An Algorithm to Generate Correlated Input-Parameters to Be Used in Probabilistic Sensitivity Analyses

Background: Assessment of uncertainty in cost-effectiveness analyses (CEAs) is paramount for decision-making. Probabilistic sensitivity analysis (PSA) estimates uncertainty by varying all input parameters simultaneously within predefined ranges; however, PSA often ignores correlations between parameters. Objective: To implement an efficient algorithm that integrates parameter correlation in PSA. Study design: An algorithm based on Cholesky decomposition was developed to generate multivariate non-normal parameter distributions for the age-dependent incidence of herpes zoster (HZ). The algorithm was implemented in an HZ CEA model and evaluated for gamma and beta distributions. The incremental cost-effectiveness ratio (ICER) and the probability of being cost-effective at a given ICER threshold were calculated for different levels of correlation. Five thousand Monte Carlo simulations were carried out. Results: Correlation coefficients between parameters sampled from the distribution generated by the algorithm matched the desired correlations for both distribution functions. With correlations set to 0.0, 0.5, and 0.9, 90% of the simulations showed ICERs below $25,000, $33,000, and $38,000 per quality-adjusted life-year (QALY), respectively, varying incidence only; and below $38,000, $48,000, and $58,000 per QALY, respectively, varying most parameters. Conclusion: Parameter correlation may impact the uncertainty of CEA results. We implemented an efficient method for generating correlated non-normal distributions for use in PSA.