The formal approach to quantitative causal inference in epidemiology: misguided or misrepresented?

Two recent articles, one by Vandenbroucke, Broadbent and Pearce (henceforth VBP) and the other by Krieger and Davey Smith (henceforth KDS), criticize what these two sets of authors characterize as the mainstream of the modern ‘causal inference’ school in epidemiology. The criticisms made by these authors are severe; VBP label the field both ‘wrong in theory’ and ‘wrong in practice’, and KDS—at least in some settings—feel that the field not only ‘bark[s] up the wrong tree’ but ‘miss[es] the forest entirely’. More specifically, the school of thought, and the concepts and methods within it, are painted as being applicable only to a very narrow range of investigations, to the exclusion of most of the important questions and study designs in modern epidemiology, such as the effects of genetic variants, the study of ethnic and gender disparities and the use of study designs that do not closely mirror randomized controlled trials (RCTs). Furthermore, the concepts and methods are painted as being potentially highly misleading even within this narrow range in which they are deemed applicable. We believe that most of VBP’s and KDS’s criticisms stem from a series of misconceptions about the approach they criticize. In this response, therefore, we aim first to paint a more accurate picture of the formal causal inference approach, and then to outline the key misconceptions underlying VBP’s and KDS’s critiques. KDS in particular criticize directed acyclic graphs (DAGs), using three examples to do so. Their discussion highlights further misconceptions concerning the role of DAGs in causal inference, and so we devote the third section of the paper to addressing these. In our Discussion we present further objections we have to the arguments in the two papers, before concluding that the clarity gained from adopting a rigorous framework is an asset, not an obstacle, to answering more reliably a very wide range of causal questions using data from observational studies of many different designs

An Assessment and Extension of the Mechanism-Based Approach to the Identification of Age-Period-Cohort Models.

: Many methods have been proposed to solve the age-period-cohort (APC) linear identification problem, but most are not theoretically informed and may lead to biased estimators of APC effects. One exception is the mechanism-based approach recently proposed and based on Pearl's front-door criterion; this approach ensures consistent APC effect estimators in the presence of a complete set of intermediate variables between one of age, period, cohort, and the outcome of interest, as long as the assumed parametric models for all the relevant causal pathways are correct. Through a simulation study mimicking APC data on cardiovascular mortality, we demonstrate possible pitfalls that users of the mechanism-based approach may encounter under realistic conditions: namely, when (1) the set of available intermediate variables is incomplete, (2) intermediate variables are affected by two or more of the APC variables (while this feature is not acknowledged in the analysis), and (3) unaccounted confounding is present between intermediate variables and the outcome. Furthermore, we show how the mechanism-based approach can be extended beyond the originally proposed linear and probit regression models to incorporate all generalized linear models, as well as nonlinearities in the predictors, using Monte Carlo simulation. Based on the observed biases resulting from departures from underlying assumptions, we formulate guidelines for the application of the mechanism-based approach (extended or not).<br/

Causal mediation analysis with multiple mediators.

In diverse fields of empirical research-including many in the biological sciences-attempts are made to decompose the effect of an exposure on an outcome into its effects via a number of different pathways. For example, we may wish to separate the effect of heavy alcohol consumption on systolic blood pressure (SBP) into effects via body mass index (BMI), via gamma-glutamyl transpeptidase (GGT), and via other pathways. Much progress has been made, mainly due to contributions from the field of causal inference, in understanding the precise nature of statistical estimands that capture such intuitive effects, the assumptions under which they can be identified, and statistical methods for doing so. These contributions have focused almost entirely on settings with a single mediator, or a set of mediators considered en bloc; in many applications, however, researchers attempt a much more ambitious decomposition into numerous path-specific effects through many mediators. In this article, we give counterfactual definitions of such path-specific estimands in settings with multiple mediators, when earlier mediators may affect later ones, showing that there are many ways in which decomposition can be done. We discuss the strong assumptions under which the effects are identified, suggesting a sensitivity analysis approach when a particular subset of the assumptions cannot be justified. These ideas are illustrated using data on alcohol consumption, SBP, BMI, and GGT from the Izhevsk Family Study. We aim to bridge the gap from "single mediator theory" to "multiple mediator practice," highlighting the ambitious nature of this endeavor and giving practical suggestions on how to proceed

