Identification of Causal Effects on Binary Outcomes Using Structural Mean Models

Structural mean models (SMMs) are used to estimate causal effects among those selecting treatment in randomised controlled trials affected by non-ignorable non-compliance. These causal effects can be identified by assuming that there is no effect modification, namely, that the causal effect is equal for the treated subgroups randomised to treatment and to control. By analysing simple structural models for binary outcomes, we argue that the no effect modification assumption does not hold in general, and so SMMs do not estimate causal effects for the treated. An exception is for designs in which those randomised to control can be completely excluded from receiving the treatment. However, when there is non-compliance in the control arm, local (or complier) causal effects can be identified provided that the further assumption of monotonic selection into treatment holds. We demonstrate these issues using numerical examples.structural mean models, identification, local average treatment effects, complier average treatment effects

Instrumental Variable Estimators for Binary Outcomes

The estimation of exposure effects on study outcomes is almost always complicated by non-random exposure selection - even randomised controlled trials can be affected by participant non-compliance. If the selection mechanism is non-ignorable then inferences based on estimators that fail to adjust for its effects will be misleading. Potentially consistent estimators of the exposure effect can be obtained if the data are expanded to include one or more instrumental variables (IVs). An IV must satisfy core conditions constraining it to be associated with the exposure, and indirectly (but not directly) associated with the outcome through this association. Here we consider IV estimators for studies in which the outcome is represented by a binary variable. While work on this problem has been carried out in statistics and econometrics, the estimators and their associated identifying assumptions have existed in the separate domains of structural models and potential outcomes with almost no overlap. In this paper, we review and integrate the work in these areas and reassess the issues of parameter identification and estimator consistency. Identification of maximum likelihood estimators comes from strong parametric modelling assumptions, with consistency depending on these assumptions being correct. Our main focus is on three semi-parametric estimators based on the generalised method of moments, marginal structural models and structural mean models (SMM). By inspecting the identifying assumptions for each method, we show that these estimators are inconsistent even if the true model generating the data is simple, and argue that this implies that consistency is obtained only under implausible conditions. Identification for SMMs can also be obtained under strong exposure-restricting design constraints that are often appropriate for randomised controlled trials, but not for observational studies. Finally, while estimation of local causal parameters is possible if the selection mechanism is monotonic, not all SMMs identify a local parameter.Econometrics, Generalized methods of moments, Parameter identification, Marginal structural models, Structural mean models, Structural models

Instrumental Variable Estimators for Binary Outcomes

Instrumental variables (IVs) can be used to construct estimators of exposure effects on the outcomes of studies affected by non-ignorable selection of the exposure. Estimators which fail to adjust for the effects of non-ignorable selection will be biased and inconsistent. Such situations commonly arise in observational studies, but even randomised controlled trials can be affected by non-ignorable participant non-compliance. In this paper, we review IV estimators for studies in which the outcome is binary. Recent work on identification is interpreted using an integrated structural modelling and potential outcomes framework, within which we consider the links between different approaches developed in statistics and econometrics. The implicit assumptions required for bounding causal effects and point-identification by each estimator are highlighted and compared within our framework. Finally, the implications for practice are discussed.bounds, causal inference, generalized method of moments, local average treatment effects, marginal structural models, non-compliance, parameter identification, potential outcomes, structural mean models, structural models

Estimating structural mean models with multiple instrumental variables using the generalised method of moments

Instrumental variables analysis using genetic markers as instruments is now a widely used technique in epidemiology and biostatistics. As single markers tend to explain only a small proportion of phenotypical variation, there is increasing interest in using multiple genetic markers to obtain more precise estimates of causal parameters. Structural mean models (SMMs) are semi-parametric models that use instrumental variables to identify causal parameters, but there has been little work on using these models with multiple instruments, particularly for multiplicative and logistic SMMs. In this paper, we show how additive, multiplicative and logistic SMMs with multiple discrete instrumental variables can be estimated efficiently using the generalised method of moments (GMM) estimator, how the Hansen J-test can be used to test for model mis-specification, and how standard GMM software routines can be used to fit SMMs. We further show that multiplicative SMMs, like the additive SMM, identify a weighted average of local causal effects if selection is monotonic. We use these methods to reanalyse a study of the relationship between adiposity and hypertension using SMMs with two genetic markers as instruments for adiposity. We find strong effects of adiposity on hypertension, but no evidence of unobserved confounding.

