Simulate outcomes from a cohort discrete time state transition model.

Format

An R6::R6Class object.

See also

Public fields

trans_model

The model for health state transitions. Must be an object of class CohortDtstmTrans.

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.

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().

Methods

Public methods


Method new()

Create a new CohortDtstm object.

Usage

CohortDtstm$new(trans_model = NULL, utility_model = NULL, cost_models = NULL)

Arguments

trans_model

The trans_model field.

utility_model

The utility_model field.

cost_models

The cost_models field.

Returns

A new CohortDtstm object.


Method sim_stateprobs()

Simulate health state probabilities using CohortDtstmTrans$sim_stateprobs().

Usage

CohortDtstm$sim_stateprobs(n_cycles)

Arguments

n_cycles

The number of model cycles to simulate the model for.

Returns

An instance of self with simulated output of class stateprobs stored in stateprobs_.


Method sim_qalys()

Simulate quality-adjusted life-years (QALYs) as a function of stateprobs_ and utility_model. See vignette("expected-values") for details.

Usage

CohortDtstm$sim_qalys(
  dr = 0.03,
  integrate_method = c("trapz", "riemann_left", "riemann_right"),
  lys = TRUE
)

Arguments

dr

Discount rate.

integrate_method

Method used to integrate state values when computing (QALYs).

lys

If TRUE, then life-years are simulated in addition to QALYs.

Returns

An instance of self with simulated output of class qalys stored in qalys_.


Method sim_costs()

Simulate costs as a function of stateprobs_ and cost_models. See vignette("expected-values") for details.

Usage

CohortDtstm$sim_costs(
  dr = 0.03,
  integrate_method = c("trapz", "riemann_left", "riemann_right")
)

Arguments

dr

Discount rate.

integrate_method

Method used to integrate state values when computing costs.

Returns

An instance of self with simulated output of class costs stored in costs_.


Method summarize()

Summarize costs and QALYs so that cost-effectiveness analysis can be performed. See summarize_ce().

Usage

CohortDtstm$summarize(by_grp = FALSE)

Arguments

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).


Method clone()

The objects of this class are cloneable with this method.

Usage

CohortDtstm$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library("data.table") library("ggplot2") theme_set(theme_bw()) set.seed(102) # NOTE: This example replicates the "Simple Markov cohort model" vignette # using a different approach (i.e., one that is not based on non-standard # evaluation). The non-standard evaluation based approach does (more or less) # what is done here under the hood. # (0) Model setup hesim_dat <- hesim_data( strategies = data.table( strategy_id = 1:2, strategy_name = c("Monotherapy", "Combination therapy") ), patients <- data.table(patient_id = 1), states = data.table( state_id = 1:3, state_name = c("State A", "State B", "State C") ) ) n_states <- nrow(hesim_dat$states) + 1 labs <- get_labels(hesim_dat) # (1) Parameters n_samples <- 10 # Number of samples for PSA ## Transition matrix ### Input data (one transition matrix for each parameter sample, ### treatment strategy, patient, and time interval) p_id <- tpmatrix_id(expand(hesim_dat, times = c(0, 2)), n_samples) N <- nrow(p_id) ### Transition matrices (one for each row in p_id) p <- array(NA, dim = c(n_states, n_states, nrow(p_id))) #### Baseline risk trans_mono <- rbind( c(1251, 350, 116, 17), c(0, 731, 512, 15), c(0, 0, 1312, 437), c(0, 0, 0, 469) ) mono_ind <- which(p_id$strategy_id == 1 | p_id$time_id == 2) p[,, mono_ind] <- rdirichlet_mat(n = 2, trans_mono) #### Apply relative risks combo_ind <- setdiff(1:nrow(p_id), mono_ind) lrr_se <- (log(.710) - log(.365))/(2 * qnorm(.975)) rr <- rlnorm(n_samples, meanlog = log(.509), sdlog = lrr_se) rr_indices <- list( # Indices of transition matrix to apply RR to c(1, 2), c(1, 3), c(1, 4), c(2, 3), c(2, 4), c(3, 4) ) rr_mat <- matrix(rr, nrow = n_samples, ncol = length(rr_indices)) p[,, combo_ind] <- apply_rr(p[, , mono_ind], rr = rr_mat, index = rr_indices) tp <- tparams_transprobs(p, p_id) ## Utility utility_tbl <- stateval_tbl( data.table( state_id = 1:3, est = c(1, 1, 1) ), dist = "fixed" ) ## Costs drugcost_tbl <- stateval_tbl( data.table( strategy_id = c(1, 1, 2, 2), time_start = c(0, 2, 0, 2), est = c(2278, 2278, 2278 + 2086.50, 2278) ), dist = "fixed" ) dmedcost_tbl <- stateval_tbl( data.table( state_id = 1:3, mean = c(A = 1701, B = 1774, C = 6948), se = c(A = 1701, B = 1774, C = 6948) ), dist = "gamma" ) cmedcost_tbl <- stateval_tbl( data.table( state_id = 1:3, mean = c(A = 1055, B = 1278, C = 2059), se = c(A = 1055, B = 1278, C = 2059) ), dist = "gamma" ) # (2) Simulation ## Constructing the economic model ### Transition probabilities transmod <- CohortDtstmTrans$new(params = tp) ### Utility utilitymod <- create_StateVals(utility_tbl, hesim_data = hesim_dat, n = n_samples) ### Costs drugcostmod <- create_StateVals(drugcost_tbl, hesim_data = hesim_dat, n = n_samples) dmedcostmod <- create_StateVals(dmedcost_tbl, hesim_data = hesim_dat, n = n_samples) cmedcostmod <- create_StateVals(cmedcost_tbl, hesim_data = hesim_dat, n = n_samples) costmods <- list(drug = drugcostmod, direct_medical = dmedcostmod, community_medical = cmedcostmod) ### Economic model econmod <- CohortDtstm$new(trans_model = transmod, utility_model = utilitymod, cost_models = costmods) ## Simulating outcomes econmod$sim_stateprobs(n_cycles = 20) autoplot(econmod$stateprobs_, ci = TRUE, ci_style = "ribbon", labels = labs)
econmod$sim_qalys(dr = 0, integrate_method = "riemann_right") econmod$sim_costs(dr = 0.06, integrate_method = "riemann_right") # (3) Decision analysis ce_sim <- econmod$summarize() wtp <- seq(0, 25000, 500) cea_pw_out <- cea_pw(ce_sim, comparator = 1, dr_qalys = 0, dr_costs = .06, k = wtp) format(icer(cea_pw_out))
#> Outcome 2 #> 1: Incremental QALYs 0.88 (0.42, 1.26) #> 2: Incremental costs 5,252 (401, 8,617) #> 3: Incremental NMB 38,957 (17,712, 56,120) #> 4: ICER 5,940