Purpose: Probabilistic sensitivity analysis (PSA) is a recommended approach by ISPOR‐SMDM Modeling Good Research Practices Task Force and a necessary step for value of information analysis. However, conducting PSA can be computationally challenging and often impractical in large-scale patient-level simulation (PLS) models (e.g. microsimulation, discrete-event simulation, agent-based models). Our purpose was to conduct PSA using Latin Hypercube sampling and compare results with a commonly used approach of Monte Carlo sampling.
Method: We developed a Markov PLS model to conduct cost-effectiveness analysis of hepatitis C treatment where states included METAVIR fibrosis scores (F0-F4), decompensated cirrhosis, hepatocellular carcinoma, liver transplant, and liver-related death. We used 33 parameters to perform PSA which included state transition probabilities, utility weights and costs. We used two sampling techniques: random sampling (RS), and Latin Hypercube sampling (LHS), a type of stratified sampling technique. We ran PSA with different number of samples, n=100,1000 (2nd-order uncertainty) resulting in RS100, RS1000, LHS100, LHS1000 strategies using 1000 iterations within each run (1st-order uncertainty). Using independent initial random-seeds, we obtained 20 sets of results for each sampling strategy and estimated standard error (SE) in the mean cost, QALYs, incremental cost-effectiveness ratios (ICERs), and their lower and upper 95% confidence limits. We compared these outcomes with a "gold standard" (GS), an outcome of extensive random sampling of 100,000 PSA inputs. Finally, we identified influential inputs based on each method and plotted cost-effectiveness acceptability curves.
Result: No trend was observed using 100 samples. Using 1000 samples, SE with LHS decreased in comparison with RS by 35-43% in costs, 37-48% in QALYs, 13-40% in confidence-intervals of costs, and 27-49% in confidence-intervals of QALYs (table). The total bias in costs and QALYs obtained with all sampling strategies was less than 4% when compared to GS. However, ICERs obtained with RS100, LHS100, RS1000 and LHS1000 were higher than that obtained with GS by 44%, 72%, 42%, and 25%, respectively.
Conclusion: Compared with standard Monte Carlo sampling the bias in costs and QALYs may reduce substantially with Latin Hypercube sampling; however, large samples are needed to reduce bias in ICERs. Results with Latin Hypercube sampling are less dependent on initial random seed as compared to random sampling. 