Center for the Evaluation of Value and Risk in Health, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA.
Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA.
Med Decis Making. 2018 May;38(4):487-494. doi: 10.1177/0272989X17746989. Epub 2018 Jan 19.
Cost-effectiveness analysis (CEA) estimates can vary substantially across patient subgroups when patient characteristics influence preferences, outcome risks, treatment effectiveness, life expectancy, or associated costs. However, no systematic review has reported the frequency of subgroup analysis in CEA, what type of heterogeneity they address, and how often heterogeneity influences whether cost-effectiveness ratios exceed or fall below conventional thresholds.
We reviewed the CEA literature cataloged in the Tufts Medical Center CEA Registry, a repository describing cost-utility analyses published through 2016. After randomly selecting 200 of 642 articles published in 2014, we ascertained whether each study reported subgroup results and collected data on the defining characteristics of these subgroups. We identified whether any of the CEA subgroup results crossed conventional cost-effectiveness benchmarks (e.g., $100,000 per QALY) and compared characteristics of studies with and without subgroup-specific findings.
Thirty-eight studies (19%) reported patient subgroup results. Articles reporting subgroup analyses were more likely to be US-based, government funded (v. drug industry- or nonprofit foundation-funded) studies, with a focus on primary or secondary (v. tertiary) prevention (P < 0.05 for comparisons). One or more patient characteristics were used to stratify CEA results 68 times within the 38 studies, with most stratifications using one characteristic (n = 47), most commonly age (n = 35). Among the 23 stratifications reported alongside average ratios in US studies, 13 produced subgroup ratios that crossed a conventional CEA ratio benchmark.
Most CEAs do not report any subgroup results, and those that do most often stratify only by patient age. Over half of the subgroup analyses reported could lead to different value-based decision making for at least some patients.
当患者特征影响偏好、结局风险、治疗效果、预期寿命或相关成本时,成本效益分析(CEA)的估计值可能会在患者亚组之间有很大差异。然而,尚无系统评价报告 CEA 中亚组分析的频率、它们解决的异质性类型以及异质性对成本效益比是否超过或低于常规阈值的影响程度。
我们查阅了 Tufts 医疗中心 CEA 注册中心收录的 CEA 文献,该数据库描述了截至 2016 年发表的成本效用分析。在随机选择了 2014 年发表的 642 篇文章中的 200 篇后,我们确定了每项研究是否报告了亚组结果,并收集了这些亚组特征的相关数据。我们确定了任何 CEA 亚组结果是否跨越了常规的成本效益基准(例如,每 QALY 100,000 美元),并比较了有和没有亚组特定发现的研究的特征。
38 项研究(19%)报告了患者亚组结果。报告亚组分析的文章更有可能是基于美国的、由政府资助的(而非制药行业或非营利基金会资助)研究,重点是初级或二级(而非三级)预防(与比较)。在 38 项研究中,有 68 次使用一个或多个患者特征对 CEA 结果进行分层,其中大多数分层使用一个特征(n = 47),最常见的是年龄(n = 35)。在 23 项在美国研究中报告的与平均比率并列的分层中,有 13 项产生了跨越常规 CEA 比率基准的亚组比率。
大多数 CEA 没有报告任何亚组结果,而且那些有报告的亚组通常只按患者年龄分层。报告的亚组分析中有一半以上可能导致至少对某些患者的基于价值的决策不同。