| 黄丽红,魏永越,陈峰.如何控制观察性疗效比较研究中的混杂因素：（二）未知或未测量混杂因素的统计学分析方法[J].Chinese journal of Epidemiology,2019,40(11):1450-1455 |
| 如何控制观察性疗效比较研究中的混杂因素：（二）未知或未测量混杂因素的统计学分析方法 |
| Confounder adjustment in observational comparative effectiveness researches: (2)statistical adjustment approaches for unmeasured confounders |
| Received:March 18, 2019 |
| DOI：10.3760/cma.j.issn.0254-6450.2019.11.020 |
| KeyWord: 观察性疗效比较研究 现实世界研究 未测量混杂 控制 统计方法 |
| English Key Word: Observational study of therapy efficacy comparison Real world study Unmeasured confounder Adjustment Statistical method |
| FundProject:国家自然科学基金青年科学基金（81903407） |
| Author Name | Affiliation | E-mail | | Huang Lihong | Department of Biostatistics, Zhongshan Hospital, Fudan University, Shanghai 200032, China | huang.lihong@zs-hospital.sh.cn | | Wei Yongyue | Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China | | | Chen Feng | Department of Biostatistics, School of Public Health, Nanjing Medical University, Nanjing 211166, China | |
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| Abstract: |
| 观察性疗效比较研究作为随机对照研究的证据补充，其应用价值越来越受到关注。未测量混杂因素的统计学分析方法是观察性疗效比较研究中的重大挑战，本文对观察性疗效比较研究中未知或未测量的混杂因素控制的统计分析方法进行述评。未测量混杂因素的统计学方法包括工具变量法、本底事件率比校正法和双重差分模型及其衍生方法。工具变量法模型构造巧妙，但满足条件的工具变量在实际研究中并不易得；本底事件率比校正法和双重差分模型均要求研究数据有干预前信息，有些实际研究中往往无法满足。未测量混杂因素对统计学方法提出了新的要求、新的挑战，有待国内外统计学者的进一步完善和研究。 |
| English Abstract: |
| Observational study of therapy efficacy comparison has been widely conducted to provide the additional efficacy evidence to support randomized control study. Statistical adjustment for unmeasured confounders is a major challenge in observational study of therapy efficacy comparison. This paper summarizes and evaluates the relative statistical methods. Currently, the most commonly used methods include instrumental variable, difference-in-differences (DiD) model and prior event rate ratio (PERR) adjustment. The instrumental variable method skill fully escapes unmeasured confounders through model structure, but it is not easy to obtain satisfied instrumental variables. Both PERR and DiD require the data prior to exposure which are not always collected in observational studies. Unmeasured confounders could result in new requirements and pose new challenges for statistical methods, which needs further study and improvement. |
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