Robust meta-analytic-predictive priors in clinical trials with historical control information.

Historical information is always relevant for clinical trial design. Additionally, if incorporated in the analysis of a new trial, historical data allow to reduce the number of subjects. This decreases costs and trial duration, facilitates recruitment, and may be more ethical. Yet, under prior-data conflict, a too optimistic use of historical data may be inappropriate. We address this challenge by deriving a Bayesian meta-analytic-predictive prior from historical data, which is then combined with the new data. This prospective approach is equivalent to a meta-analytic-combined analysis of historical and new data if parameters are exchangeable across trials. The prospective Bayesian version requires a good approximation of the meta-analytic-predictive prior, which is not available analytically. We propose two- or three-component mixtures of standard priors, which allow for good approximations and, for the one-parameter exponential family, straightforward posterior calculations. Moreover, since one of the mixture components is usually vague, mixture priors will often be heavy-tailed and therefore robust. Further robustness and a more rapid reaction to prior-data conflicts can be achieved by adding an extra weakly-informative mixture component. Use of historical prior information is particularly attractive for adaptive trials, as the randomization ratio can then be changed in case of prior-data conflict. Both frequentist operating characteristics and posterior summaries for various data scenarios show that these designs have desirable properties. We illustrate the methodology for a phase II proof-of-concept trial with historical controls from four studies. Robust meta-analytic-predictive priors alleviate prior-data conflicts ' they should encourage better and more frequent use of historical data in clinical trials.

Blind Deconvolution via Lower-Bounded Logarithmic Image Priors

In this work we devise two novel algorithms for blind deconvolution based on a family of logarithmic image priors. In contrast to recent approaches, we consider a minimalistic formulation of the blind deconvolution problem where there are only two energy terms: a least-squares term for the data fidelity and an image prior based on a lower-bounded logarithm of the norm of the image gradients. We show that this energy formulation is sufficient to achieve the state of the art in blind deconvolution with a good margin over previous methods. Much of the performance is due to the chosen prior. On the one hand, this prior is very effective in favoring sparsity of the image gradients. On the other hand, this prior is non convex. Therefore, solutions that can deal effectively with local minima of the energy become necessary. We devise two iterative minimization algorithms that at each iteration solve convex problems: one obtained via the primal-dual approach and one via majorization-minimization. While the former is computationally efficient, the latter achieves state-of-the-art performance on a public dataset.

Towards novel compact laser sources for non-invasive diagnostics and treatment

An important field of application of lasers is biomedical optics. Here, they offer great utility for diagnosis, therapy and surgery. For the development of novel methods of laser-based biomedical diagnostics careful study of light propagation in biological tissues is necessary to enhance our understanding of the optical measurements undertaken, increase research and development capacity and the diagnostic reliability of optical technologies. Ultimately, fulfilling these requirements will increase uptake in clinical applications of laser based diagnostics and therapeutics. To address these challenges informative biomarkers relevant to the biological and physiological function or disease state of the organism must be selected. These indicators are the results of the analysis of tissues and cells, such as blood. For non-invasive diagnostics peripheral blood, cells and tissue can potentially provide comprehensive information on the condition of the human organism. A detailed study of the light scattering and absorption characteristics can quickly detect physiological and morphological changes in the cells due to thermal, chemical, antibiotic treatments, etc [1-5]. The selection of a laser source to study the structure of biological particles also benefits from the fact that gross pathological changes are not induced and diagnostics make effective use of the monochromatic directional coherence properties of laser radiation.

Reading before buying - exploring consumer attitudes toward informative function of packaging

Oftentimes, packaging is the first and only marketing tool consumers encounter before a purchase, therefore it is considered to be the most important communication and informative tool (Behaegel, 1991; Peters, 1994). The aim of the research is to better understand food label usage of consumers. To make the identification of behaviour patterns possible and to understand the way consumers use labels on packaging netnography has been chosen as the research method. We identified market factors in our research which result in label use. Based on our results, two large consumer segments were identified: conscious and non-conscious consumer behaviours. Reading information on packaging can be classified in two ways, according to method of use (superficial, conditional, incidental) and place (home, or point of sale).

Non-equilibrium thermodynamics of harmonically trapped bosons

We apply the framework of non-equilibrium quantum thermodynamics to the physics of quenched small-size bosonic quantum gases in a harmonic trap. By studying the temporal behaviour of the Loschmidt echo and of the atomic density profile within the trap, which are informative of the non-equilibrium physics and the correlations among the particles, we establish a link with the statistics of (irreversible) work done on the system. This highlights interesting connections between the degree of inter-particle entanglement and the non-equilibrium thermodynamics of the system.

Establishing an EGFR mutation screening service for non-small cell lung cancer - sample quality criteria and candidate histological predictors.

