predictNMB (version 0.2.1)

get_nmb_sampler: Make a NMB sampler for use in do_nmb_sim() or screen_simulation_inputs()

Description

Make a NMB sampler for use in do_nmb_sim() or screen_simulation_inputs()

Usage

get_nmb_sampler(
  outcome_cost,
  wtp,
  qalys_lost,
  high_risk_group_treatment_effect,
  high_risk_group_treatment_cost,
  low_risk_group_treatment_effect = 0,
  low_risk_group_treatment_cost = 0,
  use_expected_values = FALSE,
  nboot = 10000
)

Value

Returns a NMBsampler object.

Arguments

outcome_cost

The cost of the outcome. Must be provided if wtp and qalys_lost are not. Or can be used in addition to these arguments to represent additional cost to the health burden.

wtp

Willingness-to-pay.

qalys_lost

Quality-adjusted life years (QALYs) lost due to healthcare event being predicted.

high_risk_group_treatment_effect

The effect of the treatment provided to patients given high risk prediction. Can be a number of a function. Provide a function to incorporate uncertainty.

high_risk_group_treatment_cost

The cost of the treatment provided to patients given high risk prediction. Can be a number of a function. Provide a function to incorporate uncertainty.

low_risk_group_treatment_effect

The effect of the treatment provided to patients given low risk prediction. Can be a number of a function. Provide a function to incorporate uncertainty. Defaults to 0 (no treatment).

low_risk_group_treatment_cost

The cost of the treatment provided to patients given low risk prediction. Can be a number of a function. Provide a function to incorporate uncertainty. Defaults to 0 (no treatment).

use_expected_values

Logical. If TRUE, gets the mean of many samples from the produced function and returns these every time. This is a sensible choice when using the resulting function for selecting the cutpoint. See fx_nmb_training. Defaults to FALSE.

nboot

The number of samples to use when creating a function that returns the expected values. Defaults to 10000.

Examples

get_nmb_training <- get_nmb_sampler(
  outcome_cost = 100,
  high_risk_group_treatment_effect = function() rbeta(1, 1, 2),
  high_risk_group_treatment_cost = 10,
  use_expected_values = TRUE
)
get_nmb_evaluation <- get_nmb_sampler(
  outcome_cost = 100,
  high_risk_group_treatment_effect = function() rbeta(1, 1, 2),
  high_risk_group_treatment_cost = 10
)

get_nmb_training()
get_nmb_training()
get_nmb_training()
get_nmb_evaluation()
get_nmb_evaluation()
get_nmb_evaluation()