|Number of pages||11|
|Journal||International Journal of Epidemiology|
|Early online date||7 Apr 2017|
|State||E-pub ahead of print - 7 Apr 2017|
There is increasing interest in the use of instrumental variable analysis to overcome unmeasured confounding in observational pharmacoepidemiological studies. This is partly because instrumental variable analyses are potentially less biased than conventional regression analyses. However, instrumental variable analyses are less precise, and regulators and clinicians find it difficult to interpret conflicting evidence from instrumental variable compared with conventional regression analyses. In this paper, we describe three techniques to assess which approach (instrumental variable versus conventional regression analyses) is least biased: negative control outcomes; negative control populations; and tests of covariate balance. We illustrate these methods using an analysis of the effects of smoking cessation therapies (varenicline) prescribed in primary care.
- instrumental variables, causal inference, pharmacoepidemiology, negative controls, electronic medical records