Biostatistics Research Group, Department of Health Sciences, University of Leicester, University Road, Leicester, UK (SHT, KRA, SB).
Division of Clinical Trials and Epidemiological Sciences, National Cancer Centre Singapore, Singapore (SHT).
Med Decis Making. 2018 Oct;38(7):834-848. doi: 10.1177/0272989X18788537. Epub 2018 Aug 13.
In health technology assessment, decisions are based on complex cost-effectiveness models that require numerous input parameters. When not all relevant estimates are available, the model may have to be simplified. Multiparameter evidence synthesis combines data from diverse sources of evidence, which results in obtaining estimates required in clinical decision making that otherwise may not be available. We demonstrate how bivariate meta-analysis can be used to predict an unreported estimate of a treatment effect enabling implementation of a multistate Markov model, which otherwise needs to be simplified. To illustrate this, we used an example of cost-effectiveness analysis for docetaxel in combination with prednisolone in metastatic hormone-refractory prostate cancer. Bivariate meta-analysis was used to model jointly available data on treatment effects on overall survival and progression-free survival (PFS) to predict the unreported effect on PFS in a study evaluating docetaxel with prednisolone. The predicted treatment effect on PFS enabled implementation of a 3-state Markov model comprising stable disease, progressive disease, and dead states, while lack of the estimate restricted the model to a 2-state model (with alive and dead states). The 2-state and 3-state models were compared by calculating the incremental cost-effectiveness ratio (which was much lower in the 3-state model: £22,148 per quality-adjusted life year gained compared to £30,026 obtained from the 2-state model) and the expected value of perfect information (which increased with the 3-state model). The 3-state model has the advantage of distinguishing surviving patients who progressed from those who did not progress. Hence, the use of advanced meta-analytic techniques allowed obtaining relevant parameter estimates to populate a model describing disease pathway in more detail while helping to prevent valuable clinical data from being discarded.
在健康技术评估中，决策是基于需要大量输入参数的复杂成本效益模型做出的。当并非所有相关估计都可用时，模型可能需要简化。多参数证据综合结合了来自不同证据来源的数据，从而获得了在临床决策中所需的估计值，否则这些估计值可能无法获得。我们展示了如何使用双变量荟萃分析来预测未报告的治疗效果估计值，从而能够实施多状态马尔可夫模型，否则需要对其进行简化。为了说明这一点，我们使用了多西他赛联合泼尼松龙治疗转移性激素难治性前列腺癌的成本效益分析示例。双变量荟萃分析用于对总生存期和无进展生存期（PFS）的可用治疗效果数据进行联合建模，以预测评估多西他赛联合泼尼松龙的研究中未报告的 PFS 治疗效果。预测的 PFS 治疗效果使实施包含稳定疾病、进展疾病和死亡状态的 3 状态马尔可夫模型成为可能，而缺乏估计值则将模型限制为 2 状态模型（具有存活和死亡状态）。通过计算增量成本效益比（在 3 状态模型中要低得多：每获得 1 个质量调整生命年的增量成本效益比为 22148 英镑，而从 2 状态模型中获得的为 30026 英镑）和完全信息的预期值（随着 3 状态模型而增加）比较了 2 状态和 3 状态模型。3 状态模型的优势在于能够区分进展和未进展的存活患者。因此，使用先进的荟萃分析技术可以获得相关参数估计值，更详细地描述疾病途径模型，同时有助于防止有价值的临床数据被丢弃。
