Citation

Ali MS, Uddin MJ, Groenwold RH, Pestman WR, Belitser SV, Hoes AW, de Boer A, Roes KC, Klungel OH. Epidemiology 2014; 25(5): 770-772.

Affiliation

Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands Department of Mathematical Psychology, Catholic University of Leuven, Leuven, Belgium Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands, O.H.Klungel@uu.nl.

Copyright

(Copyright © 2014, Lippincott Williams and Wilkins)

DOI

10.1097/EDE.0000000000000152

PMID

25076152

Abstract

Instrumental variable analysis has been used to control for unmeasured confounding in nonrandomized studies. An instrumental variable 1) is associated with exposure, 2) affects outcome only through exposure, and 3) is the independent of confounders. If these key assumptions are satisfied (together with additional assumptions such as homogeneity) instrumental variable analysis could consistently estimate the average causal effect of exposure. However if one of the assumptions is violated, the estimate can be severely biased. Several methods are available for checking the first assumption but there is no well-established method of checking the second and third assumptions. Some authors have argued that these assumptions are untestable, as they involve unmeasured confounding.

We proposed a standardized difference a robust balance measure used in propensity score analysis to falsify the third assumption by checking independence between an instrumental variable and measured confounders

Language: en