Fetal alcohol spectrum disorder and attention deficit hyperactivity disorder stimulant trial in children: an N-of-1 pilot trial to compare stimulant to placebo (FASST): protocol.

INTRODUCTION: Fetal alcohol spectrum disorder (FASD) is a neurodevelopmental disorder caused by alcohol exposure during pregnancy. FASD is associated with neurodevelopmental deviations, and 50%-94% of children with FASD meet the Diagnostic and Statistical Manual of Mental Disorders-fifth edition diagnostic criteria for attention deficit hyperactivity disorder (ADHD). There is a paucity of evidence around medication efficacy for ADHD symptoms in children with FASD. This series of N-of-1 trials aims to provide pilot data on the feasibility of conducting N-of-1 trials in children with FASD and ADHD. METHODS AND ANALYSIS: A pilot N-of-1 randomised trial design with 20 cycles of stimulant and placebo (four cycles of 2-week duration) for each child will be conducted (n=20) in Melbourne, Australia.Feasibility and tolerability will be assessed using recruitment and retention rates, protocol adherence, adverse events and parent ratings of side effects. Each child's treatment effect will be determined by analysing teacher ADHD ratings across stimulant and placebo conditions (Wilcoxon rank). N-of-1 data will be aggregated to provide an estimate of the cohort treatment effect as well as individual-level treatment effects. We will assess the sample size and number of cycles required for a future trial. Potential mediating factors will be explored to identify variables that might be associated with treatment response variability. ETHICS AND DISSEMINATION: The study was approved by the Hospital and Health Service Human Research Ethics Committee (HREC/74678/MonH-2021-269029), Monash (protocol V6, 25 June 2023).Individual outcome data will be summarised and provided to participating carers and practitioners to enhance care. Group-level findings will be presented at a local workshop to engage stakeholders. Findings will be presented at national and international conferences and published in peer-reviewed journals. All results will be reported so that they can be used to inform prior information for future trials. TRIAL REGISTRATION NUMBER: NCT04968522.

Managing surgical waiting lists through dynamic priority scoring.

Prioritising elective surgery patients under the Australian three-category system is inherently subjective due to variability in clinician decision making and the potential for extraneous factors to influence category assignment. As a result, waiting time inequities can exist which may lead to adverse health outcomes and increased morbidity, especially for patients deemed to be low priority. This study investigated the use of a dynamic priority scoring (DPS) system to rank elective surgery patients more equitably, based on a combination of waiting time and clinical factors. Such a system enables patients to progress on the waiting list in a more objective and transparent manner, at a rate relative to their clinical need. Simulation results comparing the two systems indicate that the DPS system has potential to assist in managing waiting lists by standardising waiting times relative to urgency category, in addition to improving waiting time consistency for patients of similar clinical need. In clinical practice, this system is likely to reduce subjectivity, increase transparency, and improve overall efficiency of waiting list management by providing an objective metric to prioritise patients. Such a system is also likely to increase public trust and confidence in the systems used to manage waiting lists.

Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics.

Building on a strong foundation of philosophy, theory, methods and computation over the past three decades, Bayesian approaches are now an integral part of the toolkit for most statisticians and data scientists. Whether they are dedicated Bayesians or opportunistic users, applied professionals can now reap many of the benefits afforded by the Bayesian paradigm. In this paper, we touch on six modern opportunities and challenges in applied Bayesian statistics: intelligent data collection, new data sources, federated analysis, inference for implicit models, model transfer and purposeful software products. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.

Bayesian design for minimizing prediction uncertainty in bivariate spatial responses with applications to air quality monitoring.

