Models and metaphors: complexity theory and through-life management in the built environment

Complexity thinking may have both modelling and metaphorical applications in the through-life management of the built environment. These two distinct approaches are examined and compared. In the first instance, some of the sources of complexity in the design, construction and maintenance of the built environment are identified. The metaphorical use of complexity in management thinking and its application in the built environment are briefly examined. This is followed by an exploration of modelling techniques relevant to built environment concerns. Non-linear and complex mathematical techniques such as fuzzy logic, cellular automata and attractors, may be applicable to their analysis. Existing software tools are identified and examples of successful built environment applications of complexity modelling are given. Some issues that arise include the definition of phenomena in a mathematically usable way, the functionality of available software and the possibility of going beyond representational modelling. Further questions arising from the application of complexity thinking are discussed, including the possibilities for confusion that arise from the use of metaphor. The metaphor of a 'commentary machine' is suggested as a possible way forward and it is suggested that an appropriate linguistic analysis can in certain situations reduce perceived complexity

Using Bayes to get the most out of non-significant results

No scientific conclusion follows automatically from a statistically non-significant result, yet people routinely use non-significant results to guide conclusions about the status of theories (or the effectiveness of practices). To know whether a non-significant result counts against a theory, or if it just indicates data insensitivity, researchers must use one of: power, intervals (such as confidence or credibility intervals), or else an indicator of the relative evidence for one theory over another, such as a Bayes factor. I argue Bayes factors allow theory to be linked to data in a way that overcomes the weaknesses of the other approaches. Specifically, Bayes factors use the data themselves to determine their sensitivity in distinguishing theories (unlike power), and they make use of those aspects of a theory’s predictions that are often easiest to specify (unlike power and intervals, which require specifying the minimal interesting value in order to address theory). Bayes factors provide a coherent approach to determining whether non-significant results support a null hypothesis over a theory, or whether the data are just insensitive. They allow accepting and rejecting the null hypothesis to be put on an equal footing. Concrete examples are provided to indicate the range of application of a simple online Bayes calculator, which reveal both the strengths and weaknesses of Bayes factors

The earth is flat (p < 0.05): significance thresholds and the crisis of unreplicable research

The widespread use of ‘statistical significance’ as a license for making a claim of a scientific finding leads to considerable distortion of the scientific process (according to the American Statistical Association). We review why degrading p -values into ‘significant’ and ‘nonsignificant’ contributes to making studies irreproducible, or to making them seem irreproducible. A major problem is that we tend to take small p -values at face value, but mistrust results with larger p -values. In either case, p -values tell little about reliability of research, because they are hardly replicable even if an alternative hypothesis is true. Also significance ( p ≤ 0.05) is hardly replicable: at a good statistical power of 80%, two studies will be ‘conflicting’, meaning that one is significant and the other is not, in one third of the cases if there is a true effect. A replication can therefore not be interpreted as having failed only because it is nonsignificant. Many apparent replication failures may thus reflect faulty judgment based on significance thresholds rather than a crisis of unreplicable research. Reliable conclusions on replicability and practical importance of a finding can only be drawn using cumulative evidence from multiple independent studies. However, applying significance thresholds makes cumulative knowledge unreliable. One reason is that with anything but ideal statistical power, significant effect sizes will be biased upwards. Interpreting inflated significant results while ignoring nonsignificant results will thus lead to wrong conclusions. But current incentives to hunt for significance lead to selective reporting and to publication bias against nonsignificant findings. Data dredging, p -hacking, and publication bias should be addressed by removing fixed significance thresholds. Consistent with the recommendations of the late Ronald Fisher, p -values should be interpreted as graded measures of the strength of evidence against the null hypothesis. Also larger p -values offer some evidence against the null hypothesis, and they cannot be interpreted as supporting the null hypothesis, falsely concluding that ‘there is no effect’. Information on possible true effect sizes that are compatible with the data must be obtained from the point estimate, e.g., from a sample average, and from the interval estimate, such as a confidence interval. We review how confusion about interpretation of larger p -values can be traced back to historical disputes among the founders of modern statistics. We further discuss potential arguments against removing significance thresholds, for example that decision rules should rather be more stringent, that sample sizes could decrease, or that p -values should better be completely abandoned. We conclude that whatever method of statistical inference we use, dichotomous threshold thinking must give way to non-automated informed judgment

Four reasons to prefer Bayesian analyses over significance testing

Inference using significance testing and Bayes factors is compared and contrasted in five case studies based on real research. The first study illustrates that the methods will often agree, both in motivating researchers to conclude that H1 is supported better than H0, and the other way round, that H0 is better supported than H1. The next four, however, show that the methods will also often disagree. In these cases, the aim of the paper will be to motivate the sensible evidential conclusion, and then see which approach matches those intuitions. Specifically, it is shown that a high-powered non-significant result is consistent with no evidence for H0 over H1 worth mentioning, which a Bayes factor can show, and, conversely, that a low-powered non-significant result is consistent with substantial evidence for H0 over H1, again indicated by Bayesian analyses. The fourth study illustrates that a high-powered significant result may not amount to any evidence for H1 over H0, matching the Bayesian conclusion. Finally, the fifth study illustrates that different theories can be evidentially supported to different degrees by the same data; a fact that P-values cannot reflect but Bayes factors can. It is argued that appropriate conclusions match the Bayesian inferences, but not those based on significance testing, where they disagree

