Abstract
In this paper we review the methodological underpinnings of the general pharmacogenetic approach for uncovering genetically-driven treatment effect heterogeneity. This typically utilises only individuals who are treated and relies on fairly strong baseline assumptions to estimate what we term the 'genetically moderated treatment effect' (GMTE). When these assumptions are seriously violated, we show that a robust but less efficient estimate of the GMTE that incorporates information on the population of untreated individuals can instead be used. In cases of partial violation, we clarify when Mendelian randomization and a modified confounder adjustment method can also yield consistent estimates for the GMTE. A decision framework is then described to decide when a particular estimation strategy is most appropriate and how specific estimators can be combined to further improve efficiency. Triangulation of evidence from different data sources, each with their inherent biases and limitations, is becoming a well established principle for strengthening causal analysis. We call our framework 'Triangulation WIthin a STudy' (TWIST)' in order to emphasise that an analysis in this spirit is also possible within a single data set, using causal estimates that are approximately uncorrelated, but reliant on different sets of assumptions. We illustrate these approaches by re-analysing primary-care-linked UK Biobank data relating to CYP2C19 genetic variants, Clopidogrel use and stroke risk, and data relating to APOE genetic variants, statin use and Coronary Artery Disease.
Highlights
Over the last 20 years the field of Epidemiology has embraced the exploitation of random genetic inheritance to help uncover causal mechanisms of disease using the technique of Mendelian randomization (MR). [1]
If a person’s genetics means they will experience a much reduced treatment effect, as measured with respect to a particular health outcome, they could be switched to an alternative therapy
This analysis is compromised if genetic variants
Summary
Over the last 20 years the field of Epidemiology has embraced the exploitation of random genetic inheritance to help uncover causal mechanisms of disease using the technique of Mendelian randomization (MR). [1]. The association between the exposure and an outcome, Y, hypothesised to be a downstream consequence of X, may be contributed to in observational data by unobserved confounding, U. If present, such confounding would mean that the naive association between X and Y would not reflect the causal effect of X on Y. If the important confounders could be appropriately measured and adjusted for, and no systematic selection bias or loss to follow up was present in the data this last assumption, individuals with the same exposure level would be exchangeable [2] and observational associations could be interpreted causally.
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