pmandaliya21/CUA-Trastuzumab-Kenya

R Scripts for Optimizing Trastuzumab Duration: Cost-Effectiveness Analysis of Five Regimens for HER2-Positive Breast Cancer in Kenya # CUA-Trastuzumab-Kenya R Scripts for Optimizing Trastuzumab Duration: Cost-Effectiveness Analysis of Five Regimens for HER2-Positive Breast Cancer in Kenya Data needed before running anything ------------------------------------ Create a data/ folder with: - model_parameters.csv - built from the values in S4_Appendix (Supplementary Material). Needs columns: Parameter, distribution, parameters, mean, sd, Base, min, max. - psa_batches/ - a subfolder, can be left empty. If you want to reproduce the exact PSA results without waiting hours for it to re-run, copy psa_params.rds and the batch_*.rds files from the Supplementary Material into data/psa_batches/ before running script 1. If the folder is empty, script 1 generates fresh draws itself (same seed, so results should match). Scripts - run in this order ---------------------------- 1. markov_model_psa.R Base case Markov model, full PSA (6000 iterations), one-way sensitivity analysis. Slow - budget a few hours. Can be run on its own. 2. psa_cost_table.R Item-level cost breakdown table, built from the PSA batch files script 1 produces. Can be run any time after script 1 has finished, fresh R session is fine. 3. markov_model_trastuzumab_cap.R Scenario analysis 1 - re-runs the base case with the trastuzumab price cap applied. 4. trastuzumab_price_threshold_scenario.R Scenario analysis 2 - price sweep. Must run straight after script 3, in the same R session (it uses markovmodel() and my_params_basecase still in memory). 5. budget_impact_analysis.R 5-year budget impact. Also must run in the same session as script 3, after script 4 (it uses output_basecase in memory). Scripts 3, 4 and 5 need to be run one after another without restarting R in between. Output ------ Excel tables go to output/tables/, plots go to output/graphs/. Both are created automatically. R packages needed ------------------ dampack, dplyr, tidyr, future, future.apply, ggplot2, patchwork, scales, openxlsx, writexl