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.
analyse_ce( markov_simulations = NULL, baseline = 1, willingness_to_pay_threshold = 20000, type = "base" )
| markov_simulations | A dataframe of markov samples and simulations as produced by |
|---|---|
| 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. |
A list of dataframes including: Cost effectiveness measures for each sample, and summarised cost effectiveness measures across samples.
sims <- markov_simulation_pipeline(example_two_state_markov(), duration = 10, samples = 10) analyse_ce(sims)#> $simulations_with_ce #> # A tibble: 20 x 12 #> sample intervention transition state_cost intervention_co… cohort qalys #> <int> <chr> <list> <list> <dbl> <list> <lis> #> 1 1 SoC <dbl[,2] … <dbl [2]> 0 <dbl … <dbl… #> 2 1 Soc with We… <dbl[,2] … <dbl [2]> 50 <dbl … <dbl… #> 3 2 SoC <dbl[,2] … <dbl [2]> 0 <dbl … <dbl… #> 4 2 Soc with We… <dbl[,2] … <dbl [2]> 50 <dbl … <dbl… #> 5 3 SoC <dbl[,2] … <dbl [2]> 0 <dbl … <dbl… #> 6 3 Soc with We… <dbl[,2] … <dbl [2]> 50 <dbl … <dbl… #> 7 4 SoC <dbl[,2] … <dbl [2]> 0 <dbl … <dbl… #> 8 4 Soc with We… <dbl[,2] … <dbl [2]> 50 <dbl … <dbl… #> 9 5 SoC <dbl[,2] … <dbl [2]> 0 <dbl … <dbl… #> 10 5 Soc with We… <dbl[,2] … <dbl [2]> 50 <dbl … <dbl… #> 11 6 SoC <dbl[,2] … <dbl [2]> 0 <dbl … <dbl… #> 12 6 Soc with We… <dbl[,2] … <dbl [2]> 50 <dbl … <dbl… #> 13 7 SoC <dbl[,2] … <dbl [2]> 0 <dbl … <dbl… #> 14 7 Soc with We… <dbl[,2] … <dbl [2]> 50 <dbl … <dbl… #> 15 8 SoC <dbl[,2] … <dbl [2]> 0 <dbl … <dbl… #> 16 8 Soc with We… <dbl[,2] … <dbl [2]> 50 <dbl … <dbl… #> 17 9 SoC <dbl[,2] … <dbl [2]> 0 <dbl … <dbl… #> 18 9 Soc with We… <dbl[,2] … <dbl [2]> 50 <dbl … <dbl… #> 19 10 SoC <dbl[,2] … <dbl [2]> 0 <dbl … <dbl… #> 20 10 Soc with We… <dbl[,2] … <dbl [2]> 50 <dbl … <dbl… #> # … with 5 more variables: total_costs <dbl>, total_qalys <dbl>, #> # incremental_costs <dbl>, incremental_qalys <dbl>, #> # incremental_net_benefit <dbl> #> #> $summarised_ce #> # A tibble: 2 x 13 #> intervention mean_costs sd_costs mean_qalys sd_qlays mean_incrementa… #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 SoC 0 0 4.15 0.0284 0 #> 2 Soc with We… 50 0 4.16 0.0269 0.00854 #> # … with 7 more variables: sd_incremental_qlays <dbl>, #> # mean_incremental_costs <dbl>, sd_incremental_costs <dbl>, #> # mean_incremental_net_benefit <dbl>, sd_incremental_net_benefit <dbl>, #> # probability_cost_effective <dbl>, icer <dbl> #>