A Selective Review of Negative Control Methods in Epidemiology
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EPIDEMIOLOGIC METHODS (P HOWARDS, SECTION EDITOR)
A Selective Review of Negative Control Methods in Epidemiology Xu Shi 1
&
Wang Miao 2 & Eric Tchetgen Tchetgen 3
Accepted: 31 August 2020 # Springer Nature Switzerland AG 2020
Abstract Purpose of Review Negative controls are a powerful tool to detect and adjust for bias in epidemiological research. This paper introduces negative controls to a broader audience and provides guidance on principled design and causal analysis based on a formal negative control framework. Recent Findings We review and summarize causal and statistical assumptions, practical strategies, and validation criteria that can be combined with subject-matter knowledge to perform negative control analyses. We also review existing statistical methodologies for the detection, reduction, and correction of confounding bias, and briefly discuss recent advances towards nonparametric identification of causal effects in a double-negative control design. Summary There is great potential for valid and accurate causal inference leveraging contemporary healthcare data in which negative controls are routinely available. Design and analysis of observational data leveraging negative controls is an area of growing interest in health and social sciences. Despite these developments, further effort is needed to disseminate these novel methods to ensure they are adopted by practicing epidemiologists. Keywords Bias correction . Bias detection . Bias reduction . Negative control . Unmeasured confounding
Introduction Despite ongoing efforts to improve study design and statistical analysis of epidemiological research, failure to rule out noncausal explanation of empirical findings has prompted substantial discussions in health science [1, 2]. A powerful tool increasingly recognized to mitigate bias is negative control study design and analysis [3••, 4, 5]. Negative controls have a long history in laboratory experiments and epidemiology [3••, 6–8]. However, they have mainly been used to detect bias rather than to remove bias. More recent methodological advances that enable both bias detection and bias removal have not been fully recognized. As a result, the potential for valid and accurate causal inference leveraging contemporary
* Xu Shi [email protected] 1
Department of Biostatistics, University of Michigan, Ann Arbor, USA
2
Department of Probability and Statistics, Peking University, Beijing, China
3
Statistics Department, The Wharton School, University of Pennsylvania, Philadelphia, USA
healthcare data with abundant negative controls has to date not been fully realized. This paper aims to introduce negative controls to a broader audience and provide guidance on principled design and causal analysis based on a formal negative control framework. We focus on resolving bias due to unmeasured confounding in observational studies, although negative controls have recently also been used to tackle a variety of biases such as selection bias [3••, 4, 9], measurement bias [3••, 4], and homophily bias [10, 11] in both ob
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