Sequential design of computer experiments for the estimation of a probability of failure

This paper deals with the problem of estimating the volume of the excursion set of a function $f:\mathbb{R}^d \to \mathbb{R}$ above a given threshold, under a probability measure on $\mathbb{R}^d$ that is assumed to be known. In the industrial world, this corresponds to the problem of estimating a probability of failure of a system. When only an expensive-to-simulate model of the system is available, the budget for simulations is usually severely limited and therefore classical Monte Carlo methods ought to be avoided. One of the main contributions of this article is to derive SUR (stepwise uncertainty reduction) strategies from a Bayesian-theoretic formulation of the problem of estimating a probability of failure. These sequential strategies use a Gaussian process model of $f$ and aim at performing evaluations of $f$ as efficiently as possible to infer the value of the probability of failure. We compare these strategies to other strategies also based on a Gaussian process model for estimating a probability of failure.Comment: This is an author-generated postprint version. The published version is available at http://www.springerlink.co

Time intervals from first symptom to treatment of cancer: a cohort study of 2,212 newly diagnosed cancer patients

<p>Abstract</p> <p>Background</p> <p>Delay in diagnosis of cancer may worsen prognosis. The aim of this study is to explore patient-, general practitioner (GP)- and system-related delay in the interval from first cancer symptom to diagnosis and treatment, and to analyse the extent to which delays differ by cancer type.</p> <p>Methods</p> <p>Population-based cohort study conducted in 2004-05 in the County of Aarhus, Denmark (640,000 inhabitants). Data were collected from administrative registries and questionnaires completed by GPs on 2,212 cancer patients newly diagnosed during a 1-year period. Median delay (in days) with interquartile interval (IQI) was the main outcome measure.</p> <p>Results</p> <p>Median total delay was 98 days (IQI 57-168). Most of the total delay stemmed from patient (median 21 days (7-56)) and system delay (median 55 days (32-93)). Median GP delay was 0 (0-2) days. Total delay was shortest among patients with ovarian (median 60 days (45-112)) and breast cancer (median 65 days (39-106)) and longest among patients with prostate (median 130 days (89-254)) and bladder cancer (median 134 days (93-181)).</p> <p>Conclusion</p> <p>System delay accounted for a substantial part of the total delay experienced by cancer patients. This points to a need for shortening clinical pathways if possible. A long patient delay calls for research into patient awareness of cancer. For all delay components, special focus should be given to the 4^thquartile of patients with the longest time intervals and we need research into the quality of the diagnostic work-up process. We found large variations in delay for different types of cancer. Improvements should therefore target both the population at large and the specific needs associated with individual cancer types and their symptoms.</p

Understanding missed opportunities for more timely diagnosis of cancer in symptomatic patients after presentation.

The diagnosis of cancer is a complex, multi-step process. In this paper, we highlight factors involved in missed opportunities to diagnose cancer more promptly in symptomatic patients and discuss responsible mechanisms and potential strategies to shorten intervals from presentation to diagnosis. Missed opportunities are instances in which post-hoc judgement indicates that alternative decisions or actions could have led to more timely diagnosis. They can occur in any of the three phases of the diagnostic process (initial diagnostic assessment; diagnostic test performance and interpretation; and diagnostic follow-up and coordination) and can involve patient, doctor/care team, and health-care system factors, often in combination. In this perspective article, we consider epidemiological 'signals' suggestive of missed opportunities and draw on evidence from retrospective case reviews of cancer patient cohorts to summarise factors that contribute to missed opportunities. Multi-disciplinary research targeting such factors is important to shorten diagnostic intervals post presentation. Insights from the fields of organisational and cognitive psychology, human factors science and informatics can be extremely valuable in this emerging research agenda. We provide a conceptual foundation for the development of future interventions to minimise the occurrence of missed opportunities in cancer diagnosis, enriching current approaches that chiefly focus on clinical decision support or on widening access to investigations.We acknowledge the helpful and incisive comments by Dr Rikke Sand Andersen (Aarhus University, Denmark) in conceptualising this piece and in drafts of the manuscript. The work is independent research supported by different funding schemes. GL was supported by a Post-Doctoral Fellowship by the National Institute for Health Research (PDF-2011-04-047) until the end of 2014 and by a Cancer Research UK Clinician Scientist Fellowship award (A18180) from 2015. HS is supported by the VA Health Services Research and Development Service (CRE 12-033; Presidential Early Career Award for Scientists and Engineers USA 14-274), the VA National Center for Patient Safety, the Agency for Health Care Research and Quality (R01HS022087) and in part by the Houston VA HSR&D Center for Innovations in Quality, Effectiveness and Safety (CIN 13–413). PV was supported by CaP, funded by The Danish Cancer Society and the Novo Nordisk Foundation.This is the final version of the article. It first appeared at http://dx.doi.org/10.1038/bjc.2015.4

