Predicting the Direction of Causal Effect Based on an Instrumental Variable Analysis: A Cautionary Tale

AbstractAn instrumental variable can be used to test the causal null hypothesis that an exposure has no causal effect on the outcome, by assessing the association between the instrumental variable and the outcome. Under additional assumptions, an instrumental variable can be used to estimate the magnitude of causal effect of the exposure on the outcome. In this paper, we investigate whether these additional assumptions are necessary in order to predict the direction of the causal effect, based on the direction of association between the instrumental variable and the outcome, or equivalently based on the standard (Wald) instrumental variable estimate. We demonstrate by counterexample that if these additional assumptions (such as monotonicity of the instrument–exposure association) are not satisfied, then the instrumental variable–outcome association can be in the opposite direction to the causal effect for all individuals in the population. Although such scenarios are unlikely, in most cases, a definite conclusion about the direction of causal effect requires similar assumptions to those required to estimate a causal effect.This is the final version of the article. It first appeared from De Gruyter via http://dx.doi.org

A Model of ICDT Internet flows on mobile devices for the travel and tourism consumer

Despite the increasing use of mobile devices and their applications in the travel and tourism arena, there is a lack of literature that considers how mobile device tourism applications could be evaluated. Built around a discussion of information attributes (a series of dimensions by which the delivery of information can be assessed) that have been specifically developed for the tourism sector and an examination of the specific characteristics of mobile devices, this theoretical article classifies different online tourism applications that can be accessed by mobile devices according to Angehrn's four virtual spaces (information, communication, distribution, and transaction). This is for the purpose of demonstrating that the majority of applications in the mobile tourism arena eventually fall within the realm of information provision and can thus be assessed according to how they perform in relation to information attributes. A model of ICDT Internet flows on mobile devices for the travel and tourism consumer is presented

Mendelian randomization with a binary exposure variable: interpretation and presentation of causal estimates

Mendelian randomization uses genetic variants to make causal inferences about a modifiable exposure. Subject to a genetic variant satisfying the instrumental variable assumptions, an association between the variant and outcome implies a causal effect of the exposure on the outcome. Complications arise with a binary exposure that is a dichotomization of a continuous risk factor (for example, hypertension is a dichotomization of blood pressure). This can lead to violation of the exclusion restriction assumption: the genetic variant can influence the outcome via the continuous risk factor even if the binary exposure does not change. Provided the instrumental variable assumptions are satisfied for the underlying continuous risk factor, causal inferences for the binary exposure are valid for the continuous risk factor. Causal estimates for the binary exposure assume the causal effect is a stepwise function at the point of dichotomization. Even then, estimation requires further parametric assu

A review of instrumental variable estimators for Mendelian randomization.

Instrumental variable analysis is an approach for obtaining causal inferences on the effect of an exposure (risk factor) on an outcome from observational data. It has gained in popularity over the past decade with the use of genetic variants as instrumental variables, known as Mendelian randomization. An instrumental variable is associated with the exposure, but not associated with any confounder of the exposure-outcome association, nor is there any causal pathway from the instrumental variable to the outcome other than via the exposure. Under the assumption that a single instrumental variable or a set of instrumental variables for the exposure is available, the causal effect of the exposure on the outcome can be estimated. There are several methods available for instrumental variable estimation; we consider the ratio method, two-stage methods, likelihood-based methods, and semi-parametric methods. Techniques for obtaining statistical inferences and confidence intervals are presented. The statistical properties of estimates from these methods are compared, and practical advice is given about choosing a suitable analysis method. In particular, bias and coverage properties of estimators are considered, especially with weak instruments. Settings particularly relevant to Mendelian randomization are prioritized in the paper, notably the scenario of a continuous exposure and a continuous or binary outcome.Stephen Burgess is supported by the Wellcome Trust (grant number 100114). Dylan Small was supported by a grant from the US National Science Foundation Measurement, Methodology and Statistics program. Simon G. Thompson is supported by the British Heart Foundation (grant number CH/12/2/29428).This is the final version of the article. It was first available from SAGE via http://dx.doi.org/10.1177/096228021559757

Selecting likely causal risk factors from high-throughput experiments using multivariable Mendelian randomization

Modern high-throughput experiments provide a rich resource to investigate causal determinants of disease risk. Mendelian randomization (MR) is the use of genetic variants as instrumental variables to infer the causal effect of a specific risk factor on an outcome. Multivariable MR is an extension of the standard MR framework to consider multiple potential risk factors in a single model. However, current implementations of multivariable MR use standard linear regression and hence perform poorly with many risk factors. Here, we propose a two-sample multivariable MR approach based on Bayesian model averaging (MR-BMA) that scales to high-throughput experiments. In a realistic simulation study, we show that MR-BMA can detect true causal risk factors even when the candidate risk factors are highly correlated. We illustrate MR-BMA by analysing publicly-available summarized data on metabolites to prioritise likely causal biomarkers for age-related macular degeneration

Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways

Background: Mendelian randomization uses genetic variants, assumed to be instrumental variables for a particular exposure, to estimate the causal effect of that exposure on an outcome. If the instrumental variable criteria are satisfied, the resulting estimator is consistent even in the presence of unmeasured confounding and reverse causation. Methods: We extend the Mendelian randomization paradigm to investigate more complex networks of relationships between variables, in particular where some of the effect of an exposure on the outcome may operate through an intermediate variable (a mediator). If instrumental variables for the exposure and mediator are available, direct and indirect effects of the exposure on the outcome can be estimated, for example using either a regression-based method or structural equation models. The direction of effect between the exposure and a possible mediator can also be assessed. Methods are illustrated in an applied example considering causal relationships between body mass index, C-reactive protein and uric acid. Results: These estimators are consistent in the presence of unmeasured confounding if, in addition to the instrumental variable assumptions, the effects of both the exposure on the mediator and the mediator on the outcome are homogeneous across individuals and linear without interactions. Nevertheless, a simulation study demonstrates that even considerable heterogeneity in these effects does not lead to bias in the estimates. Conclusions: These methods can be used to estimate direct and indirect causal effects in a mediation setting, and have potential for the investigation of more complex networks between multiple interrelated exposures and disease outcomes

Network Mendelian randomization: using genetic variants as instrumental variables to investigate mediation in causal pathways.

