A new approach to hierarchical data analysis: Targeted maximum likelihood estimation for the causal effect of a cluster-level exposure

We often seek to estimate the impact of an exposure naturally occurring or randomly assigned at the cluster-level. For example, the literature on neighborhood determinants of health continues to grow. Likewise, community randomized trials are applied to learn about real-world implementation, sustainability, and population effects of interventions with proven individual-level efficacy. In these settings, individual-level outcomes are correlated due to shared cluster-level factors, including the exposure, as well as social or biological interactions between individuals. To flexibly and efficiently estimate the effect of a cluster-level exposure, we present two targeted maximum likelihood estimators (TMLEs). The first TMLE is developed under a non-parametric causal model, which allows for arbitrary interactions between individuals within a cluster. These interactions include direct transmission of the outcome (i.e. contagion) and influence of one individual's covariates on another's outcome (i.e. covariate interference). The second TMLE is developed under a causal sub-model assuming the cluster-level and individual-specific covariates are sufficient to control for confounding. Simulations compare the alternative estimators and illustrate the potential gains from pairing individual-level risk factors and outcomes during estimation, while avoiding unwarranted assumptions. Our results suggest that estimation under the sub-model can result in bias and misleading inference in an observational setting. Incorporating working assumptions during estimation is more robust than assuming they hold in the underlying causal model. We illustrate our approach with an application to HIV prevention and treatment

Safe use of symbols in handover documentation for medical teams

Concern has been reported about the safe use of medical abbreviations in documents such as handover sheets and medical notes, especially when information is being communicated between staff of different specialties (BBC 2008, Sheppard et al. 2008). This article describes a study to investigate whether the use of symbols in handover documentation that is shared within and between multidisciplinary teams (MDTs) has similar safety implications. We asked 19 healthcare professionals from a range of specialties to identify 45 different combinations of 38 individual symbols. The symbols and combinations of symbols were extracted from 102 handover sheets taken from 6 different healthcare contexts in 4 London hospitals. Three symbols proposed in Microsoft's Common User Interface guidelines for alert symbols were also included. Results reveal that while some symbols are well understood, many others are either ambiguous or unknown. These results have implications for the safe use of symbols in medical documents, including paper and electronic handover documents and Electronic Patient Records (EPRs), especially where teams comprise individuals from different professional backgrounds, i.e. MDTs. We offer initial suggestions for standardisation and further research

Ractopamine HCl improved cardiac hypertrophy but not poor growth, metabolic inefficiency, or greater white blood cells associated with heat stress in concentrate-fed lambs

Heat stress decreases livestock performance and well-being (Hahn, 1999; Nienaber and Hahn, 2007), causes metabolic dysfunction that decreases growth efficiency (O’Brien et al., 2010), and alters cardiovascular function (Crandall et al., 2008). Each year, heat stress costs the livestock industry up to $2.5 billion (St-Pierre et al., 2003). Ractopamine HCl acts as a nutrient repartitioning agent (Beermann, 2002); classified as a β adrenergic agonist (βAA), it shares pharmacological properties with adrenaline (Beermann, 2002). βAA increase muscle mass and decreases fat deposition through unknown mechanisms (Beermann, 2002). In feedlot cattle, they increase growth efficiency and improve carcass yield and merit (Scramlin et al., 2010; Buntyn et al., 2017), which increases profit and allows more meat to be produced from fewer animals. However, because βAA act via a stress system, it is unclear how the products affect animals under stress conditions. β1AA and β2AA can also cause tachycardia, heart palpitations, and arrhythmias (Sears, 2002). We hypothesize that β1AA combined with heat stress may overstimulate the adrenergic system, resulting is metabolic dysfunction and decreased performance. Sheep are a common model for cattle, and thus, the objective of this study was to determine the impact of ractopamine HCl on health and cardiovascular parameters, growth, and metabolic efficiency in feeder lambs

Deep Learning using K-space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection

