Abstract
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. These techniques are 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.
Highlights
Unmeasured or residual confounders can bias the results from observational studies of routinely collected data
One way to evaluate whether the instrumental variable assumptions hold is to investigate whether the instrumental variables are associated with negative control outcomes likely to be affected by the same confounders as the outcome of interest, but that are unlikely to be directly affected by the exposure
We investigated whether varenicline was associated with a negative control outcome, urinary tract infections, as smoking cessation treatment is unlikely to affect the incidence of urinary tract infections
Summary
Unmeasured or residual confounders can bias the results from observational studies of routinely collected data. In pharmacoepidemiological studies, treatment choice is influenced by a number of factors (e.g. comorbidities, socioeconomic position, education) that relate to outcomes, but are often not perfectly recorded or measurable in the sorts of electronic medical records data that are used in such analyses. This ‘confounding by indication’ means that the observed association of treatment with an outcome is often an unreliable indicator of any causal adverse or beneficial effects of the treatment of interest. The validity of instrumental variables for dealing with confounding by indication.[22]
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