What a difference a stochastic process makes: epidemiological-based real options models of optimal treatment of disease

The real options approach has been used within environmental economics to investigate the impact of uncertainty on the optimal timing of control measures to minimise the impacts of invasive species, including pests and diseases. Previous studies typically model the growth in infected area using geometric Brownian motion (GBM). The advantage of this simple approach is that it allows for closed form solutions. However, such a process does not capture the mechanisms underlying the spread of infection. In particular the GBM assumption does not respect the natural upper boundary of the system, which is determined by the maximum size of the host species, nor the deceleration in the rate of infection as this boundary is approached. We show how the stochastic process describing the growth in infected area can be derived from the characteristics of the spread of infection. If the model used does not appropriately capture uncertainty in infection dynamics, then the excessive delay before treatment implies that the full value of the option to treat is not realised. Indeed, when uncertainty is high or the disease is fast spreading, ignoring the mechanisms of infection spread can lead to control never being deployed. Thus the results presented here have important implications for the way in which the real options approach is applied to determine optimal timing of disease control given uncertainty in future disease progression

What do we know and when do we know it?

Two essential aspects of virtual screening are considered: experimental design and performance metrics. In the design of any retrospective virtual screen, choices have to be made as to the purpose of the exercise. Is the goal to compare methods? Is the interest in a particular type of target or all targets? Are we simulating a ‘real-world’ setting, or teasing out distinguishing features of a method? What are the confidence limits for the results? What should be reported in a publication? In particular, what criteria should be used to decide between different performance metrics? Comparing the field of molecular modeling to other endeavors, such as medical statistics, criminology, or computer hardware evaluation indicates some clear directions. Taken together these suggest the modeling field has a long way to go to provide effective assessment of its approaches, either to itself or to a broader audience, but that there are no technical reasons why progress cannot be made

Intervention planning and modification of the BUMP intervention: a digital intervention for the early detection of raised blood pressure in pregnancy

Background: Hypertensive disorders in pregnancy, particularly pre-eclampsia, pose a substantial health risk for both maternal and foetal outcomes. The BUMP (Blood Pressure Self-Monitoring in Pregnancy) interventions are being tested in a trial. They aim to facilitate the early detection of raised blood pressure through self-monitoring. This article outlines how the self-monitoring interventions in the BUMP trial were developed and modified using the person-based approach to promote engagement and adherence. Methods: Key behavioural challenges associated with blood pressure self-monitoring in pregnancy were identified through synthesising qualitative pilot data and existing evidence, which informed guiding principles for the development process. Social cognitive theory was identified as an appropriate theoretical framework. A testable logic model was developed to illustrate the hypothesised processes of change associated with the intervention. Iterative qualitative feedback from women and staff informed modifications to the participant materials. Results: The evidence synthesis suggested women face challenges integrating self-monitoring into their lives and that adherence is challenging at certain time points in pregnancy (for example, starting maternity leave). Intervention modification included strategies to address adherence but also focussed on modifying outcome expectancies, by providing messages explaining pre-eclampsia and outlining the potential benefits of self-monitoring. Conclusions: With an in-depth understanding of the target population, several methods and approaches to plan and develop interventions specifically relevant to pregnant women were successfully integrated, to address barriers to behaviour change while ensuring they are easy to engage with, persuasive and acceptable

Predicting ICU survival: A meta-level approach

<p>Abstract</p> <p>Background</p> <p>The performance of separate Intensive Care Unit (ICU) status scoring systems vis-à-vis prediction of outcome is not satisfactory. Computer-based predictive modeling techniques may yield good results but their performance has seldom been extensively compared to that of other mature or emerging predictive models. The objective of the present study was twofold: to propose a prototype meta-level predicting approach concerning Intensive Care Unit (ICU) survival and to evaluate the effectiveness of typical mining models in this context.</p> <p>Methods</p> <p>Data on 158 men and 46 women, were used retrospectively (75% of the patients survived). We used Glasgow Coma Scale (GCS), Acute Physiology And Chronic Health Evaluation II (APACHE II), Sequential Organ Failure Assessment (SOFA) and Injury Severity Score (ISS) values to structure a decision tree (DTM), a neural network (NNM) and a logistic regression (LRM) model and we evaluated the assessment indicators implementing Receiver Operating Characteristics (ROC) plot analysis.</p> <p>Results</p> <p>Our findings indicate that regarding the assessment of indicators' capacity there are specific discrete limits that should be taken into account. The Az score ± SE was 0.8773± 0.0376 for the DTM, 0.8061± 0.0427 for the NNM and 0.8204± 0.0376 for the LRM, suggesting that the proposed DTM achieved a near optimal Az score.</p> <p>Conclusion</p> <p>The predicting processes of ICU survival may go "one step forward", by using classic composite assessment indicators as variables.</p

