Causal inference in perioperative medicine observational research: part 2, advanced methods

被引:12
作者
Krishnamoorthy, Vijay [1 ]
McLean, Duncan [2 ]
Ohnuma, Tetsu [1 ]
Harris, Steve K. [3 ]
Wong, Danny J. N. [4 ]
Wilson, Matt [3 ]
Moonesinghe, Ramani [3 ]
Raghunathan, Karthik [1 ]
机构
[1] Duke Univ Hosp, Dept Anesthesiol, Crit Care & Perioperat Epidemiol Res Caper Unit, Durham, NC 27710 USA
[2] Univ N Carolina, Dept Anesthesiol, Chapel Hill, NC 27515 USA
[3] Univ Coll London Hosp NHS Fdn Trust, Crit Care, London, England
[4] Guys & St Thomas NHS Fdn Trust, Dept Anaesthesia, London, England
关键词
causal inference; confounding; epidemiology; joint effects; mediation analysis; natural experiment; observational research; MEDIATION ANALYSIS; SENSITIVITY-ANALYSIS; IDENTIFICATION; SYNERGISM; BARON; BIAS;
D O I
10.1016/j.bja.2020.03.032
中图分类号
R614 [麻醉学];
学科分类号
100217 ;
摘要
Although RCTs represent the gold standard in clinical research, most clinical questions cannot be answered using this technique, because of ethical considerations, time, and cost. The goal of observational research in clinical medicine is to gain insight into the relationship between a clinical exposure and patient outcome, in the absence of evidence from RCTs. Observational research offers additional benefit when compared with data from RCTs: the conclusions are often more generalisable to a heterogenous population, which may be of greater value to everyday clinical practice. In Part 2 of this methods series, we will introduce the reader to several advanced methods for supporting the case for causality between an exposure and outcome, including: mediation analysis, natural experiments, and joint effects methods.
引用
收藏
页码:398 / 405
页数:8
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