Nakasian Sonja S, Rassen Jeremy A, Franklin Jessica M
Division of Pharmacoepidemiology & Pharmacoeconomics, Department of Medicine, Brigham & Women's Hospital and Harvard Medical School, Boston, MA, USA.
Aetion, Inc., New York, NY, USA.
Pharmacoepidemiol Drug Saf. 2017 Aug;26(8):890-899. doi: 10.1002/pds.4210. Epub 2017 Apr 11.
A fixed baseline period has been a common covariate assessment approach in pharmacoepidemiological studies from claims but may lead to high levels of covariate misclassification. Simulation studies have recommended expanding the look-back approach to all available data (AAD) for binary indicators of diagnoses, procedures, and medications, but there have been few real data analyses using this approach.
The objective of the study is to explore the impact on treatment effect estimates and covariate prevalence of expanding the look-back period within five validated studies in the Aetion system, a rapid cycle analytics platform.
We reran the five studies and assessed covariates using (i) a fixed window approach (usually 180 days before treatment initiation), (ii) AAD prior to treatment initiation, and (iii) AAD with a categorized by recency approach, where the most recent occurrence of a covariate was labeled as recent (occurring within the fixed window) or past (before the start of the fixed window). For each covariate assessment approach, we adjusted for covariates via propensity score matching.
All studies had at least one covariate that had an increase in prevalence of 15% or higher from the fixed window to the AAD approach. However, there was little change in treatment effect estimates resulting from differing covariate assessment approaches. For example, in a study of acute coronary syndrome in high-intensity versus low-intensity statin users, the estimated hazard ratio from the fixed window approach was 1.11 (95% confidence interval 0.98, 1.25) versus 1.21 (1.07, 1.37) when using AAD and 1.19 (1.05, 1.35) using categorized by recency.
Expanding the baseline period to AAD improved covariate sensitivity by capturing data that would otherwise be missed yet did not meaningfully change the overall treatment effect estimates compared with the fixed window approach. Copyright © 2017 John Wiley & Sons, Ltd.
在基于索赔数据的药物流行病学研究中，固定基线期一直是一种常用的协变量评估方法，但可能导致较高程度的协变量错误分类。模拟研究建议将回顾期扩展至所有可用数据（AAD），用于诊断、手术和药物的二元指标，但很少有实际数据分析采用这种方法。
本研究的目的是在快速循环分析平台Aetion系统的五项验证研究中，探讨扩展回顾期对治疗效果估计和协变量患病率的影响。
我们重新运行了这五项研究，并使用以下方法评估协变量：（i）固定窗口法（通常在治疗开始前180天），（ii）治疗开始前的AAD，以及（iii）按近期分类的AAD，其中协变量的最近一次出现被标记为近期（在固定窗口内发生）或过去（在固定窗口开始之前）。对于每种协变量评估方法，我们通过倾向得分匹配对协变量进行调整。
所有研究中至少有一个协变量，其患病率从固定窗口法到AAD法增加了15%或更高。然而，不同的协变量评估方法对治疗效果估计的影响很小。例如，在一项高强度与低强度他汀类药物使用者急性冠状动脉综合征的研究中，固定窗口法估计的风险比为1.11（95%置信区间0.98，1.25），而使用AAD时为1.21（1.07，1.37），按近期分类时为1.19（1.05，1.35）。
与固定窗口法相比，将基线期扩展至AAD通过捕获否则会遗漏的数据提高了协变量敏感性，但并未显著改变总体治疗效果估计。版权所有©2017约翰威立父子有限公司。