Model-based geostatistical design involves the selection of locations to collect data to minimize an expected loss function over a set of all possible locations. The loss function is specified to reflect the aim of data collection, which, for geostatistical studies, could be to minimize the prediction uncertainty at unobserved locations. In this paper, we propose a new approach to design such studies via a loss function derived through considering the entropy about the model predictions and the parameters of the model. The approach includes a multivariate extension to generalized linear spatial models, and thus can be used to design experiments with more than one response. Unfortunately, evaluating our proposed loss function is computationally expensive so we provide an approximation such that our approach can be adopted to design realistically sized geostatistical studies. This is demonstrated through a simulated study and through designing an air quality monitoring program in Queensland, Australia. The results show that our designs remain highly efficient in achieving each experimental objective individually, providing an ideal compromise between the two objectives. Accordingly, we advocate that our approach could be adopted more generally in model-based geostatistical design.

Adaptive monitoring of coral health at Scott Reef where data exhibit nonlinear and disturbed trends over time.

Time series data are often observed in ecological monitoring. Frequently, such data exhibit nonlinear trends over time potentially due to complex relationships between observed and auxiliary variables, and there may also be sudden declines over time due to major disturbances. This poses substantial challenges for modeling such data and also for adaptive monitoring. To address this, we propose methods for finding adaptive designs for monitoring in such settings. This work is motivated by a monitoring program that has been established at Scott Reef; a coral reef off the Western coast of Australia. Data collected for monitoring the health of Scott Reef are considered, and semiparametric and interrupted time series modeling approaches are adopted to describe how these data vary over time. New methods are then proposed that enable adaptive monitoring designs to be found based on such modeling approaches. These methods are then applied to find future monitoring designs at Scott Reef where it was found that future information gain is expected to be similar across a variety of different sites, suggesting that no particular location needs to be prioritized at Scott Reef for the next monitoring phase. In addition, it was found that omitting some sampling sites/reef locations was possible without substantial loss in expected information gain, depending upon the disturbances that were observed. The resulting adaptive designs are used to form recommendations for future monitoring in this region, and for reefs where changes in the current monitoring practices are being sought. As the methods used and developed throughout this study are generic in nature, this research has the potential to improve ecological monitoring more broadly where complex data are being collected over time.

Temperature Sensing and Honey Bee Colony Strength.

Strength auditing of European honey bee (Apis mellifera Linnaeus, 1758 [Hymenoptera: Apidae]) colonies is critical for apiarists to manage colony health and meet pollination contracts conditions. Colony strength assessments used during pollination servicing in Australia typically use a frame-top cluster-count (Number of Frames) inspection. Sensing technology has potential to improve auditing processes, and commercial temperature sensors are widely available. We evaluate the use and placement of temperature sensing technology in colony strength assessment and identify key parameters linking temperature to colony strength. Custom-built temperature sensors measured hive temperature across the top of hive brood boxes. A linear mixed-effect model including harmonic sine and cosine curves representing diurnal temperature fluctuations in hives was used to compare Number of Frames with temperature sensor data. There was a significant effect of presence of bees on hive temperature and range: hives without bees recorded a 5.5°C lower mean temperature and greater temperature ranges than hives containing live bees. Hives without bees reach peak temperature earlier than hives with bees, regardless of colony strength. Sensor placement across the width of the hive was identified as an important factor when linking sensor data with colony strength. Data from sensors nearest to the hive geometric center were found to be more closely linked to colony strength. Furthermore, a one unit increase in Number of Frames was significantly associated with a mean temperature increase of 0.36°C. This demonstrates that statistical models that account for diurnal temperature patterns could be used to predict colony strength from temperature sensor data.

Exploring Symptom Fluctuations and Triggers in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Using Novel Patient-Centred N-of-1 Observational Designs: A Protocol for a Feasibility and Acceptability Study.

