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.