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Average (bio)equivalence tests are used to assess if a parameter, like the mean difference in treatment response between two conditions for example, lies within a given equivalence interval, hence allowing to conclude that the conditions have ‘equivalent’ means. TheTwo One-Sided Tests(TOST) procedure, consisting in testing whether the target parameter is respectively significantly greater and lower than some pre-defined lower and upper equivalence limits, is typically used in this context, usually by checking whether the confidence interval for the target parameter lies within these limits. This intuitive and visual procedure is however known to be conservative, especially in the case of highly variable drugs, where it shows a rapid power loss, often reaching zero, hence making it impossible to conclude for equivalence when it is actually true. Here, we propose a finite sample correction of the TOST procedure, theα-TOST, which consists in a correction of the significance level of the TOST allowing to guarantee a test size (or type-I error rate) ofα. This new procedure essentially corresponds to a finite sample and variability correction of the TOST procedure. We show that this procedure is uniformly more powerful than the TOST, easy to compute, and that its operating characteristics outperform the ones of its competitors. A case study about econazole nitrate deposition in porcine skin is used to illustrate the benefits of the proposed method and its advantages compared to other available procedures.

Evolution of volcanic plumbing systems towards eruptions of different styles and sizes largely depends on processes at crustal depths that are outside our observational capabilities. Theseprocesses can be modeled and the outputs of the simulations can be compared with the chemistry of the erupted products, geophysical and geodetic data to retrieve information on the architecture of the plumbing system and the processes leading to eruption. The interaction between magmas with different physical and chemical properties often precedes volcanic eruptions. Thus, sophisticated numerical models havebeen developed that describe in detail the dynamics of interacting magmas, specifically aimed at evaluating pre-eruptive magma mingling and mixing timescales. However, our ability to explore the parameters space in order to match petrological and geophysical observations is limited by the extremely high computational costs of these multiphase, multicomponent computational fluid dynamics simulations. To overcome these limitations, we present a statistical emulator that is able to reproduce the numerical simulations results providing the temporal evolution of the distribution of magma chemistry as a function of a set of input parameters such as magmadensities and reservoir shapes. The whole rock composition of volcanic rocks is one of the mostcommon measurable parameter collected for eruptions. The statistical emulator can be usedto invert the observed distribution of whole rock chemistry to determine the duration of interaction between magmas preceding an eruption and identify the best matching input paramaters of the numerical model. Importantly, the statistical emulator intrinsically includes error propagation, thus providing confidence intervals on predicted interaction timescales on the base of the intrinsic uncertainty of the input parameters of the numerical simulations.

In this work we address the problem of rigorously evaluating the performances of a inertial navigation system under design in presence of multiple alternative choices. We introduce a framework based on Monte-Carlo simulations in which a standard extended Kalman filter is coupled with realistic and user-configurable noise generation mechanisms and attempts to recover a reference trajectory from noisy measurements. The evaluation of several statistical metrics of the solution, aggregated over hundreds of realizations, gives a reasonable estimate of the expected performances of the system in real-world conditions and allow the user to operate the choice between alternative setups. To show the generality of our approach, we consider an example application to the problem of stochastic calibration. Two competing stochastic modeling techniques, namely, the widely popular Allan variance linear regression, and the emerging generalised method of wavelet moments are rigorously compared in terms of the framework defined metrics and in multiple scenarios. We find that the latter provides substantial advantages and should be preferred, at least for certain classes of inertial sensors. Our framework allows to consider a wide range of problems related to the quantification of navigation system performances such as, for example, the robustness of an INS with respect to outliers or other modeling imperfections. While real world experiments are essential to assess to performance of new methods they tend to be costly and are typically unable to lead to a sufficient number of replicates to evaluate, for example, the correctness of estimated uncertainty. Therefore, our method can bridge the gap between these experiments and pure statistical consideration as done, for example, in the stochastic calibration literature.

