Simpson Kit N, Strassburger Alvin, Jones Walter J, Dietz Birgitta, Rajagopalan Rukmini
Department of Health Administration and Policy, College of Health Professions, Medical University of South Carolina, Charleston, SC 29425, USA.
Pharmacoeconomics. 2009;27(2):159-65. doi: 10.2165/00019053-200927020-00006.
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.
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%.
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 50-400 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.
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.
马尔可夫模型一直是使用临床试验中的替代标志物终点来预测长期临床和经济结果的标准框架。然而，它们很复杂，对数据要求很高，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）对HIV结果进行建模成为可能。以48周试验数据的已发表结果作为模型输入，比较了马尔可夫模型和DES建模方法在5年临床结果和终身成本效益估计方面的差异。
从雅培研究M97 - 720中随机选取100名未接受过抗逆转录病毒治疗、平均基线CD4 + T细胞计数为175个细胞/mm³的患者队列。该队列的参数估计值用于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。然后根据已发表的百时美施贵宝008研究中报道的阿扎那韦和洛匹那韦/利托那韦不可检测病毒载量的相对风险对模型进行修改。这使我们能够比较模型的平均成本效益。临床结果包括CD4 + T细胞计数的平均变化，以及血浆HIV - 1 RNA（病毒载量[VL]）<50拷贝/mL、VL 50 - 400拷贝/mL和VL>400拷贝/mL的受试者比例。平均成本效益分析中使用了美国批发采购成本（2007年值），成本和质量调整生命年（QALY）数据按3%进行贴现。
结果显示DES模型在临床结果预测方面略有优势。DES模型能够捕捉48周期间CD4 + T细胞计数的直接输入，以及血浆HIV - 1 RNA VL <50拷贝/mL、VL 50 - 400拷贝/mL和VL>400拷贝/mL的受试者比例，而马尔可夫模型则无法做到。DES模型和马尔可夫模型的估计值与1年临床结果的实际临床试验估计值相似；然而，DES模型预测的结果更详细，并且在长期（5年）预测有效性方面略优于马尔可夫模型。马尔可夫模型和DES得出了相似的成本估计值。与阿扎那韦治疗方案相比，两种模型均预测洛匹那韦/利托那韦治疗方案在5年和10年以及终身可节省成本。
DES模型对疾病进程的预测自然，限制较少。这可能使该模型在决策者眼中具有更高的表面效度。此外，该模型自动提供概率敏感性分析，而马尔可夫模型进行此分析则很繁琐。DES模型允许纳入更多变量而无需汇总，这可能提高模型精度。DES能够捕获更多数据，这有助于解释为什么该模型始终比马尔可夫模型预测出更好的生存率，从而节省更多成本。在区分关键输入数据中虽小但重要的差异所产生的长期影响方面，DES模型优于马尔可夫模型。
