258 citations · 772 across the 11 of their papers we have counts for
14 papers
The INSIDE assumption under all positive coding: interpretation and partial empirical assessment
Fernando Pires Hartwig, George Davey Smith, Frank Dudbridge +1
Mendelian randomisation (MR) implemented through instrumental variable (IV) analysis is a popular strategy for strengthening causal inference in observational studies. A key assump…
Just what the doctor ordered: An evaluation of provider preference-based Instrumental Variable methods in observational studies, with application for comparative effectiveness of type 2 diabetes therapy
Laura M. Güdemann, Beverley M. Shields, John M. Dennis +1
Instrumental Variables provide a way of addressing bias due to unmeasured confounding when estimating treatment effects using observational data. As instrument prescription prefere…
Triangulating Instrumental Variable, confounder adjustment and Difference-in-Difference methods for comparative effectiveness research in observational data
Laura Güdemann, John M. Dennis, Andrew P. McGovern +4
Observational studies can play a useful role in assessing the comparative effectiveness of competing treatments. In a clinical trial the randomization of participants to treatment…
Connecting Instrumental Variable methods for causal inference to the Estimand Framework
Jack Bowden, Bjoern Bornkamp, Ekkehard Glimm +1
Causal inference methods are gaining increasing prominence in pharmaceutical drug development in light of the recently published addendum on estimands and sensitivity analysis in c…
Average causal effect estimation via instrumental variables: the no simultaneous heterogeneity assumption
F. P. Hartwig, L. Wang, G. Davey Smith +1
Background: Instrumental variables (IVs) can be used to provide evidence as to whether a treatment X has a causal effect on an outcome Y. Even if the instrument Z satisfies the thr…
Sample-constrained partial identification with application to selection bias
Matthew Tudball, Rachael Hughes, Kate Tilling +2
Many partial identification problems can be characterized by the optimal value of a function over a set where both the function and set need to be estimated by empirical data. Desp…
Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score
Qingyuan Zhao, Jingshu Wang, Gibran Hemani +2
Mendelian randomization (MR) is a method of exploiting genetic variation to unbiasedly estimate a causal effect in presence of unmeasured confounding. MR is being widely used in ep…
Two-sample instrumental variable analyses using heterogeneous samples
Qingyuan Zhao, Jingshu Wang, Jack Bowden +1
Instrumental variable analysis is a widely used method to estimate causal effects in the presence of unmeasured confounding. When the instruments, exposure and outcome are not meas…
Response adaptive designs for binary responses: how to offer patient benefit while being robust to time trends?
Sofia S. Villar, Jack Bowden, James Wason
Response-adaptive randomisation (RAR) can considerably improve the chances of a successful treatment outcome for patients in a clinical trial by skewing the allocation probability…
Robust instrumental variable methods using multiple candidate instruments with application to Mendelian randomization
Stephen Burgess, Jack Bowden, Frank Dudbridge +1
Mendelian randomization is the use of genetic variants to make causal inferences from observational data. The field is currently undergoing a revolution fuelled by increasing numbe…
Integrating summarized data from multiple genetic variants in Mendelian randomization: bias and coverage properties of inverse-variance weighted methods
Stephen Burgess, Jack Bowden
Mendelian randomization is the use of genetic variants as instrumental variables to assess whether a risk factor is a cause of a disease outcome. Increasingly, Mendelian randomizat…
On the physical interpretation of a meta-analysis in the presence of heterogeneity and bias: from clinical trials to Mendelian randomization
Jack Bowden, Chris Jackson
The funnel plot is a graphical visualisation of summary data estimates from a meta-analysis, and is a useful tool for detecting departures from the standard modelling assumptions.…
Multi-armed Bandit Models for the Optimal Design of Clinical Trials: Benefits and Challenges
Sofía S. Villar, Jack Bowden, James Wason
Multi-armed bandit problems (MABPs) are a special type of optimal control problem well suited to model resource allocation under uncertainty in a wide variety of contexts. Since th…
On Instrumental Variables Estimation of Causal Odds Ratios
Stijn Vansteelandt, Jack Bowden, Manoochehr Babanezhad +1
Inference for causal effects can benefit from the availability of an instrumental variable (IV) which, by definition, is associated with the given exposure, but not with the outcom…