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

Abstract

The DES model predicts the course of a disease naturally, with few restrictions. This may give the model 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.

Do different decision-analytic modeling approaches produce different results? A systematic review of cross-validation studies

“…All studies compared two modeling approaches. Five were in the realm of infectious diseases [10][11][12][13][14]; two in the area of breast cancer [15,16] and the remaining two on a hypothetical disease [17,18]: one exploring a communicable disease and the other on a non-communicable disease (TABLE 3).…”

“…In the studies on infectious diseases, the structural decision criteria of interactivity (dynamic vs static models) [10,12,18,14] and population resolution (aggregate/cohort-vs individual-level models) [11,13] were explored using a variety of modeling approaches. All three cases of non-communicable diseases compared DES and Markov models, with one each focused on the features of population resolution [16], time advancement mechanism [17] and resource constraints [15].…”

Economics of Switching to Second-Line Antiretroviral Therapy with Lopinavir/Ritonavir in Africa: Estimates Based on DART Trial Results and Costs for Uganda and Kenya

“…The structure of the model is depicted in Figure 1. This structure is a simplification of a previously developed model used in resource-rich countries that included viral load levels in the model health state definition [28]. The health states are defined as 1) more than 500 CD4 ϩ T cells; 2) 350 to 500 CD4 ϩ T cells; 3) 200 to 349 CD4 ϩ T cells; 4) 50 to 199 CD4 ϩ T cells; 5) fewer than 50 CD4 ϩ T cells; 6) death from AIDS or infection; 7) death from malaria.…”

The Application of Operations Research Methodologies to the Delivery of Care Model for Traumatic Spinal Cord Injury: The Access to Care and Timing Project

“…Although these modeling techniques have not been applied to the entire continuum of SCI health care, previous applications of simulation modeling in health care include examining patient flow within a single phase of care, such as in emergency departments (Hoot et al, 2008(Hoot et al, ,2009Hung et al, 2007;Khare et al, 2009), intensive care units (Kolker, 2009), and outpatient clinics (Chand et al, 2009), and has been valuable in describing the course of disease (e.g., HIV; Simpson et al, 2009).…”