Being Ready to Treat Ebola Virus Disease Patients

As the outbreak of Ebola virus disease (EVD) in West Africa continues, clinical preparedness is needed in countries at risk for EVD (e.g., United States) and more fully equipped and supported clinical teams in those countries with epidemic spread of EVD in Africa. Clinical staff must approach the patient with a very deliberate focus on providing effective care while assuring personal safety. To do this, both individual health care providers and health systems must improve EVD care. Although formal guidance toward these goals exists from the World Health Organization, Medecin Sans Frontières, the Centers for Disease Control and Prevention, and other groups, some of the most critical lessons come from personal experience. In this narrative, clinicians deployed by the World Health Organization into a wide range of clinical settings in West Africa distill key, practical considerations for working safely and effectively with patients with EVD

On peer review as the ‘gold standard’ in measuring research excellence: from secrecy to openness?

As universities in the United Kingdom gear themselves up for the next Research Excellence Framework, REF2021, with peer review at its core, we critically re‐visit the idea of peer review as a gold standard proxy for research excellence. We question the premise that anonymous peer review is a necessary and enabling condition for impartial, expert judgement. We argue that the intentions and supposed benefits underlying peer review and its associated concepts have become congealed in received discourse about research quality. Hence we explore the key conceptual issues raised by the nested assumptions and concepts that come into play in peer review as currently practised: primarily those of secrecy, anonymity, legitimacy, trust, impartiality and openness. After delineating the benefits attributed to peer review, we contrast its declared virtues with its problematic features. We locate peer review in an audit culture in which the reviewer is an academic labourer. Drawing on recent trends in moral and political philosophy, we question the usefulness of the ideal of impartiality when tied to secrecy. Then we raise more deliberative, intersubjective possibilities for a revised understanding of peer review in the context of an academic community. Finally, we suggest ways in which the academic community could pursue quality in research by recasting peer review to be less secret and more open

Being Ready to Treat Ebola Virus Disease Patients

An unprecedented number of health care professionals from a variety of clinical settings, in a wide range of countries are thinking about, preparing for and caring for Ebola virus disease (EVD) patients. Guidance documents on infection prevention and control (IPC) practice and clinical care have been produced by organizations with EVD experience.1–3 The World Health Organization (WHO) produces guidance for implementation across a wide range of resource settings. Medecin Sans Frontières produces guidance for medical team activities across the outbreak. The Centers for Disease Control and Prevention (CDC) focus on measures which can be taken by the United States health system and extrapolated by others involved in preparedness and response. There are no short cuts to clinical preparedness for EVD. These documents and their revisions should be reviewed carefully. As important as guidance documents are, many lessons must be learned from specific hands-on experience. The WHO has mobilized clinical consultants in support of EVD response in each of the affected countries in West Africa. This short list of key points attempts to consolidate practical lessons learned that do not always percolate into technical documents. Having landed in unconstrained, resource-limited settings at the start of local EVD clinical operations in an outbreak, and more established EVD care centers, we hope that others might adopt some of these lessons and avoid some of the risks inherent to the steep learning curve associated with delivering EVD care. The points are geared toward the daily care of patients as opposed to the critical mechanics of establishing a care center and developing its procedures. They are focused on the outbreak setting and also have relevance to the referral hospital setting

Trends in Outcomes for Neonates Born Very Preterm and Very Low Birth Weight in 11 High-Income Countries

Objective To evaluate outcome trends of neonates born very preterm in 11 high-income countries participating in the International Network for Evaluating Outcomes of neonates. Study design In a retrospective cohort study, we included 154 233 neonates admitted to 529 neonatal units between January 1, 2007, and December 31, 2015, at 24(0/7) to 31(6/7) weeks of gestational age and birth weight <1500 g. Composite outcomes were in-hospital mortality or any of severe neurologic injury, treated retinopathy of prematurity, and bronchopulmonary dysplasia (BPD); and same composite outcome excluding BPD. Secondary outcomes were mortality and individual morbidities. For each country, annual outcome trends and adjusted relative risks comparing epoch 2 (2012-2015) to epoch 1 (2007-2011) were analyzed. Results For composite outcome including BPD, the trend decreased in Canada and Israel but increased in Australia and New Zealand, Japan, Spain, Sweden, and the United Kingdom. For composite outcome excluding BPD, the trend decreased in all countries except Spain, Sweden, Tuscany, and the United Kingdom. The risk of composite outcome was lower in epoch 2 than epoch 1 in Canada (adjusted relative risks 0.78; 95% CI 0.74-0.82) only. The risk of composite outcome excluding BPD was significantly lower in epoch 2 compared with epoch 1 in Australia and New Zealand, Canada, Finland, Japan, and Switzerland. Mortality rates reduced in most countries in epoch 2. BPD rates increased significantly in all countries except Canada, Israel, Finland, and Tuscany. Conclusions In most countries, mortality decreased whereas BPD increased for neonates born very preterm