Health Economics and Decision Science, School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, South Yorkshire, UK.
Centre for Health Economics & Medicines Evaluation (CHEME), Bangor University, Bangor, Gwynedd, UK.
Med Decis Making. 2019 Nov;39(8):910-925. doi: 10.1177/0272989X19881654. Epub 2019 Oct 24.
Medication nonadherence can have a significant negative impact on treatment effectiveness. Standard intention-to-treat analyses conducted alongside clinical trials do not make adjustments for nonadherence. Several methods have been developed that attempt to estimate what treatment effectiveness would have been in the absence of nonadherence. However, health technology assessment (HTA) needs to consider effectiveness under real-world conditions, where nonadherence levels typically differ from those observed in trials. With this analytical requirement in mind, we conducted a review to identify methods for adjusting estimates of treatment effectiveness in the presence of patient nonadherence to assess their suitability for use in HTA. A "Comprehensive Pearl Growing" technique, with citation searching and reference checking, was applied across 7 electronic databases to identify methodological papers for adjusting time-to-event outcomes for nonadherence using individual patient data. A narrative synthesis of identified methods was conducted. Methods were assessed in terms of their ability to reestimate effectiveness based on alternative, suboptimal adherence levels. Twenty relevant methodological papers covering 12 methods and 8 extensions to those methods were identified. Methods are broadly classified into 4 groups: 1) simple methods, 2) principal stratification methods, 3) generalized methods (g-methods), and 4) pharmacometrics-based methods using pharmacokinetics and pharmacodynamics (PKPD) analysis. Each method makes specific assumptions and has associated limitations. Five of the 12 methods are capable of adjusting for real-world nonadherence, with only g-methods and PKPD considered appropriate for HTA. A range of statistical methods is available for adjusting estimates of treatment effectiveness for nonadherence, but most are not suitable for use in HTA. G-methods and PKPD appear to be more appropriate to estimate effectiveness in the presence of real-world adherence.
药物依从性差会对治疗效果产生重大负面影响。与临床试验同时进行的标准意向治疗分析并未对不依从性进行调整。已经开发了几种方法来尝试估计在没有不依从性的情况下治疗效果会如何。然而，卫生技术评估（HTA）需要考虑在现实世界条件下的有效性，在现实世界中，不依从率通常与临床试验中观察到的不同。考虑到这一分析要求，我们进行了一项综述，以确定在存在患者不依从的情况下调整治疗效果估计的方法，以评估其在 HTA 中的适用性。采用“综合珍珠生成”技术，结合引文搜索和参考文献检查，在 7 个电子数据库中进行了检索，以确定使用个体患者数据调整时间事件结局的不依从性调整治疗效果的方法学论文。对确定的方法进行了叙述性综合。根据替代的、次优的依从水平，评估方法重新估计有效性的能力。确定了 20 篇相关的方法学论文，涵盖了 12 种方法和 8 种这些方法的扩展。方法大致分为 4 组：1）简单方法，2）主要分层方法，3）广义方法（g 方法），以及 4）基于药代动力学和药效学（PKPD）分析的药物代谢动力学方法。每种方法都有特定的假设和相关的局限性。12 种方法中有 5 种能够调整现实世界的不依从性，只有 g 方法和 PKPD 被认为适合 HTA。有一系列统计方法可用于调整不依从性治疗效果的估计值，但大多数不适合 HTA 使用。g 方法和 PKPD 似乎更适合估计现实世界依从性存在时的效果。
