An introduction to instrumental variables analysis: part 1.
There are several examples in the medical literature where the associations of treatment effects predicted by observational studies have been refuted by evidence from subsequent large-scale randomised trials. This is because of the fact that non-experimental studies are subject to confounding - and confounding cannot be entirely eliminated even if all known confounders have been measured in the study as there may be unknown confounders. The aim of this 2-part methodological primer is to introduce an emerging methodology for estimating treatment effects using observational data in the absence of good randomised evidence known as the method of instrumental variables.
Bennett
Derrick A
DA
Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK.
D015987:Effect Modifier, Epidemiologic; D019317:Evidence-Based Medicine / Q000379:methods*; D006801:Humans; D016018:Least-Squares Analysis; D015233:Models, Statistical; D019370:Observation / Q000379:methods; D016032:Randomized Controlled Trials as Topic / Q000379:methods* / Q000706:statistics & numerical data; D012107:Research Design