BACKGROUND: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic condition of unknown aetiology associated with a range of disabling symptoms, including post-exertional malaise, chronic fatigue, musculoskeletal pain, orthostatic intolerance, unrefreshing sleep, and cognitive dysfunction. ME/CFS is a heterogeneous disorder, with significant variation in symptom type and severity between individuals, as well as within individuals over time. The diversity of ME/CFS symptom presentation makes management challenging; treatments supported by data from randomised controlled trials may not work for all individuals due to the variability in experienced symptoms. Studies using quantitative N-of-1 observational designs involve repeated outcome measurements in an individual over time and can generate rigorous individual-specific conclusions about symptom patterns and triggers in individuals with ME/CFS. This study aims to explore the feasibility and acceptability of using novel patient-centred N-of-1 observational designs to explore symptom fluctuations and triggers in ME/CFS at the individual level. METHODS AND ANALYSIS: Individuals with a medical diagnosis of ME/CFS will be recruited through ME/CFS patient organisations to participate in a series of patient-centred N-of-1 observational studies. Using a wrist-worn electronic diary, participants will complete ecological momentary assessments of fatigue, stress, mood, and cognitive demand, three times per day for a period of 6-12 weeks. Personally relevant symptoms and triggers will also be incorporated into the questionnaire design. Physical activity will be objectively measured via an integrated accelerometer. Feasibility and acceptability outcomes will be assessed including the percentage of diary entries completed, as well as recruitment and retention rate, feasibility of analysing and interpreting the data collected, and participant views about participation elicited via a post-study semi-structured interview. DISCUSSION: This study will assess the feasibility and acceptability of patient-centred N-of-1 observational studies to assess diseases with complex presentations such as ME/CFS, as well as provide individual-level evidence about fluctuations and triggers of ME/CFS symptoms that may aid self-management. TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Registry: ACTRN12618001898246. Registered on 22 November 2018.

Establishment of an International Collaborative Network for N-of-1 Trials and Single-Case Designs.

In this article we briefly examine the unique features of Single-Case Designs (SCDs) (studies in a single participant), their history and current trends, and real-world clinical applications. The International Collaborative Network for N-of-1 Trials and Single-Case Designs (ICN) is a formal collaborative network for individuals with an interest in SCDs. The ICN was established in 2017 to support the SCD scientific community and provide opportunities for collaboration, a global communication channel, resource sharing and knowledge exchange. In May 2021, there were more than 420 members in 31 countries. A member survey was undertaken in 2019 to identify priorities for the ICN for the following few years. This article outlines the key priorities identified and the ICN's progress to date in these key areas including network activities (developing a communications strategy to increase awareness, collecting/sharing a comprehensive set of resources, guidelines and tips, and incorporating the consumer perspective) and scientific activities (writing position papers and guest editing special journal issues, exploring key stakeholder perspectives about SCDs, and working to streamline ethical approval processes for SCDs). The ICN provides a practical means to engage with this methodology through membership. We encourage clinicians, researchers, industry, and healthcare consumers to learn more about and conduct SCDs, and to join us in our mission of using SCDs to improve health outcomes for individuals and populations.

Bayesian adaptive N-of-1 trials for estimating population and individual treatment effects.

This article proposes a novel adaptive design algorithm that can be used to find optimal treatment allocations in N-of-1 clinical trials. This new methodology uses two Laplace approximations to provide a computationally efficient estimate of population and individual random effects within a repeated measures, adaptive design framework. Given the efficiency of this approach, it is also adopted for treatment selection to target the collection of data for the precise estimation of treatment effects. To evaluate this approach, we consider both a simulated and motivating N-of-1 clinical trial from the literature. For each trial, our methods were compared with the multiarmed bandit approach and a randomized N-of-1 trial design in terms of identifying the best treatment for each patient and the information gained about the model parameters. The results show that our new approach selects designs that are highly efficient in achieving each of these objectives. As such, we propose our Laplace-based algorithm as an efficient approach for designing adaptive N-of-1 trials.

SSNdesign-An R package for pseudo-Bayesian optimal and adaptive sampling designs on stream networks.

