Identification and Inference with Many Invalid Instruments

Kolesár, Michal; Chetty, Raj; Friedman, John N.; Glaeser, Edward L.; Imbens, Guido W.

doi:10.3386/w17519

Cited by 31 publications

(43 citation statements)

References 32 publications

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“…This relaxation of the IV assumptions was recently investigated by Kolesár et al. , 25 although their work differs from ours and is not presented within the context of Mendelian randomization.…”

Section: Methodsmentioning

confidence: 82%

“…Kolesár et al. 25 also propose a consistent causal estimator under the same conditions as considered in this paper. This is based on a modified version of the bias-corrected TSLS estimator, which is part of the wider group of k -class estimators, a group that also includes the TSLS, bias-corrected TSLS and limited information maximum likelihood estimators 43 .…”

Section: Discussionmentioning

confidence: 97%

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Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

Bowden

Smith

Burgess

2015

International Journal of Epidemiology

6,446

6,192

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Background: The number of Mendelian randomization analyses including large numbers of genetic variants is rapidly increasing. This is due to the proliferation of genome-wide association studies, and the desire to obtain more precise estimates of causal effects. However, some genetic variants may not be valid instrumental variables, in particular due to them having more than one proximal phenotypic correlate (pleiotropy).Methods: We view Mendelian randomization with multiple instruments as a meta-analysis, and show that bias caused by pleiotropy can be regarded as analogous to small study bias. Causal estimates using each instrument can be displayed visually by a funnel plot to assess potential asymmetry. Egger regression, a tool to detect small study bias in meta-analysis, can be adapted to test for bias from pleiotropy, and the slope coefficient from Egger regression provides an estimate of the causal effect. Under the assumption that the association of each genetic variant with the exposure is independent of the pleiotropic effect of the variant (not via the exposure), Egger’s test gives a valid test of the null causal hypothesis and a consistent causal effect estimate even when all the genetic variants are invalid instrumental variables.Results: We illustrate the use of this approach by re-analysing two published Mendelian randomization studies of the causal effect of height on lung function, and the causal effect of blood pressure on coronary artery disease risk. The conservative nature of this approach is illustrated with these examples.Conclusions: An adaption of Egger regression (which we call MR-Egger) can detect some violations of the standard instrumental variable assumptions, and provide an effect estimate which is not subject to these violations. The approach provides a sensitivity analysis for the robustness of the findings from a Mendelian randomization investigation.

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Section: Methodsmentioning

confidence: 82%

Section: Discussionmentioning

confidence: 97%

Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

Bowden

Smith

Burgess

2015

International Journal of Epidemiology

6,446

6,192

View full text Add to dashboard Cite

show abstract

“…However, their violation does not necessarily preclude valid causal inference: consistent estimation of the causal effect is still possible if the magnitude of the pleiotropy in equation , α j + k y ψ j , is independent of the SNP‐exposure associations, γ j + k x ψ j . This was first identified as a crucial assumption for causal inference in the econometrics literature by Kolesar et al and was independently derived for use in MR by Bowden et al , who termed it the InSIDE assumption (Instrument Strength Independent of Direct Effect). Because ψ j is a common factor of both the instrument strength and pleiotropy terms, by far the most natural way to imagine that InSIDE could hold (even in principle) is if IV2 holds ( ψ j = 0) and the magnitude of the pleiotropy not via U ( α j ) is independent of γ j across the instruments.…”

Section: Modelling Assumptionsmentioning

confidence: 99%

A framework for the investigation of pleiotropy in two‐sample summary data Mendelian randomization

Bowden

Minelli

et al. 2017

Statistics in Medicine

1,342

1,176

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Mendelian randomization (MR) uses genetic data to probe questions of causality in epidemiological research, by invoking the Instrumental Variable (IV) assumptions. In recent years, it has become commonplace to attempt MR analyses by synthesising summary data estimates of genetic association gleaned from large and independent study populations. This is referred to as two‐sample summary data MR. Unfortunately, due to the sheer number of variants that can be easily included into summary data MR analyses, it is increasingly likely that some do not meet the IV assumptions due to pleiotropy. There is a pressing need to develop methods that can both detect and correct for pleiotropy, in order to preserve the validity of the MR approach in this context. In this paper, we aim to clarify how established methods of meta‐regression and random effects modelling from mainstream meta‐analysis are being adapted to perform this task. Specifically, we focus on two contrastin g approaches: the Inverse Variance Weighted (IVW) method which assumes in its simplest form that all genetic variants are valid IVs, and the method of MR‐Egger regression that allows all variants to violate the IV assumptions, albeit in a specific way. We investigate the ability of two popular random effects models to provide robustness to pleiotropy under the IVW approach, and propose statistics to quantify the relative goodness‐of‐fit of the IVW approach over MR‐Egger regression. © 2017 The Authors. Statistics in Medicine Published by JohnWiley & Sons Ltd

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“…We exclude the given patient from this measure to avoid a direct linkage between Z and the average spending in a given hospital – a Jackknife Instrumental Variables Estimator (JIVE) that is more robust to weak-instrument concerns when fixed effects are used to construct an instrument (Stock et al 2002, Doyle 2007, Kolesar et al 2011). 9 …”

Section: Empirical Strategymentioning

confidence: 99%

Measuring Returns to Hospital Care: Evidence from Ambulance Referral Patterns

Doyle

Graves

Gruber

et al. 2015

Journal of Political Economy

154

129

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Medicare spending exceeds 4% of GDP in the US each year, and there are concerns that moral hazard problems have led to overspending. This paper considers whether hospitals that treat patients more aggressively and receive higher payments from Medicare improve health outcomes for their patients. An innovation is a new lens to compare hospital performance for emergency patients: plausibly exogenous variation in ambulance-company assignment among patients who live near one another. Using Medicare data from 2002–2010, we show that ambulance company assignment importantly affects hospital choice for patients in the same ZIP code. Using data for New York State from 2000–2006 that matches exact patient addresses to hospital discharge records, we show that patients who live very near each other but on either side of ambulance service area boundaries go to different types of hospitals. Both identification strategies show that higher-cost hospitals achieve better patient outcomes for a variety of emergency conditions. Using our Medicare sample, the estimates imply that a one standard deviation increase in Medicare reimbursement leads to a 4 percentage point reduction in mortality (10% compared to the mean). Taking into account one-year spending after the health shock, the implied cost per at least one year of life saved is approximately $80,000. These results are found across different types of hospitals and patients, as well across both identification strategies.

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Identification and Inference with Many Invalid Instruments

Cited by 31 publications

References 32 publications

Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression

A framework for the investigation of pleiotropy in two‐sample summary data Mendelian randomization

Measuring Returns to Hospital Care: Evidence from Ambulance Referral Patterns

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