dc.contributor | Thompson, Simon G. | |
dc.creator | Burgess, Stephen | |
dc.date.accessioned | 2018-11-24T23:26:11Z | |
dc.date.available | 2012-04-17T08:21:35Z | |
dc.date.available | 2018-11-24T23:26:11Z | |
dc.date.issued | 2012-03-06 | |
dc.identifier | http://www.dspace.cam.ac.uk/handle/1810/242184 | |
dc.identifier | https://www.repository.cam.ac.uk/handle/1810/242184 | |
dc.identifier.uri | http://repository.aust.edu.ng/xmlui/handle/123456789/3780 | |
dc.description.abstract | Mendelian randomization is an epidemiological method for using genetic variation
to estimate the causal effect of the change in a modifiable phenotype on
an outcome from observational data. A genetic variant satisfying the assumptions
of an instrumental variable for the phenotype of interest can be used
to divide a population into subgroups which differ systematically only in the
phenotype. This gives a causal estimate which is asymptotically free of bias
from confounding and reverse causation. However, the variance of the causal
estimate is large compared to traditional regression methods, requiring large
amounts of data and necessitating methods for efficient data synthesis. Additionally,
if the association between the genetic variant and the phenotype is not
strong, then the causal estimates will be biased due to the “weak instrument”
in finite samples in the direction of the observational association. This bias
may convince a researcher that an observed association is causal. If the causal
parameter estimated is an odds ratio, then the parameter of association will
differ depending on whether viewed as a population-averaged causal effect or
a personal causal effect conditional on covariates.
We introduce a Bayesian framework for instrumental variable analysis, which
is less susceptible to weak instrument bias than traditional two-stage methods,
has correct coverage with weak instruments, and is able to efficiently combine
gene–phenotype–outcome data from multiple heterogeneous sources. Methods
for imputing missing genetic data are developed, allowing multiple genetic variants
to be used without reduction in sample size. We focus on the question of
a binary outcome, illustrating how the collapsing of the odds ratio over heterogeneous
strata in the population means that the two-stage and the Bayesian
methods estimate a population-averaged marginal causal effect similar to that
estimated by a randomized trial, but which typically differs from the conditional
effect estimated by standard regression methods. We show how these
methods can be adjusted to give an estimate closer to the conditional effect.
We apply the methods and techniques discussed to data on the causal effect of
C-reactive protein on fibrinogen and coronary heart disease, concluding with
an overall estimate of causal association based on the totality of available data
from 42 studies. | |
dc.language | en | |
dc.publisher | University of Cambridge | |
dc.publisher | Department of Pure Mathematics and Mathematical Statistics | |
dc.publisher | MRC Biostatistics Unit | |
dc.rights | http://creativecommons.org/licenses/by-nc-nd/2.0/uk/ | |
dc.rights | Attribution-NonCommercial-NoDerivs 2.0 UK: England & Wales | |
dc.subject | Causal inference | |
dc.subject | Instrumental variables | |
dc.subject | Mendelian randomization | |
dc.subject | Bayesian methods | |
dc.subject | Meta-analysis | |
dc.subject | Missing data | |
dc.subject | Non-collapsibility | |
dc.title | Statistical issues in Mendelian randomization: use of genetic instrumental variables for assessing causal associations | |
dc.type | Thesis | |