Motivation Identification of system-wide causal relationships can contribute to our understanding of long-distance, intercellular signalling in biological organisms. Dynamic transcriptome analysis holds great potential to uncover coordinated biological processes between organs. However, many existing dynamic transcriptome studies are characterized by sparse and often unevenly spaced time points that make the identification of causal relationships across organs analytically challenging. Application of existing statistical models, designed for regular time series with abundant time points, to sparse data may fail to reveal biologically significant, causal relationships. With increasing research interest in biological time series data, there is a need for new statistical methods that are able to determine causality within and between time series data sets. Here, a statistical framework was developed to identify (Granger) causal gene-gene relationships of unevenly spaced, multivariate time series data from two different tissues of Arabidopsis thaliana in response to a nitrogen signal. Results This work delivers a statistical approach for modelling irregularly sampled bivariate signals which embeds functions from the domain of engineering that allow to adapt the model’s dependence structure to the specific sampling time. Using maximum-likelihood to estimate the parameters of this model for each bivariate time series, it is then possible to use bootstrap procedures for small samples (or asymptotics for large samples) in order to test for Granger-Causality. When applied to the A.thaliana data, the proposed approach produced 3078 significant interactions, in which 2012 interactions have root causal genes and 1066 interactions have shoot causal genes. Many of the predicted causal and target genes are known players in local and long-distance nitrogen signalling, including genes encoding transcription factors, hormones and signalling peptides. Of the 1007 total causal genes (either organ), 384 are either known or predicted mobile transcripts, suggesting that the identified causal genes may be directly involved in long-distance nitrogen signalling through intercellular interactions. The model predictions and subsequent network analysis identified nitrogen-responsive genes that can be further tested for their specific roles in long-distance nitrogen signalling. Availability and implementation The method was developed with the R statistical software and is made available through the R package ‘irg’ hosted on the GitHub repository https://github.com/SMAC-Group/irg where also a running example vignette can be found (https://smac-group.github.io/irg/articles/vignette.html). A few signals from the original data set are made available in the package as an example to apply the method and the complete A.thaliana data can be found at: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE97500. Supplementary information Supplementary data are available at Bioinformatics online.

Conventionally, natural hazard scholars quantify social vulnerability based on social indicators to manifest the extent to which locational communities are susceptible to adverse impacts of natural...

The Global Navigation Satellite System (GNSS) daily position time series are often described as the sum of stochastic processes and geophysical signals which allow studying global and local geodynamical effects such as plate tectonics, earthquakes, or ground water variations. In this work we propose to extend the Generalized Method of Wavelet Moments (GMWM) to estimate the parameters of linear models with correlated residuals. This statistical inferential framework is applied to GNSS daily position time series data to jointly estimate functional (geophysical) as well as stochastic noise models. Our method is called GMWMX, with X standing for eXogeneous variable: it is semi-parametric, computationally efficient and scalable. Unlike standard methods such as the widely used Maximum Likelihood Estimator (MLE), our methodology offers statistical guarantees, such as consistency and asymptotic normality, without relying on strong parametric assumptions. At the Gaussian model, our results show that the estimated parameters are similar to the ones obtained with the MLE. The computational performances of our approach has important practical implications. Indeed, the estimation of the parameters of large networks of thousands of GNSS stations quickly becomes computationally prohibitive. Compared to standard methods, the processing time of the GMWMX is over $1000$ times faster and allows the estimation of large scale problems within minutes on a standard computer. We validate the performances of our method via Monte-Carlo simulations by generating GNSS daily position time series with missing observations and we consider composite stochastic noise models including processes presenting long-range dependence such as power-law or Mat\'ern processes. The advantages of our method are also illustrated using real time series from GNSS stations located in the Eastern part of the USA., Comment: 30 pages, 11 figures, 3 tables

We develop a penalized two-pass regression with time-varying factor loadings. The penalization in the first pass enforces sparsity for the time-variation drivers while also maintaining compatibility with the no-arbitrage restrictions by regularizing appropriate groups of coefficients. The second pass delivers risk premia estimates to predict equity excess returns. Our Monte Carlo results and our empirical results on a large cross-sectional data set of US individual stocks show that penalization without grouping can yield to nearly all estimated time-varying models violating the no-arbitrage restrictions. Moreover, our results demonstrate that the proposed method reduces the prediction errors compared to a penalized approach without appropriate grouping or a time-invariant factor model.

