Meta-analysis and causal inference: a case study of benzene and non-Hodgkin lymphoma

Meta-analysis is an important method in the practice of occupational epidemiology, with a legitimate, but limited role to play in causal inference. Meta-analysis provides an assessment of consistency-one of several classic causal criteria-through tests of heterogeneity and an assessment of differences across studies. It can also provide an increase in the precision of effect estimates, including the precision of dose response relationships. Causal inference, however, involves much more: a complete assessment of the classic causal criteria, for example. Causal claims, therefore, should not emerge from meta-analyses as such. A recent meta-analysis of epidemiological studies of benzene exposure and non-Hodgkin lymphoma (NHL), however, does exactly that. Using studies from a previous narrative review in which the authors made no causal claim, the same authors performed a meta-analysis and concluded that it represented new evidence that benzene causes NHL. Despite a lack of consistency (i.e., significant heterogeneity), weak associations, no evidence of dose-response, no effort to provide an assessment of biological plausibility, and no new epidemiological evidence, the authors, nevertheless, changed their conclusion from association to causation. By using case study as an illustrative platform, this commentary provides cautionary and critical comments about the use of meta-analysis and causal inference in occupational epidemiology.