Data · dataset · 2015
Instrumental Variables Estimation With Some Invalid Instruments and its Application to Mendelian Randomization
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Instrumental variables have been widely used for estimating the causal effect between exposure and outcome.
Description
Conventional estimation methods require complete knowledge about all the instruments’ validity; a valid instrument must not have a direct effect on the outcome and not be related to unmeasured confounders. Often, this is impractical as highlighted by Mendelian randomization studies where genetic markers are used as instruments and complete knowledge about instruments’ validity is equivalent to complete knowledge about the involved genes’ functions.
In this article, we propose a method for estimation of causal effects when this complete knowledge is absent. It is shown that causal effects are identified and can be estimated as long as less than 50% of instruments are invalid, without knowing which of the instruments are invalid. We also introduce conditions for identification when the 50% threshold is violated.
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A fast penalized ℓ 1 estimation method, called sisVIVE, is introduced for estimating the causal effect without knowing which instruments are valid, with theoretical guarantees on its performance. The proposed method is demonstrated on simulated data and a real Mendelian randomization study concerning the effect of body mass index(BMI) on health-related quality of life (HRQL) index. An R package sisVIVE is available on CRAN.
Supplementary materials for this article are available online.
Links
Where it is published
- Repository landing page tandf.figshare.com/articles/dataset/Instrumental_Variables_Estimation_With_Some_I… ↗
landing page · from DataCite
- DOI doi.org/10.6084/m9.figshare.1293027 ↗
DOI / persistent id · from DataCite
Documentation and papers
- Creative Commons Attribution 4.0 International creativecommons.org/licenses/by/4.0/legalcode ↗
license · from DataCite
- IsSupplementTo 10.1080/01621459.2014.994705 doi.org/10.1080/01621459.2014.994705 ↗
publication · from DataCite
Catalogue records · 2
- DataCite API api.datacite.org/dois/10.6084/m9.figshare.1293027 ↗
metadata API · from DataCite
- DataCite Commons commons.datacite.org/doi.org/10.6084/m9.figshare.1293027 ↗
catalogue entry · from DataCite
Topics
- Stated by source
- Biological sciences · Mathematics
- From keywords
- Medicine & Health
Related
Provenance · 1 source records, 11 field assertions
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|---|---|---|---|
| DataCite | 10.6084/m9.figshare.1293027 | 13 d ago | JSON v1 |
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