Causal Analyses of Electronic Health Record Data for Assessing the Comparative Effectiveness of Treatment Regimens [Methods Study], United States, 2014-2019

Patients with chronic health problems, such as diabetes, often need to change treatment plans over time to improve their health. To help with this process, doctors can monitor patients' health through follow-up clinic visits and lab tests. Doctors may also suggest changing a treatment plan in response to visits or lab test results. When a treatment plan changes in this way, it's called a dynamic treatment plan.
In this study, the research team developed and tested new statistical methods to learn how dynamic treatment plans and choices about follow-up care affect patients' health. These methods use electronic health records, or EHRs. Using EHRs is helpful because they have data on

What treatments patients have received over time
How treatments have affected patients' health
Follow-up information such as lab test results

But the data may differ for patients based on when and why they go to the doctor. These differences make it hard for researchers to accurately know the effect of dynamic treatment plans across many patients.
To access the methods and software, please visit the simcasual R Package.

罹患糖尿病等慢性疾病的患者，通常需随病程调整治疗方案以优化健康状态。为助力此类临床管理，医生可通过随访门诊及实验室检测监测患者健康状况。医生亦可根据随访结果或实验室检测指标建议调整治疗方案，此类动态调整的治疗方案被称为动态治疗方案（dynamic treatment plan）。
本研究中，研究团队开发并验证了新型统计方法，旨在探究动态治疗方案与随访医疗选择对患者健康的影响。此类方法依托电子健康记录（Electronic Health Records，以下简称EHRs）开展。使用EHRs具备显著优势，因其涵盖以下三类核心数据：
1. 患者随时间接受的治疗方案详情
2. 治疗方案对患者健康的实际影响
3. 实验室检测结果等随访相关信息
但不同患者因就诊时机与就诊动因的差异，其数据分布亦存在异质性。这类异质性会导致研究人员难以精准评估动态治疗方案在全体患者群体中的整体效果。
若需获取本研究的方法与配套软件，请访问simcasual R包。