A statistical framework to evaluate virtual screening

<p>Abstract</p> <p>Background</p> <p>Receiver operating characteristic (ROC) curve is widely used to evaluate virtual screening (VS) studies. However, the method fails to address the "early recognition" problem specific to VS. Although many other metrics, such as RIE, BEDROC, and pROC that emphasize "early recognition" have been proposed, there are no rigorous statistical guidelines for determining the thresholds and performing significance tests. Also no comparisons have been made between these metrics under a statistical framework to better understand their performances.</p> <p>Results</p> <p>We have proposed a statistical framework to evaluate VS studies by which the threshold to determine whether a ranking method is better than random ranking can be derived by bootstrap simulations and 2 ranking methods can be compared by permutation test. We found that different metrics emphasize "early recognition" differently. BEDROC and RIE are 2 statistically equivalent metrics. Our newly proposed metric SLR is superior to pROC. Through extensive simulations, we observed a "seesaw effect" – overemphasizing early recognition reduces the statistical power of a metric to detect true early recognitions.</p> <p>Conclusion</p> <p>The statistical framework developed and tested by us is applicable to any other metric as well, even if their exact distribution is unknown. Under this framework, a threshold can be easily selected according to a pre-specified type I error rate and statistical comparisons between 2 ranking methods becomes possible. The theoretical null distribution of SLR metric is available so that the threshold of SLR can be exactly determined without resorting to bootstrap simulations, which makes it easy to use in practical virtual screening studies.</p

International Veterinary Epilepsy Task Force Consensus Proposal: Outcome of therapeutic interventions in canine and feline epilepsy

Common criteria for the diagnosis of drug resistance and the assessment of outcome are needed urgently as a prerequisite for standardized evaluation and reporting of individual therapeutic responses in canine epilepsy. Thus, we provide a proposal for the definition of drug resistance and partial therapeutic success in canine patients with epilepsy. This consensus statement also suggests a list of factors and aspects of outcome, which should be considered in addition to the impact on seizures. Moreover, these expert recommendations discuss criteria which determine the validity and informative value of a therapeutic trial in an individual patient and also suggest the application of individual outcome criteria. Agreement on common guidelines does not only render a basis for future optimization of individual patient management, but is also a presupposition for the design and implementation of clinical studies with highly standardized inclusion and exclusion criteria. Respective standardization will improve the comparability of findings from different studies and renders an improved basis for multicenter studies. Therefore, this proposal provides an in-depth discussion of the implications of outcome criteria for clinical studies. In particular ethical aspects and the different options for study design and application of individual patient-centered outcome criteria are considered

Telecommunication Networks: Economy, Ecology, Rule

This essay deals with technologies, techniques, business models and legal structures governing telecommunications infrastructures. Megacities are especially vulnerable to shifting agencies in telecoms provision. This paper addresses the relation of the economics of growth, built-in obsolescence and product life cycles with the complex determinations of telecommunications governance in relation to the physical environment of megacities. It argues that an ‘environmentalism of the poor’ must be integrated into considerations of both ecological critique and analyses of telecommunications infrastructure and business practice

Does Alendronate reduce the risk of fracture in men? A meta-analysis incorporating prior knowledge of anti-fracture efficacy in women

BACKGROUND: Alendronate has been found to reduce the risk of fractures in postmenopausal women as demonstrated in multiple randomized controlled trials enrolling thousands of women. Yet there is a paucity of such randomized controlled trials in osteoporotic men. Our objective was to systematically review the anti-fracture efficacy of alendronate in men with low bone mass or with a history of prevalent fracture(s) and incorporate prior knowledge of alendronate efficacy in women in the analysis. METHODS: We examined randomized controlled trials in men comparing the anti-fracture efficacy of alendronate to placebo or calcium or vitamin D, or any combination of these. Studies of men with secondary causes of osteoporosis other than hypogonadism were excluded. We searched the following electronic databases (without language restrictions) for potentially relevant citations: Medline, Medline in Process (1966-May 24/2004), and Embase (1996–2004). We also contacted the manufacturer of the drug in search of other relevant trials. Two reviewers independently identified two trials (including 375 men), which met all inclusion criteria. Data were abstracted by one reviewer and checked by another. Results of the male trials were pooled using Bayesian random effects models, incorporating prior information of anti-fracture efficacy from meta-analyses of women. RESULTS: The odds ratios of incident fractures in men (with 95% credibility intervals) with alendronate (10 mg daily) were: vertebral fractures, 0.44 (0.23, 0.83) and non-vertebral fractures, 0.60 (0.29, 1.44). CONCLUSION: In conclusion, alendronate decreases the risk of vertebral fractures in men at risk. There is currently insufficient evidence of a statistically significant reduction of non-vertebral fractures, but the paucity of trials in men limit the statistical power to detect such an effect