Causal mediation analysis with multiple mediators

In diverse fields of empirical research - including many in the biological sciences - attempts are made to decompose the effect of an exposure on an outcome into its effects via a number of different pathways. For example, we may wish to separate the effect of heavy alcohol consumption on systolic blood pressure (SBP) into effects via body mass index (BMI), via gamma-glutamyl transpeptidase (GGT), and via other pathways. Much progress has been made, mainly due to contributions from the field of causal inference, in understanding the precise nature of statistical estimands that capture such intuitive effects, the assumptions under which they can be identified, and statistical methods for doing so. These contributions have focused almost entirely on settings with a single mediator, or a set of mediators considered en bloc; in many applications, however, researchers attempt a much more ambitious decomposition into numerous path-specific effects through many mediators. In this article, we give counterfactual definitions of such path-specific estimands in settings with multiple mediators, when earlier mediators may affect later ones, showing that there are many ways in which decomposition can be done. We discuss the strong assumptions under which the effects are identified, suggesting a sensitivity analysis approach when a particular subset of the assumptions cannot be justified. These ideas are illustrated using data on alcohol consumption, SBP, BMI, and GGT from the Izhevsk Family Study. We aim to bridge the gap from single mediator theory to multiple mediator practice, highlighting the ambitious nature of this endeavor and giving practical suggestions on how to proceed

Commentary: Incorporating concepts and methods from causal inference into life course epidemiology

The review by Ben-Shlomo et al.1 highlights how life course epidemiology is evolving and adapting to accommodate increasing access to data on novel dimensions and over extended periods. This enriched framework raises ever greater methodological challenges, leaving statisticians like us daunted by the task of translating life course enquiries into suitable analyses of the data at hand. Take for example Figure 4 of Ben-Shlomo et al..1 This is very useful for gaining a ‘big picture’ understanding of a complex area such as ageing, and for establishing which processes may benefit from a more detailed investigation. However, the leap from such a diagram to a specific data analysis should not be (and is not typically) made without greater thought. We will argue in this commentary that some recent developments from the field of modern causal inference may be helpful in this regard. First, in order to state unambiguously the question (or questions) of interest, the potential outcomes framework, a cornerstone of modern causal inference thinking, is invaluable. Then, the conceptual framework should be refined to a causal directed acyclic graph (DAG) relevant to the question, and the causal DAG should be formally interrogated to see if the question can be addressed, and if so how. Indeed, depending on the question, the causal DAG and the data available, we may find that standard statistical methods traditionally used in epidemiology are sufficient; in other settings we may find that more novel techniques are needed. We will discuss each of these points next, mentioning also the issues of missing data and measurement error, as well as highlighting concerns about the difference between the processes which are the focus of investigations and their manifestations in observed data

The depression in visual impairment trial (DEPVIT): trial design and protocol

&lt;b&gt;Background&lt;/b&gt; The prevalence of depression in people with a visual disability is high but screening for depression and referral for treatment is not yet an integral part of visual rehabilitation service provision. One reason for this may be that there is no good evidence about the effectiveness of treatments in this patient group. This study is the first to evaluate the effect of depression treatments on people with a visual impairment and co morbid depression.&lt;p&gt;&lt;/p&gt; &lt;b&gt;Methods/design&lt;/b&gt; The study is an exploratory, multicentre, individually randomised waiting list controlled trial. Participants will be randomised to receive Problem Solving Therapy (PST), a ‘referral to the GP’ requesting treatment according to the NICE’s ‘stepped care’ recommendations or the waiting list arm of the trial. The primary outcome measure is change (from randomisation) in depressive symptoms as measured by the Beck’s Depression Inventory (BDI-II) at 6 months. Secondary outcomes include change in depressive symptoms at 3 months, change in visual function as measured with the near vision subscale of the VFQ-48 and 7 item NEI-VFQ at 3 and 6 months, change in generic health related quality of life (EQ5D), the costs associated with PST, estimates of incremental cost effectiveness, and recruitment rate estimation.&lt;p&gt;&lt;/p&gt; &lt;b&gt;Discussion&lt;/b&gt; Depression is prevalent in people with disabling visual impairment. This exploratory study will establish depression screening and referral for treatment in visual rehabilitation clinics in the UK. It will be the first to explore the efficacy of PST and the effectiveness of NICE’s ‘stepped care’ approach to the treatment of depression in people with a visual impairment.&lt;p&gt;&lt;/p&gt

