药物流行病学
医学
因果推理
观察研究
医疗保健
协议(科学)
文档
随机对照试验
临床试验
推论
数据科学
数据挖掘
计算机科学
替代医学
药理学
外科
病理
人工智能
经济增长
经济
程序设计语言
药方
作者
Thuy Nhu Thai,Almut G. Winterstein
摘要
Abstract Background Observational designs can complement evidence from randomized controlled trials not only in situations when randomization is not feasible, but also by evaluating drug effects in real‐world, considering a broader spectrum of users and clinical scenarios. However, use of such real‐world scenarios captured in routinely collected clinical or administrative data also comes with specific challenges. Unlike in trials, medication use is not protocol based. Instead, exposure is determined by a multitude of factors involving patients, providers, healthcare access, and other policies. Accurate measurement of medication exposure relies on a similar broad set of factors which, if not understood and appropriately addressed, can lead to exposure misclassification and bias. Aim To describe core considerations for measurement of medication exposure in routinely collected healthcare data. Methods We describe the strengths and weaknesses of the two main types of routinely collected healthcare data (electronic health records and administrative claims) used in pharmacoepidemiologic research. We introduce key elements in those data sources and issues in the curation process that should be considered when developing exposure definitions. We present challenges in exposure measurement such as the appropriate determination of exposure time windows or the delineation of concomitant medication use versus switching of therapy, and related implications for bias. Results We note that true exposure patterns are typically unknown when using routinely collected healthcare data and that an in‐depth understanding of healthcare delivery, patient and provider decision‐making, data documentation and governance, as well as pharmacology are needed to ensure unbiased approaches to measuring exposure. Conclusions Various assumptions are made with the goal that the chosen exposure definition can approximate true exposure. However, the possibility of exposure misclassification remains, and sensitivity analyses that can test the impact of such assumptions on the robustness of estimated medication effects are necessary to support causal inferences.
科研通智能强力驱动
Strongly Powered by AbleSci AI