Comparison of Markov Model and Discrete-Event Simulation Techniques for HIV
Comparison of Markov Model and Discrete-Event Simulation Techniques for HIV
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DOI：
10.2165/00019053-200927020-00006
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发表时间：
2009-01-01
影响因子：
4.4
通讯作者：
Rajagopalan, Rukmini
中科院分区：
文献类型：
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作者：
Simpson, Kit N.;Strassburger, Alvin;Rajagopalan, Rukmini
背景资料：马尔可夫模型已经成为使用临床试验的替代标志终点预测长期临床和经济结局的标准框架。然而，这些问题很复杂，需要大量数据，决策者往往难以理解。建模软件的最新发展使得使用离散事件模拟（DES）来模拟艾滋病毒的结果成为可能。使用已发表的48周试验数据结果作为模型输入，比较马尔可夫模型和DES模型方法在5年临床结局和终生成本效益estimate.Methods：从Abbott研究M97-720中随机选择100例接受洛匹那韦/利托那韦治疗的平均基线CD 4 + T细胞计数为175个/mm 3的抗逆转录病毒初治患者。使用该队列的参数估计值填充马尔可夫模型和DES模型，并比较这些队列的长期估计值。然后使用已发表的BMS 008研究中报告的阿扎那韦和洛匹那韦/利托那韦的不可检测病毒载量的相对风险来修改模型。这使我们能够比较模型的平均成本效益。临床结局包括CD 4 + T细胞计数的平均变化和血浆HIV-1 RNA（病毒载量[VL]）400拷贝/mL的受试者比例。以美国2007年的批发采购成本作为平均成本-效果分析的依据，成本和QALY数据均折现3%。DES模型可以捕获CD 4 + T细胞计数的直接输入，以及48周内血浆HIV-1 RNA VL 400拷贝/mL的受试者比例，而Markov模型不能。DES和Markov模型估计值与1年临床结果的实际临床试验估计值相似;然而，DES模型预测的结局更详细，长期（5年）预测有效性略优于Markov模型。类似的成本估计来自马尔可夫模型和DES。这两个模型预测成本节省在5年和10年，并在一生中的洛匹那韦/利托那韦治疗方案相比，阿扎那韦regiment.Conclusion：DES模型预测疾病的过程中自然，几乎没有限制。这可能会给决策者的模型上级表面有效性。此外，该模型自动提供概率敏感性分析，这是繁琐的马尔可夫模型执行。DES模型允许包含更多的变量而无需聚合，这可以提高模型精度。DES捕获额外数据的能力有助于解释为什么该模型始终预测更好的生存率，从而比马尔可夫模型节省更多。DES模型在隔离关键输入数据中微小但重要的差异的长期影响方面优于马尔可夫模型。
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 anti retroviral-naive patients with a mean baseline CD4+ T-cell count of 175cells/mm(3) 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]) 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 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. 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.