Estimating Structural Mean Models with Multiple Instrumental Variables using the Generalised Method of Moments

Instrumental variables analysis using genetic markers as instruments is now a widely used technique in epidemiology and biostatistics. As single markers tend to explain only a small proportion of phenotypical variation, there is increasing interest in using multiple genetic markers to obtain more precise estimates of causal parameters. Structural mean models (SMMs) are semi-parametric models that use instrumental variables to identify causal parameters, but there has been little work on using these models with multiple instruments, particularly for multiplicative and logistic SMMs. In this paper, we show how additive, multiplicative and logistic SMMs with multiple discrete instrumental variables can be estimated efficiently using the generalised method of moments (GMM) estimator, how the Hansen J-test can be used to test for model mis-specification, and how standard GMM software routines can be used to fit SMMs. We further show that multiplicative SMMs, like the additive SMM, identify a weighted average of local causal effects if selection is monotonic. We use these methods to reanalyse a study of the relationship between adiposity and hypertension using SMMs with two genetic markers as instruments for adiposity. We find strong effects of adiposity on hypertension, but no evidence of unobserved confounding.Structural Mean Models, Multiple Instrumental Variables, Generalised Method of Moments, Mendelian Randomisation, Local Average Treatment Effects

Identification of an actin binding region in aldolase

AbstractFragmentation of the actin binding glycolytic enzyme, aldolase, with cyanogen bromide yields an 18K actin binding fragment which corresponds to residues 1–164 of the aldolase sequence. Within this fragment there is a region of sequence (residues 32–52) which is highly homologous to a region of sequence near the C-terminus of actin itself and which is also found in the actin binding domains of a number of other actin binding proteins. A synthetic peptide corresponding to the aldolase sequence 32–52 encompassing this region of homology binds to F-actin and specifically competes with native aldolase for binding to this cytoskeletal protein

When History is Ignored: Business Black Swans and the Use and Abuse of a Notion

Historical enquiry reveals how ideas mutate. This account of the ideas underpinning how fair value accounting (FVA) drifted into corporate financial reporting shows that a primary lesson of business history is that we ignore history at our peril, that frequently we encourage the recall of history for possibly the wrong reason – to supposedly ‘learn lessons’ regarding what we might or might not repeat. It might be more fruitful to use history to gain insight into the development of the ideas (good and bad) that delivered us to where we are. The case of fair value is shown to have drifted from the basis for a specific purpose calculation into a general application in accounting statements of financial position and financial performance. The Mark-to-Market (MtM) dispute during the current global financial crisis has nurtured further mutation of its FVA predecessor. What originally arose as an attempt to disclose a present financial state or condition is being denied by many in the name of the alleged virtue of hiding it. Doing so contradicts what history tells us has been the focus from when fair value accidentally ‘drifted’ into the accounting for adaptive companies. Our analysis also highlights historical enquiry aptly showing how accounting is conducive to politicization – an easy victim of interested parties’ special pleading, corrupting its technology function primarily because it is inconvenient to have accounting data tell it how it is.The symposium is organised on behalf of AAHANZBS by the Business and Labour History Group, The University of Sydney, with the financial support of the University’s Faculty of Economics and Business

When History is Ignored: Business Black Swans and the Use and Abuse of a Notion

Historical enquiry reveals how ideas mutate. This account of the ideas underpinning how fair value accounting (FVA) drifted into corporate financial reporting shows that a primary lesson of business history is that we ignore history at our peril, that frequently we encourage the recall of history for possibly the wrong reason – to supposedly ‘learn lessons’ regarding what we might or might not repeat. It might be more fruitful to use history to gain insight into the development of the ideas (good and bad) that delivered us to where we are. The case of fair value is shown to have drifted from the basis for a specific purpose calculation into a general application in accounting statements of financial position and financial performance. The Mark-to-Market (MtM) dispute during the current global financial crisis has nurtured further mutation of its FVA predecessor. What originally arose as an attempt to disclose a present financial state or condition is being denied by many in the name of the alleged virtue of hiding it. Doing so contradicts what history tells us has been the focus from when fair value accidentally ‘drifted’ into the accounting for adaptive companies. Our analysis also highlights historical enquiry aptly showing how accounting is conducive to politicization – an easy victim of interested parties’ special pleading, corrupting its technology function primarily because it is inconvenient to have accounting data tell it how it is.The symposium is organised on behalf of AAHANZBS by the Business and Labour History Group, The University of Sydney, with the financial support of the University’s Faculty of Economics and Business