BACKGROUND: Mendelian randomization uses genetic variants, assumed to be instrumental variables for a particular exposure, to estimate the causal effect of that exposure on an outcome. If the instrumental variable criteria are satisfied, the resulting estimator is consistent even in the presence of unmeasured confounding and reverse causation. METHODS: We extend the Mendelian randomization paradigm to investigate more complex networks of relationships between variables, in particular where some of the effect of an exposure on the outcome may operate through an intermediate variable (a mediator). If instrumental variables for the exposure and mediator are available, direct and indirect effects of the exposure on the outcome can be estimated, for example using either a regression-based method or structural equation models. The direction of effect between the exposure and a possible mediator can also be assessed. Methods are illustrated in an applied example considering causal relationships between body mass index, C-reactive protein and uric acid. RESULTS: These estimators are consistent in the presence of unmeasured confounding if, in addition to the instrumental variable assumptions, the effects of both the exposure on the mediator and the mediator on the outcome are homogeneous across individuals and linear without interactions. Nevertheless, a simulation study demonstrates that even considerable heterogeneity in these effects does not lead to bias in the estimates. CONCLUSIONS: These methods can be used to estimate direct and indirect causal effects in a mediation setting, and have potential for the investigation of more complex networks between multiple interrelated exposures and disease outcomes

Beyond Mendelian randomization: how to interpret evidence of shared genetic predictors.

OBJECTIVE: Mendelian randomization is a popular technique for assessing and estimating the causal effects of risk factors. If genetic variants which are instrumental variables for a risk factor are shown to be additionally associated with a disease outcome, then the risk factor is a cause of the disease. However, in many cases, the instrumental variable assumptions are not plausible, or are in doubt. In this paper, we provide a theoretical classification of scenarios in which a causal conclusion is justified or not justified, and discuss the interpretation of causal effect estimates. RESULTS: A list of guidelines based on the 'Bradford Hill criteria' for judging the plausibility of a causal finding from an applied Mendelian randomization study is provided. We also give a framework for performing and interpreting investigations performed in the style of Mendelian randomization, but where the choice of genetic variants is statistically, rather than biologically motivated. Such analyses should not be assigned the same evidential weight as a Mendelian randomization investigation. CONCLUSION: We discuss the role of such investigations (in the style of Mendelian randomization), and what they add to our understanding of potential causal mechanisms. If the genetic variants are selected solely according to statistical criteria, and the biological roles of genetic variants are not investigated, this may be little more than what can be learned from a well-designed classical observational study.Stephen Burgess is supported by the Wellcome Trust (grant number 100114).This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.jclinepi.2015.08.00

Lipoprotein(a) in Alzheimer, Atherosclerotic, Cerebrovascular, Thrombotic, and Valvular Disease: Mendelian Randomization Investigation.

Lipoprotein(a) (Lp[a]) is a circulating lipoprotein with proatherogenic, proinflammatory, and possibly prothrombotic properties. Circulating Lp(a) levels are largely genetically determined, in particular, by the LPA gene. As such, genetic variants at the LPA locus can serve as instrumental variables for investigating the clinical effects of circulating Lp(a) levels. Mendelian randomization (MR) studies have shown that elevated Lp(a) levels are associated with a higher risk of coronary artery disease1–3 and aortic valve stenosis.2–4 Evidence on the causal role of elevated Lp(a) levels for other atherosclerotic and specific valvular diseases is limited, although there are MR data supporting a positive association between genetically predicted Lp(a) levels and peripheral artery disease.2,3 Whether Lp(a) is causally related to thrombotic disease and cerebrovascular disease remains unclear.2,3,5 In this study, we used the UK Biobank cohort to perform an MR investigation into the causal effects of circulating Lp(a) levels on atherosclerotic, cerebrovascular, thrombotic, and valvular diseases. Because a recent MR study provided evidence of an inverse association of Lp(a) levels with Alzheimer disease,5 we additionally explored whether genetically predicted Lp(a) levels are associated with Alzheimer disease and dementia.Dr Larsson receives support from the Swedish Heart-Lung Foundation (Hjärt-Lungfonden, grant number 20190247), the Swedish Research Council (Vetenskapsrådet, grant number 2019-00977), and the Swedish Research Council for Health, Working Life and Welfare (Forte, grant number 2018-00123). Dr Gill is funded by the Wellcome 4i Clinical PhD Program at Imperial College London. Dr Burgess is supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (award number 204623/Z/16/Z). Drs Burgess and Butterworth report funding from Novartis relating to the investigation of lipoprotein(a). The funder had no influence on the content of the investigation or the decision to publish. This work was supported by core funding from the UK Medical Research Council (MR/L003120/1), the British Heart Foundation (RG/13/13/30194; RG/18/13/33946), the National Institute for Health Research [Cambridge Biomedical Research Centre at the Cambridge University Hospitals NHS Foundation Trust] and Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation and Wellcome