Instrumental variable analysis: choice of control variables is critical and can lead to biased results

Abstract

AbstractInstrumental variable (IV) analysis is a widely used technique in econometrics to estimate causal effects in the presence of confounding. A recent application of this technique was used in a high-profile analysis inJAMA Internal Medicineto estimate the effect of cefepime, a broad-spectrum antibiotic, on mortality in severe infection. There has been ongoing concern that piperacillin-tazobactam, another broad-spectrum antibiotic with greater anaerobic activity might be inferior to cefepime, however this has not been shown in randomized controlled trials. The authors used an international shortage of piperacillin-tazobactam as an instrument, as during this shortage period, cefepime was used as an alternative. The authors report a strong mortality effect (5% absolute increase) with piperacillin-tazobactam. In this paper, we closely examine this estimate and find it is likely conditional on inclusion of a control variable (metronidazole usage). Inclusion of this variable is highly likely to lead to collider bias, which we show via simulation. We then generate estimates unadjusted for metronidazole which are much closer to the null and may represent residual confounding or confounding by indication. We highlight the ongoing challenge of collider bias in empirical IV analyses and the potential for large biases to occur. We finally suggest the authors consider including these unadjusted estimates in their manuscript, as the large increase in mortality reported with piperacillin-tazobactam is unlikely to be true.