Estimating structural mean models with multiple instrumental variables using the generalised method of moments

Clarke, Paul and Palmer, Thomas Michael and Windmeijer, Frank (2015) Estimating structural mean models with multiple instrumental variables using the generalised method of moments. Statistical Science, 30 (1). pp. 96-117. ISSN 0883-4237

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Abstract

Instrumental variables analysis using genetic markers as instruments is now a widely used technique in epidemiology and biostatistics. As single markers tend to explain only a small proportion of phenotypic variation, there is increasing interest in using multiple genetic markers to obtain more precise estimates of causal parameters. Structural mean models (SMMs) are semiparametric models that use instrumental variables to identify causal parameters. Recently, interest has started to focus on using these models with multiple instruments, particularly for multiplicative and logistic SMMs. In this paper we show how additive, multiplicative and logistic SMMs with multiple orthogonal binary instrumental variables can be estimated efficiently in models with no further (continuous) covariates, using the generalised method of moments (GMM) estimator. We discuss how the Hansen J-test can be used to test for model misspecification, and how standard GMM software routines can be used to fit SMMs. We further show that multiplicative SMMs, like the additive SMM, identify a weighted average of local causal effects if selection is monotonic. We use these methods to reanalyse a study of the relationship between adiposity and hypertension using SMMs with two genetic markers as instruments for adiposity. We find strong effects of adiposity on hypertension.

Item Type:
Journal Article
Journal or Publication Title:
Statistical Science
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600
Subjects:
ID Code:
76804
Deposited By:
Deposited On:
24 Nov 2015 11:46
Refereed?:
Yes
Published?:
Published
Last Modified:
27 May 2020 04:13