Factors influencing the speed of cancer diagnosis in rural Western Australia: A General Practice perspective

Introduction. The speed of diagnosis impacts on prognosis and survival in all types of cancer. In most cases survival and prognosis are significantly worse in rural and remote Australian populations who have less access to diagnostic and therapeutic services than metropolitan communities in this country. Research suggests that in general delays in diagnosis were a factor of misdiagnosis, the confounding effect of existing conditions and delayed or misleading investigation of symptoms. The aim of this study is to further explore the factors that impact on the speed of diagnosis in rural Western Australia with direct reference to General Practitioners (GPs) working in this setting. Methods. The methodology consisted of a structured discussion of specific cases. GPs based in two rural locations in Western Australia were asked to identify up to eight clinical cases for discussion. A diversity of cases was requested encompassing those with timely and delayed diagnosis of cancer. Focus groups were held with the practitioners to identify which factors under six headings delayed or facilitated the diagnosis in each case. A structured summary of the discussion was relayed to a wider group of GPs to seek additional views or comments on specific factors that impact on the speed of cancer diagnosis in rural and remote locations in Australia. Results. A number of factors affecting the speed of diagnosis were identified: the demographic shift towards a frailer and older population, presenting with multiple and complex diseases, increases the challenge to identify early cancer symptoms; seasonal and demanding work patterns leading to procrastination in presenting for medical care; unhelpful scheduling of specialist appointments; and the varying impact of informal networks and social relationships. Conclusion. Within the limitations of this study we have generated a number of hypotheses that require formal evaluation: (1) GPs working within informal professional and social networks are better informed about their patients' health needs and have an advantage in making early diagnosis; (2) Despite the other differences in the population characteristics decentralising services would improve the prospect for timely diagnosis; and (3) Careful coordination of specialist appointments would improve the speed of diagnosis for rural patients. © 2007 Jiwa et al; licensee BioMed Central Ltd

Samples from Lomonosov Ridge place new constraints on the geological evolution of Arctic Ocean

A number of rock samples were collected from two dredge positions on the Lomonosov Ridge at water depths of 2–3.5 km. The dredge samples are dominated by sediments deformed and metamorphosed under greenschist-facies conditions 470 myr ago according to 40Ar/39Ar dating of metamorphic muscovite. This shows that the Lomonosov Ridge was involved in a major Mid-Ordovician orogenic event that correlates with early arc–terrane accretion observed in northern Ellesmere Island, Svalbard, and other parts of the Caledonian belt. Detrital zircon age spectra of these metasediments span the Mesoproterozoic–Palaeoproterozoic with a main peak at around 1.6 Ga, and a pattern similar to that known from Caledonian metasedimentary rocks in East Greenland and northern Norway, as well as from Cambrian sediments in Estonia and Palaeozoic sediments on Novaya Zemlya. A second population of dredge samples comprises undeformed, non-metamorphic sandstones and siltstones. Detrital zircons in these sediments span the Palaeoproterozoic with a few Archaean zircons. Both rock types are covered by an up to 8 Ma ferromanganese crust and are evaluated to represent outcrop, and apatite fission-track data from three of the rock samples indicate that exposure at the seabed corresponds to a regional event of uplift and erosion that affected the Arctic in the Late Miocene. The data from the Lomonosov Ridge suggest that the 470 Ma orogenic event extended from Scotland and northern Scandinavia into the Arctic, including Svalbard, the Pearya Terrane and the Chukchi Borderlands