Streams and rivers are biodiverse and provide valuable ecosystem services. Maintaining these ecosystems is an important task, so organisations often monitor the status and trends in stream condition and biodiversity using field sampling and, more recently, autonomous in-situ sensors. However, data collection is often costly, so effective and efficient survey designs are crucial to maximise information while minimising costs. Geostatistics and optimal and adaptive design theory can be used to optimise the placement of sampling sites in freshwater studies and aquatic monitoring programs. Geostatistical modelling and experimental design on stream networks pose statistical challenges due to the branching structure of the network, flow connectivity and directionality, and differences in flow volume. Geostatistical models for stream network data and their unique features already exist. Some basic theory for experimental design in stream environments has also previously been described. However, open source software that makes these design methods available for aquatic scientists does not yet exist. To address this need, we present SSNdesign, an R package for solving optimal and adaptive design problems on stream networks that integrates with existing open-source software. We demonstrate the mathematical foundations of our approach, and illustrate the functionality of SSNdesign using two case studies involving real data from Queensland, Australia. In both case studies we demonstrate that the optimal or adaptive designs outperform random and spatially balanced survey designs implemented in existing open-source software packages. The SSNdesign package has the potential to boost the efficiency of freshwater monitoring efforts and provide much-needed information for freshwater conservation and management.

Relative rate of change in cognitive score network dynamics via Bayesian hierarchical models reveal spatial patterns of neurodegeneration.

The degeneration of the human brain is a complex process, which often affects certain brain regions due to healthy aging or disease. This degeneration can be evaluated on regions of interest (ROI) in the brain through probabilistic networks and morphological estimates. Current approaches for finding such networks are limited to analyses at discrete neuropsychological stages, which cannot appropriately account for connectivity dynamics over the onset of cognitive deterioration, and morphological changes are seldom unified with connectivity networks, despite known dependencies. To overcome these limitations, a probabilistic wombling model is proposed to simultaneously estimate ROI cortical thickness and covariance networks contingent on rates of change in cognitive decline. This proposed model was applied to analyze longitudinal data from healthy control (HC) and Alzheimer's disease (AD) groups and found connection differences pertaining to regions, which play a crucial role in lasting cognitive impairment, such as the entorhinal area and temporal regions. Moreover, HC cortical thickness estimates were significantly higher than those in the AD group across all ROIs. The analyses presented in this work will help practitioners jointly analyze brain tissue atrophy at the ROI-level conditional on neuropsychological networks, which could potentially allow for more targeted therapeutic interventions.

Author Correction: Leaf nutrients, not specific leaf area, are consistent indicators of elevated nutrient inputs.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Comparison of Aggregated N-of-1 Trials with Parallel and Crossover Randomized Controlled Trials Using Simulation Studies.

Background: N-of-1 trials offer an innovative approach to delivering personalized clinical care together with population-level research. While increasingly used, these methods have raised some statistical concerns in the healthcare community. Methods: We discuss concerns of selection bias, carryover effects from treatment, and trial data analysis conceptually, then rigorously evaluate concerns of effect sizes, power, and sample size through simulation study. Four variance structures for patient heterogeneity and model error are considered in a series of 5000 simulated trials with three cycles, which compare N-of-1 trials to parallel randomized controlled trials (RCTs) and crossover trials. Results: N-of-1 trials outperformed both traditional parallel RCTs and crossover designs when trial designs were simulated in terms of power and required sample size to obtain a given power. N-of-1 designs resulted in a higher type-I error probability than parallel RCT and cross over designs when moderate-to-strong carryover or washout effects were not considered or in the presence of modeled selection bias. However, N-of-1 designs allowed better estimation of patient-level random effects. These results reinforce the need to account for these factors when planning N-of-1 trials. Conclusion: N-of-1 trial designs offer a rigorous method for advancing personalized medicine and healthcare with the potential to minimize costs and resources. Interventions can be tested with adequate power with far fewer patients than traditional RCT and crossover designs. Operating characteristics compare favorably to both traditional RCT and crossover designs.

Predicting sediment and nutrient concentrations from high-frequency water-quality data.