Quality assessment of medical images is essential for complete automation of image processing pipelines. For large population studies such as the UK Biobank, artefacts such as those caused by heart motion are problematic and manual identification is tedious and time-consuming. Therefore, there is an urgent need for automatic image quality assessment techniques. In this paper, we propose a method to automatically detect the presence of motion-related artefacts in cardiac magnetic resonance (CMR) images. As this is a highly imbalanced classification problem (due to the high number of good quality images compared to the low number of images with motion artefacts), we propose a novel k-space based training data augmentation approach in order to address this problem. Our method is based on 3D spatio-temporal Convolutional Neural Networks, and is able to detect 2D+time short axis images with motion artefacts in less than 1ms. We test our algorithm on a subset of the UK Biobank dataset consisting of 3465 CMR images and achieve not only high accuracy in detection of motion artefacts, but also high precision and recall. We compare our approach to a range of state-of-the-art quality assessment methods.Comment: Accepted for MICCAI2018 Conferenc

A four-season prospective study of muscle strain reoccurrences in a professional football club

The aim of this investigation was to characterise muscle strain reinjuries and examine their impact on playing resources in a professional football club. Muscle strains and reoccurrences were prospectively diagnosed over four seasons in first-team players (n = 46). Altogether, 188 muscle strains were diagnosed with 44 (23.4%) of these classed as reinjuries, leading to an incidence of 1.32 strain reoccurrences per 1,000 hours exposure (95% Confidence Interval [CI], 0.93–1.71). The incidence of recurrent strains was higher in match-play compared with training (4.51, 95% CI, 2.30–6.72 vs 0.94, 95% CI, 0.59–1.29). Altogether, 50.0% of players sustained at least 1 reoccurrence of a muscle strain, leading to approximately 3 days lost and 0.4 matches missed per player per season. The incidence of recurrent strains was highest in centre-forwards (2.15, 95% CI, 1.06–3.24), peaked in May (3.78, 95% CI, 0.47–7.09), and mostly affected the hamstrings (38.6% of all reoccurrences). Mean layoff for nonreoccurrences and recurrences was similar: ∼7.5 days. These results provide greater insight into the extent of the problem of recurrent muscle strains in professional football

Distinct nature of static and dynamic magnetic stripes in cuprate superconductors

We present detailed neutron scattering studies of the static and dynamic stripes in an optimally doped high-temperature superconductor, La$_2$CuO$_{4+y}$. We find that the dynamic stripes do not disperse towards the static stripes in the limit of vanishing energy transfer. We conclude that the dynamic stripes observed in neutron scattering experiments are not the Goldstone modes associated with the broken symmetry of the simultaneously observed static stripes, but rather that the signals originate from different domains in the sample. These domains may be related by structural twinning, or may be entirely different phases, where the static stripes in one phase are pinned versions of the dynamic stripes in the other. Our results explain earlier observations of unusual dispersions in underdoped La$_{2-x}$Sr$_x$CuO$_{4}$ ($x=0.07$) and La$_{2-x}$Ba$_x$CuO$_{4}$ ($x=0.095$). Our findings are relevant for all compounds exhibiting magnetic stripes, and may thus be a vital part in unveiling the nature of high temperature superconductivity

Targeted Estimation and Inference for the Sample Average Treatment Effect

While the population average treatment effect has been the subject of extensive methods and applied research, less consideration has been given to the sample average treatment effect: the mean difference in the counterfactual outcomes for the study units. The sample parameter is easily interpretable and is arguably the most relevant when the study units are not representative of a greater population or when the exposure\u27s impact is heterogeneous. Formally, the sample effect is not identifiable from the observed data distribution. Nonetheless, targeted maximum likelihood estimation (TMLE) can provide an asymptotically unbiased and efficient estimate of both the population and sample parameters. In this paper, we study the asymptotic and finite sample properties of the TMLE for the sample effect and provide a conservative variance estimator. In most settings, the sample parameter can be estimated more efficiently than the population parameter. Finite sample simulations illustrate the potential gains in precision and power from selecting the sample effect as the target of inference. As a motivating example, we discuss the Sustainable East Africa Research in Community Health (SEARCH) study, an ongoing cluster randomized trial for HIV prevention and treatment