Assumptions of IV methods for observational epidemiology

V Didelez, Sha Meng, Nuala Sheehan

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

120 Citations (Scopus)

Abstract

Instrumental variable (IV) methods are becoming increasingly popular as they seem to offer the only viable way to overcome the problem of unobserved confounding in observational studies. However, some attention has to be paid to the details, as not all such methods target the same causal parameters and some rely on more restrictive parametric assumptions than others. We therefore discuss and contrast the most common IV approaches with relevance to typical applications in observational epidemiology. Further, we illustrate and compare the asymptotic bias of these IV estimators when underlying assumptions are violated in a numerical study. One of our conclusions is that all IV methods encounter problems in the presence of effect modification by unobserved confounders. Since this can never be ruled out for sure, we recommend that practical applications of IV estimators be accompanied routinely by a sensitivity analysis.
Translated title of the contributionAssumptions of IV methods for observational epidemiology
Original languageEnglish
Pages (from-to)22 - 40
Number of pages19
JournalStatistical Science
Volume25
Issue number1
DOIs
Publication statusPublished - Jan 2010

Bibliographical note

Publisher: Cornell Univ Press / Duke Univ Press

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