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Eur J Epidemiol. 2018 Aug;33(8):723-728. doi: 10.1007/s10654-018-0396-6. Epub 2018 May 2.

Causal null hypotheses of sustained treatment strategies: What can be tested with an instrumental variable?

Author information

1
Department of Epidemiology, Erasmus MC, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands. s.swanson@erasmusmc.nl.
2
Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, USA. s.swanson@erasmusmc.nl.
3
Department of Epidemiology, Erasmus MC, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands.
4
Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, USA.
5
Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, USA.
6
Harvard-MIT Division of Health Sciences and Technology, Boston, USA.

Abstract

Sometimes instrumental variable methods are used to test whether a causal effect is null rather than to estimate the magnitude of a causal effect. However, when instrumental variable methods are applied to time-varying exposures, as in many Mendelian randomization studies, it is unclear what causal null hypothesis is tested. Here, we consider different versions of causal null hypotheses for time-varying exposures, show that the instrumental variable conditions alone are insufficient to test some of them, and describe additional assumptions that can be made to test a wider range of causal null hypotheses, including both sharp and average causal null hypotheses. Implications for interpretation and reporting of instrumental variable results are discussed.

KEYWORDS:

Causal null hypothesis; Hypothesis testing; Instrumental variable; Mendelian randomization

PMID:
29721747
PMCID:
PMC6061140
DOI:
10.1007/s10654-018-0396-6
[Indexed for MEDLINE]
Free PMC Article

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