New tests of the pp-wave correspondence.

The pp-wave/SYM correspondence is an equivalence relation, H string = Δ-J , between the hamiltonian H string of string field theory in the pp-wave background and the dilatation operator Δ in = 4 Super Yang-Mills in the double scaling limit. We calculate matrix elements of these operators in string field theory and in gauge theory. In the string theory Hilbert space we use the natural string basis, and in the gauge theory we use the basis which is isomorphic to it. States in this basis are specific linear combinations of the original BMN operators, and were constructed previously for the case of two scalar impurities. We extend this construction to incorporate BMN operators with vector and mixed impurities. This enables us to verify from the gauge theory perspective two key properties of the three-string interaction vertex of Spradlin and Volovich: (1) the vanishing of the three-string amplitude for string states with one vector and one scalar impurity; and (2) the relative minus sign in the string amplitude involving states with two vector impurities compared to that with two scalar impurities. This implies a spontaneous breaking of the 2 symmetry of the string field theory in the pp-wave background. Furthermore, we calculate the gauge theory matrix elements of Δ-J for states with an arbitrary number of scalar impurities. In all cases we find perfect agreement with the corresponding string amplitudes derived from the three-string vertex

Advanced Modelling Strategies: Challenges and pitfalls in robust causal inference with observational data

Advanced Modelling Strategies: Challenges and pitfalls in robust causal inference with observational data summarises the lecture notes prepared for a four-day workshop sponsored by the Society for Social Medicine and hosted by the Leeds Institute for Data Analytics (LIDA) at the University of Leeds on 17th-20th July 2017

Intervention differential effects and regression to the mean in studies where sample selection is based on the initial value of the outcome variable: an evaluation of methods illustrated in weight-management studies

© 2020, © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. Background: Intervention differential effects (IDEs) occur where changes in an outcome depend upon the initial values of that outcome. Although methods to identify IDEs are well documented, there remains a lack of understanding about the circumstances under which these methods are robust. One context that has not been explored is the identification of intervention differential effect in studies where sample selection is based on the initial value of the outcome being evaluated. We hypothesise that, in such settings, established methods for detecting IDEs will struggle to discriminate these from regression to the mean. Methods: Using simulated datasets of weight-loss intervention programmes that recruit according to initial body mass index, we explore the reliability of Oldham's method and multilevel modelling (MLM) to detect IDEs. Results: In datasets simulated with no IDE, Oldham's method and MLM yield Type I error rates >90%, confirming that threshold selection/truncation leads to bias due to regression to the mean. Type I error rates return close to 5% for both methods when a control group is introduced. Conclusions: Oldham's method and MLM can robustly detect IDEs in this setting, but only if analyses incorporate a control group for comparison

