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An overview of mixture modelling for latent evolutions in longitudinal data: Modelling approaches, fit statistics and software.纵向数据中潜在演变的混合建模概述:建模方法、拟合统计量与软件
Adv Life Course Res. 2020 Mar;43:100323. doi: 10.1016/j.alcr.2019.100323. Epub 2020 Jan 25.
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Pharmacoepidemiol Drug Saf. 2020 Sep;29(9):1111-1119. doi: 10.1002/pds.5089. Epub 2020 Aug 3.
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Chronic opioid use in women following hysterectomy: Patterns and predictors.女性子宫切除术后慢性阿片类药物使用:模式和预测因素。
Pharmacoepidemiol Drug Saf. 2020 Apr;29(4):493-503. doi: 10.1002/pds.4972. Epub 2020 Feb 26.
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Adherence trajectories of adjuvant endocrine therapy in the five years after its initiation among women with non-metastatic breast cancer: a cohort study using administrative databases.辅助内分泌治疗在非转移性乳腺癌女性起始后五年的依从轨迹:使用行政数据库的队列研究。
Breast Cancer Res Treat. 2020 Apr;180(3):777-790. doi: 10.1007/s10549-020-05549-x. Epub 2020 Feb 21.
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6
Opening the black box of the group-based trajectory modeling process to analyze medication adherence patterns: An example using real-world statin adherence data.打开基于群组的轨迹建模过程的“黑箱”,以分析药物依从性模式:使用真实世界的他汀类药物依从性数据示例。
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7
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Clin Pharmacol Ther. 2019 Sep;106(3):616-622. doi: 10.1002/cpt.1430. Epub 2019 May 10.
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Using Group-based Trajectory Models and Propensity Score Weighting to Detect Heterogeneous Treatment Effects: The Case Study of Generic Hormonal Therapy for Women With Breast Cancer.基于群组的轨迹模型和倾向评分加权法检测异质治疗效果:以乳腺癌女性接受通用激素治疗为例。
Med Care. 2019 Jan;57(1):85-93. doi: 10.1097/MLR.0000000000001019.

基于他汀类药物用药依从性数据比较连续和二进制群组轨迹建模。

Comparing Continuous and Binary Group-based Trajectory Modeling Using Statin Medication Adherence Data.

机构信息

Division of Pharmaceutical Outcomes and Policy, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill.

Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC.

出版信息

Med Care. 2021 Nov 1;59(11):997-1005. doi: 10.1097/MLR.0000000000001625.

DOI:10.1097/MLR.0000000000001625
PMID:34644285
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8525904/
Abstract

BACKGROUND

Of 58 medication adherence group-based trajectory modeling (GBTM) published studies, 74% used binary and 26% used continuous GBTM. Few studies provided a rationale for this choice. No medication adherence studies have compared continuous and binary GBTM.

OBJECTIVE

The objective of this study was to assess whether continuous versus binary GBTM: (1) impacts adherence trajectory shapes; and (2) results in the differential classification of patients into adherence groups.

METHODS

Patients were prevalent statin users with myocardial infarction hospitalization, 66+ years old, and continuously enrolled in fee-for-service Medicare. Statin medication adherence was measured 6 months prehospitalization using administrative claims. Final GBTM specifications beyond default settings were selected using a previously defined standardized procedure and applied separately to continuous and binary (proportion of days covered ≥0.80) medication adherence measures. Assignment to adherence groups was compared between continuous and binary models using percent agreement of patient classification and the κ coefficient.

RESULTS

Among 113,296 prevalent statin users, 4 adherence groups were identified in both models. Three groups were consistent: persistently adherent, progressively nonadherent, and persistently nonadherent. The fourth continuous group was moderately adherent (progressively adherent in the binary model). When comparing patient assignment into adherence groups between continuous and binary trajectory models, only 78.4% of patients were categorized into comparable groups (κ=0.641; 95% confidence interval: 0.638-0.645). The agreement was highest in the persistently adherent group (∼94%).

CONCLUSIONS

Continuous and binary trajectory models are conceptually different measures of medication adherence. The choice between these approaches should be guided by study objectives and the role of medication adherence within the study-exposure, outcome, or confounder.

摘要

背景

在 58 项基于药物依从性的轨迹建模(GBTM)已发表研究中,74%使用二进制,26%使用连续 GBTM。很少有研究为此选择提供理论依据。没有药物依从性研究比较过连续和二进制 GBTM。

目的

本研究旨在评估连续与二进制 GBTM 是否存在以下差异:(1)对依从轨迹形状的影响;(2)对患者分类到不同依从组的结果。

方法

患者为有心肌梗死住院史的老年(≥66 岁)持续性他汀类药物使用者,且连续参加了按服务收费的医疗保险。使用行政索赔数据测量患者在住院前 6 个月的他汀类药物依从性。采用先前定义的标准化程序选择超出默认设置的最终 GBTM 规范,并分别应用于连续和二进制(覆盖天数比例≥0.80)药物依从性测量值。通过患者分类的百分比一致性和 κ 系数比较连续和二进制模型对依从组的分配。

结果

在 113296 名有他汀类药物使用史的患者中,两种模型均确定了 4 个依从组。其中 3 个组一致:持续依从、逐渐不依从和持续不依从。第四个连续组为中度依从(在二进制模型中为逐渐依从)。当比较连续和二进制轨迹模型中患者分配到依从组的情况时,只有 78.4%的患者被归入可比组(κ=0.641;95%置信区间:0.638-0.645)。在持续依从组中,一致性最高(接近 94%)。

结论

连续和二进制轨迹模型是药物依从性的两种不同概念性度量方法。在选择这些方法时,应根据研究目标以及药物依从性在研究中的作用(暴露、结局或混杂因素)来指导。