Water-quality monitoring in rivers often focuses on the concentrations of sediments and nutrients, constituents that can smother biota and cause eutrophication. However, the physical and economic constraints of manual sampling prohibit data collection at the frequency required to adequately capture the variation in concentrations through time. Here, we developed models to predict total suspended solids (TSS) and oxidized nitrogen (NOx) concentrations based on high-frequency time series of turbidity, conductivity and river level data from in situ sensors in rivers flowing into the Great Barrier Reef lagoon. We fit generalized-linear mixed-effects models with continuous first-order autoregressive correlation structures to water-quality data collected by manual sampling at two freshwater sites and one estuarine site and used the fitted models to predict TSS and NOx from the in situ sensor data. These models described the temporal autocorrelation in the data and handled observations collected at irregular frequencies, characteristics typical of water-quality monitoring data. Turbidity proved a useful and generalizable surrogate of TSS, with high predictive ability in the estuarine and fresh water sites. Turbidity, conductivity and river level served as combined surrogates of NOx. However, the relationship between NOx and the covariates was more complex than that between TSS and turbidity, and consequently the ability to predict NOx was lower and less generalizable across sites than for TSS. Furthermore, prediction intervals tended to increase during events, for both TSS and NOx models, highlighting the need to include measures of uncertainty routinely in water-quality reporting. Our study also highlights that surrogate-based models used to predict sediments and nutrients need to better incorporate temporal components if variance estimates are to be unbiased and model inference meaningful. The transferability of models across sites, and potentially regions, will become increasingly important as organizations move to automated sensing for water-quality monitoring throughout catchments.

RD-RAP: beyond rare disease patient registries, devising a comprehensive data and analytic framework.

Within the 21 APEC economies alone, there are an estimated 200 million individuals living with a rare disease. As such, health data on these individuals, and hence patient registries, are vital. However, registries can come in many different forms and operating models in different jurisdictions. They possess a varying degree of functionality and are used for a variety of purposes. For instance registries can facilitate service planning as well as underpin public health and clinical research by providing de-identified data to researchers. Furthermore, registries may be used to create and disseminate new knowledge to inform clinical best practice and care, to identify and enrol participants for clinical trials, and to enable seamless integration of patient data for diagnostic testing and cascade screening. Registries that add capability such as capturing patient reported outcomes enable patients, and their carers, to become active partners in their care, rapidly furthering research and ensuring up-to-date practice-based evidence. Typically, a patient registry centres around the notion of health data 'capture', usually for only one or a small subset of the functions outlined above, thereby creating fragmented datasets that, despite the best efforts and intentions, make it difficult to exchange the right data for the right purpose to the right stakeholder under appropriate governance arrangements. Trying to incorporate maximum functionality into a registry is an obvious strategy, but monolithic software solutions are not desirable. As an alternative, we propose that it is important to incorporate analytics as core to a patient registry, rather than just utilising registries as a 'data capture' solution. We contend that embracing an analytics-centric focus makes it reasonable to imagine a future where it will be possible to evaluate the individual outcomes of health interventions in real time. The purposeful and, importantly, the repurposable application of health data will allow stakeholders to extract, create and reuse knowledge to improve health outcomes, assist clinical decision making, and improve health service design and delivery. To realise this vision, we introduce and describe the concept of a Rare Disease Registry and Analytics Platform (RD-RAP); one that we hope will make a meaningful difference to the lives of those living with a rare disease.

Incidence and risk factors for recurrence of endocrinopathic laminitis in horses.

BACKGROUND: Endocrinopathic laminitis is common in horses and ponies, but the recurrence rate of the disease is poorly defined. OBJECTIVES: To determine the incidence of, and risk factors for, the recurrence of endocrinopathic laminitis. ANIMALS: Privately owned horses and ponies with acute laminitis (n = 317, of which 276 cases with endocrinopathic laminitis were followed up to study completion). METHODS: This prospective cohort study collected data on veterinary-diagnosed cases of acute laminitis for 2 years. Each case was classified on acceptance to the study as endocrinopathic or non-endocrinopathic using data collected in a questionnaire completed by the animal's veterinarian. Follow-up data were collected at regular intervals to determine whether the laminitis recurred in the 2-year period after diagnosis. RESULTS: The recurrence rate for endocrinopathic laminitis was 34.1%. The risk of recurrence during the 2-year study period increased with basal, fasted serum insulin concentration (P ≤ .05), with the probability of recurrence increasing markedly as the insulin concentration increased beyond the normal range (0-20 µIU/mL) to over the threshold for normal (up to approximately 45 µIU/mL). Being previously diagnosed with laminitis (before the study; P = .05) was also a risk factor for recurrent laminitis. Cases with a higher Obel grade of laminitis were likely (P = .05) to recur sooner. CONCLUSIONS AND CLINICAL IMPORTANCE: Knowing that hyperinsulinemia and being previously diagnosed with laminitis are significant risk factors for recurrence will enable clinicians to proactively address these factors, thereby potentially reducing the risk of recurrence of laminitis.