COVID-19 and the epistemology of epidemiological models at the dawn of AI

The models used to estimate disease transmission, susceptibility and severity determine what epidemiology can (and cannot tell) us about COVID-19. These include: ‘model organisms’ chosen for their phylogenetic/aetiological similarities; multivariable statistical models to estimate the strength/direction of (potentially causal) relationships between variables (through ‘causal inference’), and the (past/future) value of unmeasured variables (through ‘classification/prediction’); and a range of modelling techniques to predict beyond the available data (through ‘extrapolation’), compare different hypothetical scenarios (through ‘simulation’), and estimate key features of dynamic processes (through ‘projection’). Each of these models: address different questions using different techniques; involve assumptions that require careful assessment; and are vulnerable to generic and specific biases that can undermine the validity and interpretation of their findings. It is therefore necessary that the models used: can actually address the questions posed; and have been competently applied. In this regard, it is important to stress that extrapolation, simulation and projection cannot offer accurate predictions of future events when the underlying mechanisms (and the contexts involved) are poorly understood and subject to change. Given the importance of understanding such mechanisms/contexts, and the limited opportunity for experimentation during outbreaks of novel diseases, the use of multivariable statistical models to estimate the strength/direction of potentially causal relationships between two variables (and the biases incurred through their misapplication/misinterpretation) warrant particular attention. Such models must be carefully designed to address: ‘selection-collider bias’, ‘unadjusted confounding bias’ and ‘inferential mediator adjustment bias’ – all of which can introduce effects capable of enhancing, masking or reversing the estimated (true) causal relationship between the two variables examined. Selection-collider bias occurs when these two variables independently cause a third (the ‘collider’), and when this collider determines/reflects the basis for selection in the analysis. It is likely to affect all incompletely representative samples, although its effects will be most pronounced wherever selection is constrained (e.g. analyses focusing on infected/hospitalised individuals). Unadjusted confounding bias disrupts the estimated (true) causal relationship between two variables when: these share one (or more) common cause(s); and when the effects of these causes have not been adjusted for in the analyses (e.g. whenever confounders are unknown/unmeasured). Inferentially similar biases can occur when: one (or more) variable(s) (or ‘mediators’) fall on the causal path between the two variables examined (i.e. when such mediators are caused by one of the variables and are causes of the other); and when these mediators are adjusted for in the analysis. Such adjustment is commonplace when: mediators are mistaken for confounders; prediction models are mistakenly repurposed for causal inference; or mediator adjustment is used to estimate direct and indirect causal relationships (in a mistaken attempt at ‘mediation analysis’). These three biases are central to ongoing and unresolved epistemological tensions within epidemiology. All have substantive implications for our understanding of COVID-19, and the future application of artificial intelligence to ‘data-driven’ modelling of similar phenomena. Nonetheless, competently applied and carefully interpreted, multivariable statistical models may yet provide sufficient insight into mechanisms and contexts to permit more accurate projections of future disease outbreaks. 1. These biases, and the terminology involved, may be challenging to readers who are unfamiliar with the use of causal path diagrams (such as Directed Acyclic Graphs; DAGs) which have been instrumental in identifying the different roles that variables can play in causal processes (whether as ‘exposures’, ‘outcomes’, ‘confounders’, ‘mediators’, ‘colliders’, ‘competing exposures’ or ‘consequences of the outcome’) and revealing hitherto under-acknowledged sources of bias in analyses designed to support causal inference. For what we hoped might offer accessible introductions to DAGs (and how [not] to use these) please see: Ellison (2020); and Tennant et al. (2019). For more technical detail on ‘collider bias’, ‘unadjusted confounding bias’ and ‘inferential mediator adjustment bias’ (and its related concern, the ‘Table 2 fallacy’), please refer to: Cook and Ranstam 2017; Munafò et al. (2018); Tennant et al. (2017); VanderWeele and Arah (2011); and Westreich and Greenland (2013)

Cardiovascular disease in a cohort exposed to the 1940-45 Channel Islands occupation

BACKGROUND To clarify the nature of the relationship between food deprivation/undernutrition during pre- and postnatal development and cardiovascular disease (CVD) in later life, this study examined the relationship between birth weight (as a marker of prenatal nutrition) and the incidence of hospital admissions for CVD from 1997–2005 amongst 873 Guernsey islanders (born in 1923–1937), 225 of whom had been exposed to food deprivation as children, adolescents or young adults (i.e. postnatal undernutrition) during the 1940–45 German occupation of the Channel Islands, and 648 of whom had left or been evacuated from the islands before the occupation began. METHODS Three sets of Cox regression models were used to investigate (A) the relationship between birth weight and CVD, (B) the relationship between postnatal exposure to the occupation and CVD and (C) any interaction between birth weight, postnatal exposure to the occupation and CVD. These models also tested for any interactions between birth weight and sex, and postnatal exposure to the occupation and parish of residence at birth (as a marker of parish residence during the occupation and related variation in the severity of food deprivation). RESULTS The first set of models (A) found no relationship between birth weight and CVD even after adjustment for potential confounders (hazard ratio (HR) per kg increase in birth weight: 1.12; 95% confidence intervals (CI): 0.70 – 1.78), and there was no significant interaction between birth weight and sex (p = 0.60). The second set of models (B) found a significant relationship between postnatal exposure to the occupation and CVD after adjustment for potential confounders (HR for exposed vs. unexposed group: 2.52; 95% CI: 1.54 – 4.13), as well as a significant interaction between postnatal exposure to the occupation and parish of residence at birth (p = 0.01), such that those born in urban parishes (where food deprivation was worst) had a greater HR for CVD than those born in rural parishes. The third model (C) found no interaction between birth weight and exposure to the occupation (p = 0.43). CONCLUSION These findings suggest that the levels of postnatal undernutrition experienced by children, adolescents and young adults exposed to food deprivation during the 1940–45 occupation of the Channel Islands were a more important determinant of CVD in later life than the levels of prenatal undernutrition experienced in utero prior to the occupatio

