Do the predictNMB simulation, evaluating the net monetary benefit (NMB) of the simulated model.

Do the predictNMB simulation, evaluating the net monetary benefit (NMB) of the simulated model.

Usage

do_nmb_sim(
  sample_size,
  n_sims,
  n_valid,
  sim_auc,
  event_rate,
  cutpoint_methods = get_inbuilt_cutpoint_methods(),
  fx_nmb_training,
  fx_nmb_evaluation,
  meet_min_events = TRUE,
  min_events = NA,
  show_progress = FALSE,
  cl = NULL
)

Arguments

sample_size

Sample size of training set. If missing, a sample size calculation will be performed and the calculated size will be used.

n_sims

Number of simulations to run.

n_valid

Sample size for evaluation set.

sim_auc

Simulated model discrimination (AUC).

event_rate

Simulated event rate of the binary outcome being predicted. Also known as prevalence.

cutpoint_methods

A value or vector of cutpoint methods to include. Defaults to use the inbuilt methods:

• "all" = treat all patients (cutpoint = 0)

• "none" = treat no patients (cutpoint = 1)

• "value_optimising" = select the cutpoint that maximises NMB

• "youden" = select cutpoint based on the Youden index, also known as the J-index (sensitivity + specificity - 1)

• "cost_minimising" = select the cutpoint that minimises expected value of costs

• "prod_sens_spec" = product of sensitivity and specificity (sensitivity * specificity)

• "roc01" = selects the closest threshold to the (0,1) point on the ROC curve

User-defined cutpoint methods can be used by passing the name of a function that takes the following arguments:

• predicted (predicted probabilities)

• actual (the actual, binary outcome)

• nmb (a named vector containing NMB values assigned to each predicted class (i.e. c("TP" = -3, "TN" = 0, "FP" = -1, "FN" = -4)))

See ?get_thresholds for an example of a user-defined cutpoint function.

fx_nmb_training

Function or NMBsampler that returns a named vector of NMB assigned to classifications used for obtaining cutpoint on training set.

fx_nmb_evaluation

Function or NMBsampler that returns a named vector of NMB assigned to classifications used for obtaining cutpoint on evaluation set.

meet_min_events

Whether or not to incrementally add samples until the expected number of events (sample_size * event_rate) is met. (Applies to sampling of training data only.)

min_events

The minimum number of events to include in the training sample. If less than this number are included in sample of size sample_size, additional samples are added until the min_events is met. The default (NA) will use the expected value given the event_rate and the sample_size.

show_progress

Logical. Whether to display a progress bar. Requires the pbapply package.

cl

A cluster made using parallel::makeCluster(). If a cluster is provided, the simulation will be done in parallel.

Value

Returns a predictNMBsim object.

Details

This function runs a simulation for a given set of inputs that represent a healthcare setting using model-guided interventions.

The arguments fx_nmb_training and fx_nmb_evaluation should be functions that capture the treatment being used, its costs and effectiveness, and the costs of the outcome being treated/prevented.

Both of these are functions that return a named vector of NMB values when called and are used for obtaining and evaluating cutpoints, respectively. For example, the following function returns the appropriately named vector.

get_nmb <- function() c("TP" = -3, "TN" = 0, "FP" = -1, "FN" = -4)

There is a helper function, get_nmb_sampler(), to help you create these.

Examples

# \donttest{
get_nmb <- function() c("TP" = -3, "TN" = 0, "FP" = -1, "FN" = -4)
do_nmb_sim(
  sample_size = 200, n_sims = 50, n_valid = 10000, sim_auc = 0.7,
  event_rate = 0.1, fx_nmb_training = get_nmb, fx_nmb_evaluation = get_nmb
)
#> predictNMB object
#> 
#> Training data sample size:  200
#> Minimum number of events in training sample:  20
#> Evaluation data sample size:  10000
#> Number of simulations:  50
#> Simulated AUC:  0.7
#> Simulated event rate:  0.1
# }