INTRODUCTION: EGFR screening requires good quality tissue, sensitivity and turn-around time (TAT). We report our experience of routine screening, describing sample type, TAT, specimen quality (cellularity and DNA yield), histopathological description, mutation result and clinical outcome. METHODS: Non-small cell lung cancer (NSCLC) sections were screened for EGFR mutations (M+) in exons 18-21. Clinical, pathological and screening outcome data were collected for year 1 of testing. Screening outcome alone was collected for year 2. RESULTS: In year 1, 152 samples were tested, most (72%) were diagnostic. TAT was 4.9 days (95%confidence interval (CI)=4.5-5.5). EGFR-M+ prevalence was 11% and higher (20%) among never-smoking women with adenocarcinomas (ADCs), but 30% of mutations occurred in current/ex-smoking men. EGFR-M+ tumours were non-mucinous ADCs and 100% thyroid transcription factor (TTF1+). No mutations were detected in poorly differentiated NSCLC-not otherwise specified (NOS). There was a trend for improved overall survival (OS) among EGFR-M+ versus EGFR-M- patients (median OS=78 versus 17 months). In year 1, test failure rate was 19%, and associated with scant cellularity and low DNA concentrations. However 75% of samples with poor cellularity but representative of tumour were informative and mutation prevalence was 9%. In year 2, 755 samples were tested; mutation prevalence was 13% and test failure only 5.4%. Although samples with low DNA concentration (2.2 ng/μL), the mutation rate was 9.2%. CONCLUSION: Routine epidermal growth factor receptor (EGFR) screening using diagnostic samples is fast and feasible even on samples with poor cellularity and DNA content. Mutations tend to occur in better-differentiated non-mucinous TTF1+ ADCs. Whether these histological criteria may be useful to select patients for EGFR testing merits further investigation.

Tractographie de la matière blanche orientée par a priori anatomiques et microstructurels = White matter tractography guided by anatomical and microstructural priors

Résumé : L'imagerie par résonance magnétique pondérée en diffusion est une modalité unique sensible aux mouvements microscopiques des molécules d'eau dans les tissus biologiques. Il est possible d'utiliser les caractéristiques de ce mouvement pour inférer la structure macroscopique des faisceaux de la matière blanche du cerveau. La technique, appelée tractographie, est devenue l'outil de choix pour étudier cette structure de façon non invasive. Par exemple, la tractographie est utilisée en planification neurochirurgicale et pour le suivi du développement de maladies neurodégénératives. Dans cette thèse, nous exposons certains des biais introduits lors de reconstructions par tractographie, et des méthodes sont proposées pour les réduire. D'abord, nous utilisons des connaissances anatomiques a priori pour orienter la reconstruction. Ainsi, nous montrons que l'information anatomique sur la nature des tissus permet d'estimer des faisceaux anatomiquement plausibles et de réduire les biais dans l'estimation de structures complexes de la matière blanche. Ensuite, nous utilisons des connaissances microstructurelles a priori dans la reconstruction, afin de permettre à la tractographie de suivre le mouvement des molécules d'eau non seulement le long des faisceaux, mais aussi dans des milieux microstructurels spécifiques. La tractographie peut ainsi distinguer différents faisceaux, réduire les erreurs de reconstruction et permettre l'étude de la microstructure le long de la matière blanche. Somme toute, nous montrons que l'utilisation de connaissances anatomiques et microstructurelles a priori, en tractographie, augmente l'exactitude des reconstructions de la matière blanche du cerveau.

Correcting for bias when estimating the cost of hospital-acquired infection : an analysis of lower respiratory tract infections in non-surgical patients

Hospital acquired infections (HAI) are costly but many are avoidable. Evaluating prevention programmes requires data on their costs and benefits. Estimating the actual costs of HAI (a measure of the cost savings due to prevention) is difficult as HAI changes cost by extending patient length of stay, yet, length of stay is a major risk factor for HAI. This endogeneity bias can confound attempts to measure accurately the cost of HAI. We propose a two-stage instrumental variables estimation strategy that explicitly controls for the endogeneity between risk of HAI and length of stay. We find that a 10% reduction in ex ante risk of HAI results in an expected savings of £693 ($US 984).

A Petrov-Galerkin method for a singularly perturbed ordinary differential equation with non-smooth data

In this paper, a singularly perturbed ordinary differential equation with non-smooth data is considered. The numerical method is generated by means of a Petrov-Galerkin finite element method with the piecewise-exponential test function and the piecewise-linear trial function. At the discontinuous point of the coefficient, a special technique is used. The method is shown to be first-order accurate and singular perturbation parameter uniform convergence. Finally, numerical results are presented, which are in agreement with theoretical results.