N-state partitioned survival model
Simulate outcomes from an N-state partitioned survival model.
An R6::R6Class object.
library("flexsurv") library("ggplot2") theme_set(theme_bw()) # Model setup strategies <- data.frame(strategy_id = c(1, 2, 3), strategy_name = paste0("Strategy ", 1:3)) patients <- data.frame(patient_id = seq(1, 3), age = c(45, 50, 60), female = c(0, 0, 1)) states <- data.frame(state_id = seq(1, 3), state_name = paste0("State ", seq(1, 3))) hesim_dat <- hesim_data(strategies = strategies, patients = patients, states = states) labs <- c( get_labels(hesim_dat), list(curve = c("Endpoint 1" = 1, "Endpoint 2" = 2, "Endpoint 3" = 3)) ) n_samples <- 2 # Survival models surv_est_data <- psm4_exdata$survival fit1 <- flexsurvreg(Surv(endpoint1_time, endpoint1_status) ~ factor(strategy_id), data = surv_est_data, dist = "exp") fit2 <- flexsurvreg(Surv(endpoint2_time, endpoint2_status) ~ factor(strategy_id), data = surv_est_data, dist = "exp") fit3 <- flexsurvreg(Surv(endpoint3_time, endpoint3_status) ~ factor(strategy_id), data = surv_est_data, dist = "exp") fits <- flexsurvreg_list(fit1, fit2, fit3) surv_input_data <- expand(hesim_dat, by = c("strategies", "patients")) psm_curves <- create_PsmCurves(fits, input_data = surv_input_data, uncertainty = "bootstrap", est_data = surv_est_data, n = n_samples) # Cost model(s) cost_input_data <- expand(hesim_dat, by = c("strategies", "patients", "states")) fit_costs_medical <- lm(costs ~ female + state_name, data = psm4_exdata$costs$medical) psm_costs_medical <- create_StateVals(fit_costs_medical, input_data = cost_input_data, n = n_samples) # Utility model utility_tbl <- stateval_tbl(tbl = data.frame(state_id = states$state_id, min = psm4_exdata$utility$lower, max = psm4_exdata$utility$upper), dist = "unif") psm_utility <- create_StateVals(utility_tbl, n = n_samples, hesim_data = hesim_dat) # Partitioned survival decision model psm <- Psm$new(survival_models = psm_curves, utility_model = psm_utility, cost_models = list(medical = psm_costs_medical)) psm$sim_survival(t = seq(0, 5, 1/12)) autoplot(psm$survival_, labels = labs, ci = FALSE, ci_style = "ribbon") psm$sim_stateprobs() autoplot(psm$stateprobs_, labels = labs) psm$sim_costs(dr = .03) head(psm$costs_) head(psm$sim_qalys(dr = .03)$qalys_)
Incerti and Jansen (2021). See Section 2.3 for a mathematical description of a PSM and Section 4.2 for an example in oncology. The mathematical approach used to simulate costs and QALYs from state probabilities is described in Section 2.1.
The PsmCurves documentation describes the class for the survival models and the StateVals documentation describes the class for the cost and utility models. A PsmCurves
object is typically created using create_PsmCurves(). The PsmCurves documentation provides an example in which the model is parameterized from parameter objects (i.e., without having the patient-level data required to fit a model with R). A longer example is provided in vignette("psm").
survival_models: The survival models used to predict survival curves. Must be an object of class PsmCurves.
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.
n_states: Number of states in the partitioned survival model.
t_: A numeric vector of times at which survival curves were predicted. Determined by the argument t in $sim_curves().
survival_: An object of class survival simulated using sim_survival().
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 $sim_costs().
new()Create a new Psm object.
Psm$new(survival_models = NULL, utility_model = NULL, cost_models = NULL)
survival_models: The survival_models field.
utility_model: The utility_model field.
cost_models: The cost_models field.
n_states is set equal to the number of survival models plus one.
A new Psm object.
sim_survival()Simulate survival curves as a function of time using PsmCurves$survival().
Psm$sim_survival(t)
t: A numeric vector of times. The first element must be 0.
An instance of self with simulated output from PsmCurves$survival()
stored in survival_.
sim_stateprobs()Simulate health state probabilities from survival_ using a partitioned survival analysis.
Psm$sim_stateprobs()
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 stateprobs_ and utility_model. See sim_qalys() for details.
Psm$sim_qalys(
dr = 0.03,
integrate_method = c("trapz", "riemann_left", "riemann_right"),
lys = TRUE
)
dr: Discount rate.
integrate_method: Method used to integrate state values when computing costs or QALYs. Options are trapz for the trapezoid rule, riemann_left for a left Riemann sum, and riemann_right for a right Riemann sum.
lys: If TRUE, then life-years are simulated in addition to QALYs.
An instance of self with simulated output of class qalys stored in qalys_.
sim_costs()Simulate costs as a function of stateprobs_ and cost_models. See sim_costs() for details.
Psm$sim_costs(
dr = 0.03,
integrate_method = c("trapz", "riemann_left", "riemann_right")
)
dr: Discount rate.
integrate_method: Method used to integrate state values when computing costs or QALYs. Options are trapz for the trapezoid rule, riemann_left for a left Riemann sum, and riemann_right for a right Riemann sum.
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().
Psm$summarize(by_grp = FALSE)
by_grp: If TRUE, then costs and QALYs are computed by subgroup. If FALSE, then costs and QALYs are aggregated across all patients (and subgroups).
clone()The objects of this class are cloneable with this method.
Psm$clone(deep = FALSE)
deep: Whether to make a deep clone.
Useful links