat) transmod <- create_IndivCtstmTrans(fits, input_data = transmod_data, trans_mat = tmat, n = n_samples) ### Utility utilitymod <- create_StateVals(utility_tbl, n = n_samples, hesim_data = hesim_dat) ### Costs drugcostmod <- create_StateVals(drugcost_tbl, n = n_samples, hesim_data = hesim_dat) medcostmod <- create_StateVals(medcost_tbl, n = n_samples, hesim_data = hesim_dat) costmods <- list(drugs = drugcostmod, medical = medcostmod) ### Combine ictstm <- IndivCtstm$new(trans_model = transmod, utility_model = utilitymod, cost_models = costmods) ## Simulate outcomes head(ictstm$sim_disease()$disprog_) head(ictstm$sim_stateprobs(t = c(0, 5, 10))$stateprobs_[t == 5]) ictstm$sim_qalys(dr = .03) ictstm$sim_costs(dr = .03) ### Summarize cost-effectiveness ce <- ictstm$summarize() head(ce) format(summary(ce), pivot_from = "strategy")
Incerti and Jansen (2021). See Section 2.2 for a mathematical description of an individual-level CTSTM and Section 4.1 for an example in oncology.
The IndivCtstmTrans documentation describes the class for the transition model and the StateVals documentation describes the class for the cost and utility models. An IndivCtstmTrans
object is typically created using create_IndivCtstmTrans().
There are currently two relevant vignettes. vignette("mstate") shows how to parameterize IndivCtstmTrans objects in cases where patient-level data is available by fitting a multi-state models. vignette("markov-inhomogeneous-indiv")
shows how the time inhomogeneous Markov cohort model in vignette("markov-inhomogeneous-cohort") can be developed as an individual patient simulation; in doing so, it shows how IndivCtstm models can be used even without access to patient-level data.
trans_model: The model for health state transitions. Must be an object of class IndivCtstmTrans.
utility_model: The model for health state utility. Must be an object of class StateVals.
cost_models: The models used to predict costs by health state. Must be a list of objects of class StateVals, where each element of the list represents a different cost category.
disprog_: An object of class disprog.
stateprobs_: An object of class stateprobs simulated using $sim_stateprobs().
qalys_: An object of class qalys simulated using $sim_qalys().
costs_: An object of class costs simulated using $sim_costs().
new()Create a new IndivCtstm object.
IndivCtstm$new(trans_model = NULL, utility_model = NULL, cost_models = NULL)
trans_model: The trans_model field.
utility_model: The utility_model field.
cost_models: The cost_models field.
A new IndivCtstm object.
sim_disease()Simulate disease progression (i.e., individual trajectories through a multi-state model) using IndivCtstmTrans$sim_disease().
IndivCtstm$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 instance of self with simulated output stored in disprog_.
sim_stateprobs()Simulate health state probabilities as a function of time using the simulation output stored in disprog.
IndivCtstm$sim_stateprobs(t)
t: A numeric vector of times.
An instance of self with simulated output of class stateprobs
stored in stateprobs_.
sim_qalys()Simulate quality-adjusted life-years (QALYs) as a function of disprog_ and utility_model.
IndivCtstm$sim_qalys(
dr = 0.03,
type = c("predict", "random"),
lys = TRUE,
by_patient = FALSE
)
dr: Discount rate.
type: "predict" for mean values or "random" for random samples as in $sim() in StateVals.
lys: If TRUE, then life-years are simulated in addition to QALYs.
by_patient: If TRUE, then QALYs and/or costs are computed at the patient level. If FALSE, then they are averaged across patients by health state.
An instance of self with simulated output of class qalys stored in qalys_.
sim_costs()Simulate costs as a function of disprog_ and cost_models.
IndivCtstm$sim_costs(
dr = 0.03,
type = c("predict", "random"),
by_patient = FALSE,
max_t = Inf
)
dr: Discount rate.
type: "predict" for mean values or "random" for random samples as in $sim() in StateVals.
by_patient: If TRUE, then QALYs and/or costs are computed at the patient level. If FALSE, then they are averaged across patients by health state.
max_t: Maximum time duration to compute costs once a patient has entered a (new) health state. By default, equal to Inf, so that costs are computed over the entire duration that a patient is in a given health state. If time varies by each cost category, then time can also be passed as a numeric vector of length equal to the number of cost categories (e.g., c(1, 2, Inf, 3) for a model with four cost categories).
An instance of self with simulated output of class costs
stored in costs_.
summarize()Summarize costs and QALYs so that cost-effectiveness analysis can be performed. See summarize_ce().
IndivCtstm$summarize(by_grp = FALSE)
by_grp: If TRUE, then costs and QALYs are computed by subgroup. If FALSE, then costs and QALYs are aggregated across all patients (and subgroups).
clone()The objects of this class are cloneable with this method.
IndivCtstm$clone(deep = FALSE)
deep: Whether to make a deep clone.
Useful links