A model expression is defined by specifying random number generation functions
for a probabilistic sensitivity analysis (PSA) and transformations of the sampled
parameters as a function of input_data. The unevaluated expressions
are evaluated with eval_model() and used to generate the model inputs needed to
create an economic model.
define_model(tparams_def, rng_def, params = NULL, n_states = NULL) eval_model(x, input_data)
| tparams_def | A tparams_def object or a list of
tparams_def objects. A list might be considered if time intervals
specified with the |
|---|---|
| rng_def | A rng_def object used to randomly draw samples of the parameters from suitable probability distributions. |
| params | Either (i) a list containing the values of parameters for random
number generation or (ii) parameter samples that have already been randomly
generated using |
| n_states | The number of health states (inclusive of all health states
including the the death state) in the model. If |
| x | An object of class |
| input_data | An object of class expanded_hesim_data expanded by patients and treatment strategies. |
define_model() returns an object of class model_def,
which is a list containing the arguments to the function. eval_model() returns
a list containing ID variables
identifying parameter samples, treatment strategies, patient cohorts, and time
intervals; the values of parameters of the transition probability matrix,
utilities, and/or cost categories; the number of health states; and the number
of random number generation samples for the PSA.
eval_model() evaluates the expressions in an object of class
model_def returned by define_model() and is, in turn, used within
functions that instantiate economic models (e.g., create_CohortDtstm()).
The direct output of eval_model() can also be useful for understanding and debugging
model definitions, but it is not used directly for simulation.
Economic models are constructed as a function of input data and parameters:
Input data: Objects of class expanded_hesim_data consisting of the treatment strategies and patient population.
Parameters: The underlying parameter estimates from the literature
are first stored in a list (params argument). Random number generation
is then used to sample the parameters from suitable probability distributions
for the PSA (rng_def argument). Finally, the sampled parameters are
transformed as a function of the input data into values (e.g., elements of a
transition probability matrix) used for the simulation (tparams_def argument).
The params argument can be omitted if the underlying parameters values are
defined inside a define_rng() block.
# Data library("data.table") strategies <- data.table(strategy_id = 1:2, strategy_name = c("Monotherapy", "Combination therapy")) patients <- data.table(patient_id = 1) hesim_dat <- hesim_data(strategies = strategies, patients = patients) data <- expand(hesim_dat) # Model parameters rng_def <- define_rng({ alpha <- matrix(c(1251, 350, 116, 17, 0, 731, 512, 15, 0, 0, 1312, 437, 0, 0, 0, 469), nrow = 4, byrow = TRUE) rownames(alpha) <- colnames(alpha) <- c("A", "B", "C", "D") lrr_mean <- log(.509) lrr_se <- (log(.710) - log(.365))/(2 * qnorm(.975)) list( p_mono = dirichlet_rng(alpha), rr_comb = lognormal_rng(lrr_mean, lrr_se), u = 1, c_zido = 2278, c_lam = 2086.50, c_med = gamma_rng(mean = c(A = 2756, B = 3052, C = 9007), sd = c(A = 2756, B = 3052, C = 9007)) ) }, n = 2) tparams_def <- define_tparams({ rr = ifelse(strategy_name == "Monotherapy", 1, rr_comb) list( tpmatrix = tpmatrix( C, p_mono$A_B * rr, p_mono$A_C * rr, p_mono$A_D * rr, 0, C, p_mono$B_C * rr, p_mono$B_D * rr, 0, 0, C, p_mono$C_D * rr, 0, 0, 0, 1), utility = u, costs = list( drug = ifelse(strategy_name == "Monotherapy", c_zido, c_zido + c_lam), medical = c_med ) ) }) # Simulation ## Define the economic model model_def <- define_model( tparams_def = tparams_def, rng_def = rng_def) ### Evaluate the model expression to generate model inputs ### This can be useful for understanding the output of a model expression eval_model(model_def, data)#> $id #> $id[[1]] #> sample strategy_id patient_id time time_start #> 1: 1 1 1 0 0 #> 2: 1 2 1 0 0 #> 3: 2 1 1 0 0 #> 4: 2 2 1 0 0 #> #> #> $tpmatrix #> s1_s1 s1_s2 s1_s3 s1_s4 s2_s1 s2_s2 s2_s3 #> 1: 0.7303831 0.18679555 0.07260427 0.010217072 0 0.6025887 0.3872602 #> 2: 0.8823737 0.08149364 0.03167520 0.004457421 0 0.8266206 0.1689507 #> 3: 0.7129687 0.20776387 0.06806632 0.011201099 0 0.6137990 0.3760511 #> 4: 0.8859624 0.08254467 0.02704278 0.004450201 0 0.8465622 0.1494053 #> s2_s4 s3_s1 s3_s2 s3_s3 s3_s4 s4_s1 s4_s2 s4_s3 s4_s4 #> 1: 0.010151107 0 0 0.7420537 0.2579463 0 0 0 1 #> 2: 0.004428642 0 0 0.8874653 0.1125347 0 0 0 1 #> 3: 0.010149909 0 0 0.7463815 0.2536185 0 0 0 1 #> 4: 0.004032563 0 0 0.8992373 0.1007627 0 0 0 1 #> #> $utility #> [1] 1 1 1 1 #> attr(,"id_index") #> [1] 1 #> #> $costs #> $costs$drug #> [1] 2278.0 4364.5 2278.0 4364.5 #> attr(,"id_index") #> [1] 1 #> #> $costs$medical #> A B C #> 1: 1463.6975 1262.136 15708.566 #> 2: 1463.6975 1262.136 15708.566 #> 3: 293.2644 4551.563 7743.327 #> 4: 293.2644 4551.563 7743.327 #> #> #> $n_states #> [1] 4 #> #> $n #> [1] 2 #> #> attr(,"class") #> [1] "eval_model"