analyse_ce: Analyse the Cost Effectiveness of Interventions

In seabbs/SpeedyMarkov: Speed up Discrete Markov Model Cost Effectiveness Simulations

Description

This function produces cost effectiveness summary measures using the output of markov_simulation_pipeline or similar data structures. At least two interventions must be present.

Usage

analyse_ce(
  markov_simulations = NULL,
  baseline = 1,
  willingness_to_pay_threshold = 20000,
  type = "base"
)

Arguments

markov_simulations	A dataframe of markov samples and simulations as produced by markov_simulation_pipeline. At least two interventions must be present.
baseline	Numeric, the intervention to consider as the baseline for pairwise comparisons.
willingness_to_pay_threshold	Numeric, defaulting to 20,000. This is the threshold at which an intervention may be considered cost effective in the UK.
type	A character string specifying the approach to use to simulate the model. Currently implemented approaches are "base" with "base" as the default.

Value

A list of dataframes including: Cost effectiveness measures for each sample, and summarised cost effectiveness measures across samples.

Examples

sims <-  markov_simulation_pipeline(example_two_state_markov(),
                                    duration = 10, samples = 10)
  
analyse_ce(sims)
  

seabbs/SpeedyMarkov documentation built on Dec. 26, 2019, 4:38 a.m.