How to compare instrumental variable and conventional regression analyses using negative controls and bias plots.
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.
['Davies|Neil M|NM|', 'Thomas|Kyla H|KH|', 'Taylor|Amy E|AE|', 'Taylor|Gemma M J|GMJ|', 'Martin|Richard M|RM|', 'Munafò|Marcus R|MR|', 'Windmeijer|Frank|F|']
D015982:Bias D016022:Case-Control Studies D015984:Causality D015986:Confounding Factors, Epidemiologic D003627:Data Interpretation, Statistical D006801:Humans D017063:Outcome Assessment, Health Care D017891:Pharmacoepidemiology D012044:Regression Analysis
Instrumental variables negative controls pharmacoepidemiology causal inference