The challenging interpretation of instrumental variable estimates under monotonicity
Open accessAbstract
Background: Instrumental variable (IV) methods are often used to identify 'local' causal effects in a subgroup of the population of interest. Such 'local' effects may not be ideal for informing clinical or policy decision making. When the instrument is non-causal, additional difficulties arise for interpreting 'local' effects. Little attention has been paid to these difficulties, even though commonly proposed instruments in epidemiology are non-causal (e.g. proxies for physician's preference; genetic variants in some Mendelian randomization studies). Methods: For IV estimates obtained from both causal and non-causal instruments under monotonicity, we present results to help investigators pose four questions about the local effect estimates obtained in their studies. (1) To what subgroup of the population does the effect pertain? Can we (2) estimate the size of or (3) describe the characteristics of this subgroup relative to the study population? (4) Can the sensitivity of the effect estimate to deviations from monotonicity be quantified? Results: We show that the common interpretations and approaches for answering these four questions are generally only appropriate in the case of causal instruments. Conclusions: Appropriate interpretation of an IV estimate under monotonicity as a 'local' effect critically depends on whether the proposed instrument is causal or non-causal. The results and formal proofs presented here can help in the transparent reporting of IV results and in enhancing the use of IV estimates in informing decision-making efforts.
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The homogeneity assumption can also be replaced by imposing a monotonicity assumption that an increase in the number of risk alleles does not lower the likelihood of exposure for any individuals, typically leading to estimating an effect in a subgroup of the study population.
“The homogeneity assumption can also be replaced by imposing a monotonicity assumption that an increase in the number of risk alleles does not lower the likelihood of exposure for any individuals, typically leading to estimating an effect in a subgroup of the study population.61”
From Strengthening the Reporting of Observational Studies in Epidemiology using Mendelian Randomisation (STROBE-MR): explanation and elaboration. (2021) · cites this paper as [61]
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“Meanwhile, the IV model is considered to handle the influence of unmeasured confounders (Greenland, 2000; Swanson & Hernán, 2018).”
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“Finally, to be interpreted as causal effects, IV analyses require a fourth assumption of homogeneity for the average causal effect or monotonicity for the local average causal effect (Hernán & Robins, 2006; Swanson & Hernán, 2018).”
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Reporting of Baseline Characteristics to Accompany Analysis by Instrumental Variables
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“Alternatively, if we assume the treatment effect does not vary with principal strata membership, the instrumental variable (IV) estimand is equal to the average treatment effect, but this assumption may be implausible.(22) Critically, the complier average causal effect estimand doesn’t tell us about possible causal ef…”
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“In MR studies, this condition is ensured by maintaining a positive association coefficient between the exposure and each IV, followed by harmonizing the reference and effect alleles between the exposure and outcome GWAS [21, 22].”
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Causal relationship between asthma and hernia risk
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- Authors
- Swanson, S. A., & Hernán, M. A.
- Affiliations
- Harvard University (US)
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA
- Corresponding author
- Swanson, Sonja A
- ORCID
- Swanson, Sonja A
- Venue
- International Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-03-30
- Volume
- 47
- Issue
- 4
- Pages
- 1289-1297
- ISSN
- 0300-5771 (ISSN-L)
- 1464-3685
- PMC
- PMC6124612
- OpenAlex
- W2597625792
- Cited by
- 71
- References
- 36
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 8 · 2023: 9 · 2022: 10 · 2021: 9 · 2020: 4 · 2019: 10 · 2018: 7 · 2017: 3
- FWCI
- 4.53
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- International Journal of Epidemiology (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion, other-oa)
- RePub (Erasmus University Rotterdam) (submittedVersion, other-oa)
- Funders
- National Institutes of Health (R01 AI102634)
- DynaHEALTH
- European Union (H2020-PHC-2014, 633595)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- instrumental variable estimation, monotonicity assumption, subgroup effects, Mendelian randomization
- MeSH terms
- Data Interpretation, Statistical, Humans, Practice Patterns, Physicians', Genetic Variation, Models, Statistical, Bias, Mendelian Randomization Analysis
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