Repository logo
 

Semiparametric methods for estimation of a nonlinear exposure-outcome relationship using instrumental variables with application to Mendelian randomization

Published version
Peer-reviewed

Change log

Authors

Staley, JR 

Abstract

Mendelian randomization, the use of genetic variants as instrumental variables (IV), can test for and estimate the causal effect of an exposure on an outcome. Most IV methods assume that the function relating the exposure to the expected value of the outcome (the exposure-outcome relationship) is linear. However, in practice, this assumption may not hold. Indeed, often the primary question of interest is to assess the shape of this relationship. We present two novel IV methods for investigating the shape of the exposure-outcome relationship: a fractional polynomial method and a piecewise linear method. We divide the population into strata using the exposure distribution, and estimate a causal effect, referred to as a localized average causal effect (LACE), in each stratum of population. The fractional polynomial method performs metaregression on these LACE estimates. The piecewise linear method estimates a continuous piecewise linear function, the gradient of which is the LACE estimate in each stratum. Both methods were demonstrated in a simulation study to estimate the true exposure-outcome relationship well, particularly when the relationship was a fractional polynomial (for the fractional polynomial method) or was piecewise linear (for the piecewise linear method). The methods were used to investigate the shape of relationship of body mass index with systolic blood pressure and diastolic blood pressure.

Description

Keywords

causal effects, fractional polynomials, genetic variants, piecewise linear models, UK Biobank

Journal Title

Genetic Epidemiology

Conference Name

Journal ISSN

0741-0395
1098-2272

Volume Title

41

Publisher

Wiley
Sponsorship
MRC (1646420)
Medical Research Council (G0800270)
Medical Research Council (MR/L003120/1)
European Research Council (268834)
Wellcome Trust (100114/Z/12/Z)
British Heart Foundation (None)
British Heart Foundation (None)
Medical Research Council (MC_UU_00002/7)
Wellcome Trust (204623/Z/16/Z)
Medical Research Council (G0800270/1)
This work was supported by the UK Medical Research Council (G66840, G0800270), Pfizer (G73632), British Heart Foundation (SP/09/002), UK National Institute for Health Research Cambridge Biomedical Research Centre, European Research Council (268834), and European Commission Framework Programme 7 (HEALTH-F2-2012-279233). Stephen Burgess is supported by the Wellcome Trust (100114).
Relationships
Is derived from: