Comparison of Markov Model and Discrete-Event Simulation Techniques for HIV

Abstract

Background: Markov models have been the standard framework for predicting long-term clinical and economic outcomes using the surrogate marker endpoints from clinical trials. However, they are complex, have intensive data requirements and are often difficult for decision makers to understand. Recent developments in modelling software have made it possible to use discrete-event simulation (DES) to model outcomes in HIV. Using published results from 48-week trial data as model inputs, Markov model and DES modelling approaches were compared in terms of clinical outcomes at 5 years and lifetime cost-effectiveness estimates.

Methods: A randomly selected cohort of 100 antiretroviral-naive patients with a mean baseline CD4+ T-cell count of 175 cells/mm³ treated with lopinavir/ritonavir was selected from Abbott study M97-720. Parameter estimates from this cohort were used to populate both a Markov and a DES model, and the long-term estimates for these cohorts were compared. The models were then modified using the relative risk of undetectable viral load as reported for atazanavir and lopinavir/ritonavir in the published BMS 008 study. This allowed us to compare the mean cost effectiveness of the models. The clinical outcomes included mean change in CD4+ T-cell count, and proportion of subjects with plasma HIV-1 RNA (viral load [VL]) <50 copies/mL, VL 50–400 copies/mL and VL >400 copies/mL. US wholesale acquisition costs (year 2007 values) were used in the mean cost-effectiveness analysis, and the cost and QALY data were discounted at 3%.

Results: The results show a slight predictive advantage of the DES model for clinical outcomes. The DES model could capture direct input of CD4+ T-cell count, and proportion of subjects with plasma HIV-1 RNA VL <50 copies/mL, VL 50–00 copies/mL and VL >400 copies/mL over a 48-week period, which the Markov model could not. The DES and Markov model estimates were similar to the actual clinical trial estimates for 1-year clinical results; however, the DES model predicted more detailed outcomes and had slightly better long-term (5-year) predictive validity than the Markov model. Similar cost estimates were derived from the Markov model and the DES. Both models predict cost savings at 5 and 10 years, and over a lifetime for the lopinavir/ritonavir treatment regimen as compared with an atazanavir regimen.

Conclusion: The DES model predicts the course of a disease naturally, with few restrictions. Thismay give themodel superior face validity with decision makers. Furthermore, this model automatically provides a probabilistic sensitivity analysis, which is cumbersome to perform with a Markov model. DES models allow inclusion of more variables without aggregation, which may improve model precision. The capacity of DES for additional data capture helps explain why this model consistently predicts better survival and thus greater savings than the Markov model. The DES model is better than the Markov model in isolating long-term implications of small but important differences in crucial input data.