Core Clinical Sciences Inc., Vancouver, British Columbia, Canada.
Département de médecine sociale et préventive, Université Laval, Québec, Canada.
Pharm Stat. 2024 Jul-Aug;23(4):511-529. doi: 10.1002/pst.2365. Epub 2024 Feb 8.
It is well known that medication adherence is critical to patient outcomes and can decrease patient mortality. The Pharmacy Quality Alliance (PQA) has recognized and identified medication adherence as an important indicator of medication-use quality. Hence, there is a need to use the right methods to assess medication adherence. The PQA has endorsed the proportion of days covered (PDC) as the primary method of measuring adherence. Although easy to calculate, the PDC has however several drawbacks as a method of measuring adherence. PDC is a deterministic approach that cannot capture the complexity of a dynamic phenomenon. Group-based trajectory modeling (GBTM) is increasingly proposed as an alternative to capture heterogeneity in medication adherence. The main goal of this paper is to demonstrate, through a simulation study, the ability of GBTM to capture treatment adherence when compared to its deterministic PDC analogue and to the nonparametric longitudinal K-means. A time-varying treatment was generated as a quadratic function of time, baseline, and time-varying covariates. Three trajectory models are considered combining a cat's cradle effect, and a rainbow effect. The performance of GBTM was compared to the PDC and longitudinal K-means using the absolute bias, the variance, the c-statistics, the relative bias, and the relative variance. For all explored scenarios, we find that GBTM performed better in capturing different patterns of medication adherence with lower relative bias and variance even under model misspecification than PDC and longitudinal K-means.
众所周知，用药依从性对患者的治疗结果至关重要，可以降低患者死亡率。药房质量联盟（PQA）已经认识到并确定用药依从性是用药质量的一个重要指标。因此，需要使用正确的方法来评估用药依从性。PQA 已经认可比例天数覆盖（PDC）作为衡量依从性的主要方法。虽然易于计算，但 PDC 作为衡量依从性的方法存在几个缺点。PDC 是一种确定性方法，无法捕捉动态现象的复杂性。群组轨迹建模（GBTM）越来越多地被提议作为替代方法，以捕捉用药依从性的异质性。本文的主要目标是通过模拟研究，展示 GBTM 在与确定性 PDC 类似物和非参数纵向 K-均值相比时，捕捉治疗依从性的能力。时变治疗被生成为时变、基线和时变协变量的二次函数。考虑了三种结合了猫的摇篮效应和彩虹效应的轨迹模型。使用绝对偏差、方差、C 统计量、相对偏差和相对方差比较 GBTM 与 PDC 和纵向 K-均值的性能。对于所有探索的场景，我们发现即使在模型失拟的情况下，GBTM 在捕捉不同的用药依从性模式方面表现更好，具有更低的相对偏差和方差，甚至优于 PDC 和纵向 K-均值。
