Li Yunfeng, Zhou Huanxue, Cai Beilei, Kahler Kristijan H, Tian Haijun, Gabriel Susan, Arcona Steve
Novartis Pharmaceuticals Corporation, East Hanover, NJ, USA.
KMK Consulting Inc., Florham Park, NJ, USA.
Clinicoecon Outcomes Res. 2014 Apr 10;6:197-208. doi: 10.2147/CEOR.S59339. eCollection 2014.
Proportion of days covered (PDC), a commonly used adherence metric, does not provide information about the longitudinal course of adherence to treatment over time. Group-based trajectory model (GBTM) is an alternative method that overcomes this limitation.
The statistical principles of GBTM and PDC were applied to assess adherence during a 12-month follow-up in psoriasis patients starting treatment with a biologic. The optimal GBTM model was determined on the basis of the balance between each model's Bayesian information criterion and the percentage of patients in the smallest group in each model. Variables potentially predictive of adherence were evaluated.
In all, 3,249 patients were included in the analysis. Four GBTM adherence groups were suggested by the optimal model, and patients were categorized as demonstrating continuously high adherence, high-then-low adherence, moderate-then-low adherence, or consistently moderate adherence during follow-up. For comparison, four PDC groups were constructed: PDC Group 4 (PDC ≥75%), PDC Group 3 (25%≤ PDC <50%), PDC Group 2 (PDC <25%), and PDC Group 1 (50%≤ PDC <75%). Our findings suggest that the majority of patients (97.9%) from PDC Group 2 demonstrated moderate-then-low adherence, whereas 96.4% of patients from PDC Group 4 showed continuously high adherence. The remaining PDC-based categorizations did not capture patients with uniform adherence behavior based on GBTM. In PDC Group 3, 25.3%, 17.2%, and 57.5% of patients exhibited GBTM-defined consistently moderate adherence, moderate-then-low adherence, or high-then-low adherence, respectively. In PDC Group 1, 70.8%, 23.6%, and 5.7% of patients had consistently moderate adherence, high-then-low adherence, and continuously high adherence, respectively. Additional analyses suggested GBTM-based categorization was best predicted by patient age, sex, certain comorbidities, and particular drug use.
GBTM is a more appropriate way to model dynamic behaviors and offers researchers an alternative to more traditional drug adherence measurements.
覆盖天数比例（PDC）是一种常用的依从性指标，无法提供随时间推移治疗依从性的纵向变化信息。基于群组的轨迹模型（GBTM）是一种克服此局限性的替代方法。
应用GBTM和PDC的统计原理，对开始使用生物制剂治疗的银屑病患者进行为期12个月的随访，以评估依从性。根据每个模型的贝叶斯信息准则与每个模型中最小组患者百分比之间的平衡，确定最佳GBTM模型。评估可能预测依从性的变量。
总共3249例患者纳入分析。最佳模型提出了四个GBTM依从性组，患者在随访期间被分类为持续高依从性、先高后低依从性、先中后低依从性或持续中度依从性。作为比较，构建了四个PDC组：PDC第4组（PDC≥75%）、PDC第3组（25%≤PDC<50%）、PDC第2组（PDC<25%）和PDC第1组（50%≤PDC<75%）。我们的研究结果表明，PDC第2组的大多数患者（97.9%）表现为先中后低依从性，而PDC第4组的96.4%患者表现为持续高依从性。其余基于PDC的分类未根据GBTM捕获具有统一依从性行为的患者。在PDC第3组中，分别有25.3%、17.2%和57.5%的患者表现出GBTM定义的持续中度依从性、先中后低依从性或先高后低依从性。在PDC第1组中，分别有70.8%、23.6%和5.7%的患者具有持续中度依从性、先高后低依从性和持续高依从性。进一步分析表明，基于GBTM的分类最能通过患者年龄、性别、某些合并症和特定药物使用情况进行预测。
GBTM是一种更合适的动态行为建模方法，为研究人员提供了一种替代更传统药物依从性测量方法的选择。
