Abstract：
Warfarin\'s complex dosing is a significant barrier to measurement of its exposure in observational studies using population databases. Using population-based administrative data (1996-2019) from British Columbia, Canada, we developed a method based on statistical modeling (Random Effects Warfarin Days\' Supply (REWarDS)) that involves fitting a random-effects linear regression model to patients\' cumulative dosage over time for estimation of warfarin exposure. Model parameters included a minimal universally available set of variables from prescription records for estimation of patients\' individualized average daily doses of warfarin. REWarDS estimates were validated against a reference standard (manual calculation of the daily dose using the free-text administration instructions entered by the dispensing pharmacist) and compared with alternative methods (fixed window, fixed tablet, defined daily dose, and reverse wait time distribution) using Pearson\'s correlation coefficient (r), the intraclass correlation coefficient, and the root mean squared error. REWarDS-estimated days\' supply showed strong correlation and agreement with the reference standard (r = 0.90 (95% confidence interval (CI): 0.90, 0.90); intraclass correlation coefficient = 0.95 (95% CI: 0.94, 0.95); root mean squared error = 8.24 days) and performed better than all of the alternative methods. REWarDS-estimated days\' supply was valid and more accurate than estimates from all other available methods. REWarDS is expected to confer optimal precision in studies measuring warfarin exposure using administrative data.
摘要：
华法林的复杂剂量是在使用人群数据库进行观察性研究中测量其暴露量的重要障碍。使用不列颠哥伦比亚省基于人口的行政数据(1996-2019年)，加拿大,我们开发了一种基于统计模型(随机效应华法林日供应(REWartharinDays))的方法,该方法涉及将随机效应线性回归模型拟合至患者随时间累积剂量,以估计华法林暴露量.模型参数包括来自处方记录的最小通用变量集，用于估计患者个性化平均每日华法林剂量。REWarDS估计值根据参考标准（使用配药药剂师输入的自由文本给药说明手动计算每日剂量）进行验证，并与替代方法（固定窗口，固定平板电脑，定义的每日剂量，和反向等待时间分布)使用皮尔逊相关系数(r)，组内相关系数，和均方根误差。REWarDS估计的供应天数与参考标准具有很强的相关性和一致性（r=0.90（95％置信区间（CI）：0.90，0.90）；组内相关系数=0.95（95％CI：0.94，0.95）；均方根误差=8.24天），并且优于所有替代方法。REWards-估计的供应天数是有效的，比所有其他可用方法的估计更准确。REWards有望在使用管理数据测量华法林暴露的研究中赋予最佳精度。