The Authors Respond

Although the studies highlighted in Kinlen and Peto’s letter describe situations they take to be “national in scope”, none of these adopted the ‘region-wide’ analysis we recommend. Rather, these studies have focussed on rural areas with small populations experiencing extreme levels of inward-migration that had been selected from larger regions/nation states. To definitively avoid bias, our study points to the need for comparisons of areas with varying levels of inward migration, either by comparing all areas within an entire region/nation state or random subsets thereof

Economic vulnerability and poor service delivery made it more difficult for shack-dwellers to comply with COVID-19 restrictions : the impracticability and inequitable burden of universal/unstratified public health policies

In South Africa, demand for housing close to viable/sustained sources of employment has far outstripped supply; and the size of the population living in temporary structures/shacks (and in poorly serviced informal settlements) has continued to increase. While such dwellings and settlements pose a number of established risks to the health of their residents, the present study aimed to explore whether they might also undermine the potential impact of regulations intended to safeguard public health, such as the stringent lockdown restrictions imposed to curb the spread of COVID-19 in 2020 and 2021. Using a representative sample of 1,381 South African households surveyed in May-June 2021, the present study found that respondents in temporary structures/shacks were more likely to report non-compliance (or difficulty in complying) with lockdown restrictions when compared to those living in traditional/formal houses/flats/rooms/hostels (OR:1.61; 95%CI:1.06-2.45). However, this finding was substantially attenuated and lost precision following adjustment for preceding sociodemographic and economic determinants of housing quality (adjusted OR:1.20; 95%CI:0.78-1.87). Instead, respondents were far more likely to report non-compliance (or difficulty in complying) with COVID-19 lockdown restrictions if their dwellings lacked private/indoor toilet facilities (adjusted OR:1.56; 95%CI:1.08,2.22) or they were ‘Black/African’, young, poorly educated and under-employed (regardless of: their socioeconomic position, or whether they resided in temporary structures/shacks, respectively). Restrictions imposed to safeguard public health need to be more sensitively designed to accommodate the critical role that poverty and inadequate service delivery play in limiting the ability of residents living in temporary structures/shacks and inadequately serviced dwellings/settlements to comply

Adjustment for time-invariant and time-varying confounders in ‘unexplained residuals’ models for longitudinal data within a causal framework and associated challenges

‘Unexplained residuals’ models have been used within lifecourse epidemiology to model an exposure measured longitudinally at several time points in relation to a distal outcome. It has been claimed that these models have several advantages, including: the ability to estimate multiple total causal effects in a single model, and additional insight into the effect on the outcome of greater-than-expected increases in the exposure compared to traditional regression methods. We evaluate these properties and prove mathematically how adjustment for confounding variables must be made within this modelling framework. Importantly, we explicitly place unexplained residual models in a causal framework using directed acyclic graphs. This allows for theoretical justification of appropriate confounder adjustment and provides a framework for extending our results to more complex scenarios than those examined in this paper. We also discuss several interpretational issues relating to unexplained residual models within a causal framework. We argue that unexplained residual models offer no additional insights compared to traditional regression methods, and, in fact, are more challenging to implement; moreover, they artificially reduce estimated standard errors. Consequently, we conclude that unexplained residual models, if used, must be implemented with great care

Ethical issues in human genomics research in developing countries

<p>Abstract</p> <p>Background</p> <p>Genome-wide association studies (GWAS) provide a powerful means of identifying genetic variants that play a role in common diseases. Such studies present important ethical challenges. An increasing number of GWAS is taking place in lower income countries and there is a pressing need to identify the particular ethical challenges arising in such contexts. In this paper, we draw upon the experiences of the MalariaGEN Consortium to identify specific ethical issues raised by such research in Africa, Asia and Oceania.</p> <p>Discussion</p> <p>We explore ethical issues in three key areas: protecting the interests of research participants, regulation of international collaborative genomics research and protecting the interests of scientists in low income countries. With regard to participants, important challenges are raised about community consultation and consent. Genomics research raises ethical and governance issues about sample export and ownership, about the use of archived samples and about the complexity of reviewing such large international projects. In the context of protecting the interests of researchers in low income countries, we discuss aspects of data sharing and capacity building that need to be considered for sustainable and mutually beneficial collaborations.</p> <p>Summary</p> <p>Many ethical issues are raised when genomics research is conducted on populations that are characterised by lower average income and literacy levels, such as the populations included in MalariaGEN. It is important that such issues are appropriately addressed in such research. Our experience suggests that the ethical issues in genomics research can best be identified, analysed and addressed where ethics is embedded in the design and implementation of such research projects.</p