How to compare instrumental variable and conventional regression analyses using negative controls and bias plots

Neil Davies*, Kyla Thomas, Amy Taylor, Gemma Taylor, Richard Martin, Marcus Munafo, Frank Windmeijer

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

11 Citations (Scopus)
347 Downloads (Pure)

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: 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.
Original languageEnglish
Article numberdyx014
Pages (from-to)2067-2077
Number of pages11
JournalInternational Journal of Epidemiology
Volume46
Issue number6
Early online date7 Apr 2017
DOIs
Publication statusPublished - 1 Dec 2017

Structured keywords

  • ICEP
  • Brain and Behaviour
  • Tobacco and Alcohol

Keywords

  • instrumental variables
  • causal inference
  • pharmacoepidemiology
  • negative controls
  • electronic medical records

Fingerprint Dive into the research topics of 'How to compare instrumental variable and conventional regression analyses using negative controls and bias plots'. Together they form a unique fingerprint.

Cite this