Is structural equation modelling a step forward for epidemiologists?
Yu-Kang Tu Biostatistics Unit, Centre for Epidemiology and Biostatistics, Leeds Institute for Genetics, Health and Therapeutics, and Leeds Dental Institute, University of Leeds, UK. E-mail: y.k.tu{at}leeds.ac.uk’ + u + ‘@’ + d + ”//–> Accepted 27 November 2008 One of the major challenges for epidemiologists is to understand causal relationships between risk factors and health outcomes by analysing data from observational studies. Epidemiologists know too well that correlations, such as those in regression analysis, rarely mean causation, and it would be very desirable if there is a methodology for observational studies, analogous to randomization in experimental studies, which can discover causes and effects amongst variables or at least confirm or refute the proposed causal relationships. Randomized controlled trials (RCTs) are certainly the gold standard of establishing causes and effects, but quite often it is either unethical or unfeasible to conduct RCTs to test causal relations