Leaf nutrients, not specific leaf area, are consistent indicators of elevated nutrient inputs.

Leaf traits are frequently measured in ecology to provide a 'common currency' for predicting how anthropogenic pressures impact ecosystem function. Here, we test whether leaf traits consistently respond to experimental treatments across 27 globally distributed grassland sites across 4 continents. We find that specific leaf area (leaf area per unit mass)-a commonly measured morphological trait inferring shifts between plant growth strategies-did not respond to up to four years of soil nutrient additions. Leaf nitrogen, phosphorus and potassium concentrations increased in response to the addition of each respective soil nutrient. We found few significant changes in leaf traits when vertebrate herbivores were excluded in the short-term. Leaf nitrogen and potassium concentrations were positively correlated with species turnover, suggesting that interspecific trait variation was a significant predictor of leaf nitrogen and potassium, but not of leaf phosphorus concentration. Climatic conditions and pretreatment soil nutrient levels also accounted for significant amounts of variation in the leaf traits measured. Overall, we find that leaf morphological traits, such as specific leaf area, are not appropriate indicators of plant response to anthropogenic perturbations in grasslands.

A framework for automated anomaly detection in high frequency water-quality data from in situ sensors.

Monitoring the water quality of rivers is increasingly conducted using automated in situ sensors, enabling timelier identification of unexpected values or trends. However, the data are confounded by anomalies caused by technical issues, for which the volume and velocity of data preclude manual detection. We present a framework for automated anomaly detection in high-frequency water-quality data from in situ sensors, using turbidity, conductivity and river level data collected from rivers flowing into the Great Barrier Reef. After identifying end-user needs and defining anomalies, we ranked anomaly importance and selected suitable detection methods. High priority anomalies included sudden isolated spikes and level shifts, most of which were classified correctly by regression-based methods such as autoregressive integrated moving average models. However, incorporation of multiple water-quality variables as covariates reduced performance due to complex relationships among variables. Classifications of drift and periods of anomalously low or high variability were more often correct when we applied mitigation, which replaces anomalous measurements with forecasts for further forecasting, but this inflated false positive rates. Feature-based methods also performed well on high priority anomalies and were similarly less proficient at detecting lower priority anomalies, resulting in high false negative rates. Unlike regression-based methods, however, all feature-based methods produced low false positive rates and have the benefit of not requiring training or optimization. Rule-based methods successfully detected a subset of lower priority anomalies, specifically impossible values and missing observations. We therefore suggest that a combination of methods will provide optimal performance in terms of correct anomaly detection, whilst minimizing false detection rates. Furthermore, our framework emphasizes the importance of communication between end-users and anomaly detection developers for optimal outcomes with respect to both detection performance and end-user application. To this end, our framework has high transferability to other types of high frequency time-series data and anomaly detection applications.

Principles of Experimental Design for Big Data Analysis.

Big Datasets are endemic, but are often notoriously difficult to analyse because of their size, heterogeneity and quality. The purpose of this paper is to open a discourse on the potential for modern decision theoretic optimal experimental design methods, which by their very nature have traditionally been applied prospectively, to improve the analysis of Big Data through retrospective designed sampling in order to answer particular questions of interest. By appealing to a range of examples, it is suggested that this perspective on Big Data modelling and analysis has the potential for wide generality and advantageous inferential and computational properties. We highlight current hurdles and open research questions surrounding efficient computational optimisation in using retrospective designs, and in part this paper is a call to the optimisation and experimental design communities to work together in the field of Big Data analysis.