title: Implementation and reporting of causal mediation analysis in 2015: a systematic review in epidemiological studies.
abstract: BACKGROUND
  Causal mediation analysis is often used to understand the impact of variables along the causal pathway of an occurrence relation. How well studies apply and report the elements of causal mediation analysis remains unknown.
  
  
  METHODS
  We systematically reviewed epidemiological studies published in 2015 that employed causal mediation analysis to estimate direct and indirect effects of observed associations between an exposure on an outcome. We identified potential epidemiological studies through conducting a citation search within Web of Science and a keyword search within PubMed. Two reviewers independently screened studies for eligibility. For eligible studies, one reviewer performed data extraction, and a senior epidemiologist confirmed the extracted information. Empirical application and methodological details of the technique were extracted and summarized.
  
  
  RESULTS
  Thirteen studies were eligible for data extraction. While the majority of studies reported and identified the effects of measures, most studies lacked sufficient details on the extent to which identifiability assumptions were satisfied. Although most studies addressed issues of unmeasured confounders either from empirical approaches or sensitivity analyses, the majority did not examine the potential bias arising from the measurement error of the mediator. Some studies allowed for exposure-mediator interaction and only a few presented results from models both with and without interactions. Power calculations were scarce.
  
  
  CONCLUSIONS
  Reporting of causal mediation analysis is varied and suboptimal. Given that the application of causal mediation analysis will likely continue to increase, developing standards of reporting of causal mediation analysis in epidemiological research would be prudent.
authors.affiliation: Clinical and Population Health Research Program, Graduate School of Biomedical Sciences, University of Massachusetts Medical School, 368 Plantation Street, Worcester, MA, 01655, USA. shaohsien.liu@umassmed.edu.
authors.forename: Shao-Hsien
authors.initials: SH
authors.lastname: Liu
authors.affiliation: Division of Epidemiology of Chronic Diseases and Vulnerable Populations, Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, 01605, USA.
authors.forename: Christine M
authors.initials: CM
authors.lastname: Ulbricht
authors.affiliation: Division of Biostatistics and Health Services Research, Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, 01605, USA.
authors.forename: Stavroula A
authors.initials: SA
authors.lastname: Chrysanthopoulou
authors.affiliation: Division of Epidemiology of Chronic Diseases and Vulnerable Populations, Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, 01605, USA.
authors.forename: Kate L
authors.initials: KL
authors.lastname: Lapane
mesh_terms: D015982:Bias; D015984:Causality; D016021:Epidemiologic Studies; D006801:Humans; D015233:Models, Statistical
keywords: Causal inference; Causal mediation analysis; Causality; Systematic review