Acquisition of functions on the outer capsid surface during evolution of double-stranded RNA fungal viruses

Unlike their counterparts in bacterial and higher eukaryotic hosts, most fungal viruses are transmitted intracellularly and lack an extracellular phase. Here we determined the cryo-EM structure at 3.7 Å resolution of Rosellinia necatrix quadrivirus 1 (RnQV1), a fungal double-stranded (ds)RNA virus. RnQV1, the type species of the family Quadriviridae, has a multipartite genome consisting of four monocistronic segments. Whereas most dsRNA virus capsids are based on dimers of a single protein, the ~450-Å-diameter, T = 1 RnQV1 capsid is built of P2 and P4 protein heterodimers, each with more than 1000 residues. Despite a lack of sequence similarity between the two proteins, they have a similar α-helical domain, the structural signature shared with the lineage of the dsRNA bluetongue virus-like viruses. Domain insertions in P2 and P4 preferential sites provide additional functions at the capsid outer surface, probably related to enzyme activity. The P2 insertion has a fold similar to that of gelsolin and profilin, two actin-binding proteins with a function in cytoskeleton metabolism, whereas the P4 insertion suggests protease activity involved in cleavage of the P2 383-residue C-terminal region, absent in the mature viral particle. Our results indicate that the intimate virus-fungus partnership has altered the capsid genome-protective and/or receptor-binding functions. Fungal virus evolution has tended to allocate enzyme activities to the virus capsid outer surface

Matched increases in cerebral artery shear stress, irrespective of stimulus, induce similar changes in extra-cranial arterial diameter in humans.

The mechanistic role of arterial shear stress in the regulation of cerebrovascular responses to physiological stimuli (exercise and hypercapnia) is poorly understood. We hypothesised that, if shear stress is a key regulator of arterial dilation, then matched increases in shear, induced by distinct physiological stimuli, would trigger similar dilation of the large extra-cranial arteries. Participants ( n = 10) participated in three 30-min experimental interventions, each separated by ≥48 h: (1) mild-hypercapnia (FICO2:∼0.045); (2) submaximal cycling (EX; 60%HRreserve); or (3) resting (time-matched control, CTRL). Blood flow, diameter, and shear rate were assessed (via Duplex ultrasound) in the internal carotid and vertebral arteries (ICA, VA) at baseline, during and following the interventions. Hypercapnia and EX produced similar elevations in blood flow and shear rate through the ICA and VA ( p < 0.001), which were both greater than CTRL. Vasodilation of ICA and VA diameter in response to hypercapnia (5.3 ± 0.8 and 4.4 ± 2.0%) and EX (4.7 ± 0.7 and 4.7 ± 2.2%) were similar, and greater than CTRL ( p < 0.001). Our findings indicate that matched levels of shear, irrespective of their driving stimulus, induce similar extra-cranial artery dilation. We demonstrate, for the first time in humans, an important mechanistic role for the endothelium in regulating cerebrovascular response to common physiological stimuli in vivo

The Trans-Heliospheric Survey

CONTEXT: Though the solar wind is characterized by spatial and temporal variability across a wide range of scales, long-term averages of in situ measurements have revealed clear radial trends: changes in average values of basic plasma parameters (e.g., density, temperature, and speed) and a magnetic field with a distance from the Sun. AIMS: To establish our current understanding of the solar wind's average expansion through the heliosphere, data from multiple spacecraft needed to be combined and standardized into a single dataset. METHODS: In this study, data from twelve heliospheric and planetary spacecraft - Parker Solar Probe (PSP), Helios 1 and 2, Mariner 2 and 10, Ulysses, Cassini, Pioneer 10 and 11, New Horizons, and Voyager 1 and 2 - were compiled into a dataset spanning over three orders of magnitude in heliocentric distance. To avoid introducing artifacts into this composite dataset, special attention was given to the solar cycle, spacecraft heliocentric elevation, and instrument calibration. RESULTS: The radial trend in each parameter was found to be generally well described by a power-law fit, though up to two break points were identified in each fit. CONCLUSIONS: These radial trends are publicly released here to benefit research groups in the validation of global heliospheric simulations and in the development of new deep-space missions such as Interstellar Probe