The measurement noise of a terrestrial laser scanner (TLS) is correlated. Neglecting those correlations affects the dispersion of the parameters when the TLS point clouds are mathematically modelled: statistical tests for the detection of outliers or deformation become misleading. The account for correlations is, thus, mandatory to avoid unfavourable decisions. Unfortunately, fully populated variance covariance matrices (VCM) are often associated with computational burden. To face that challenge, one answer is to rescale a diagonal VCM with a simple und physically justifiable variance inflation factor (VIF). Originally developed for a short-range correlation model, we extend the VIF to account for long-range dependence coming from, for example, atmospheric turbulent effects. The validation of the VIF is performed for the congruency test for deformation with Monte Carlo simulations. Our real application uses data from a bridge under load.

The common approach to inertial sensor calibration for navigation purposes has been to model the stochastic error signals of individual sensors independently, whether as components of a single inertial measurement unit (IMU) in different directions or arrayed in the same direction for redundancy. These signals usually have an extremely complex spectral structure that is often described using latent (or composite) models composed by a sum of underlying models. A large amount of research in this domain has been focused on the latter aspect through the proposal of various methods that have been able to improve the estimation of these models both from a computational and a statistical point of view. However, the separate calibration of the individual sensors is still unable to take into account the dependence between each of them which can have an important impact on the precision of the navigation systems. In this paper, we develop a new approach to simultaneously model both the individual signals and the dependence between them by studying the quantity called Wavelet Cross-Covariance and using it to extend the application of the Generalized Method of Wavelet Moments. This new method can be used in other settings for time series modeling, especially in cases where the dependence among signals may be hard to detect. Moreover, in the field of inertial sensor calibration, this approach can deliver important contributions among which the possibility to test dependence between sensors, integrate their dependence within the navigation filter and construct an optimal virtual sensor that can be used to simplify and improve navigation accuracy. The advantages of this method and its usefulness for inertial sensor calibration are highlighted through a simulation study and an applied example with a small array of XSens MTi-G IMUs.

The existing prediction models for earthquake fatalities usually require a detailed building inventory that might not be readily available. In addition, existing models tend to overlook the socioeconomic characteristics of communities of interest as well as zero-fatality data points. This paper presents a methodology that develops a probabilistic zero-inflated beta regression model to predict earthquake fatality rates given the geographic distributions of earthquake intensities with data reflecting community vulnerability. As an illustration, the prediction model is calibrated using fatality data from 61 earthquakes affecting Taiwan from 1999 to 2016, as well as information on the socioeconomic and environmental characteristics of the affected communities. Using a local seismic hazard map, the calibrated prediction model is used in a seismic risk analysis for Taiwan that predicts the expected fatality rates and counts caused by earthquakes in future years.

Background Medical schools can contribute to the insufficient primary care physician workforce by influencing students’ career preferences. Primary care career choice evolves between matriculation and graduation and is influenced by several individual and contextual factors. This study explored the longitudinal dynamics of primary care career intentions and the association of students’ motives for becoming doctors with these intentions in a cohort of undergraduate medical students followed over a four-year period. Methods The sample consisted of medical students from two classes recruited into a cohort study during their first academic year, and who completed a yearly survey over a four-year period from their third (end of pre-clinical curriculum) to their sixth (before graduation) academic year. Main outcome measures were students’ motives for becoming doctors (ten motives rated on a 6-point scale) and career intentions (categorized into primary care, non-primary care, and undecided). Population-level flows of career intentions were investigated descriptively. Changes in the rating of motives over time were analyzed using Wilcoxon tests. Two generalized linear mixed models were used to estimate which motives were associated with primary care career intentions. Results The sample included 217 students (60% females). Career intentions mainly evolved during clinical training, with smaller changes at the end of pre-clinical training. The proportion of students intending to practice primary care increased over time from 12.8% (year 3) to 24% (year 6). Caring for patients was the most highly rated motive for becoming a doctor. The importance of the motives cure diseases, saving lives, and vocation decreased over time. Primary care career intentions were positively associated with the motives altruism and private practice, and negatively associated with the motives prestige, academic interest and cure diseases. Conclusion Our study indicates that career intentions are not fixed and change mainly during clinical training, supporting the influence of clinical experiences on career-related choices. The impact of students’ motives on primary care career choice suggests strategies to increase the attractivity of this career, such as reinforcing students’ altruistic values and increasing the academic recognition of primary care.

Facial angiofibromas are benign tumors characteristic of tuberous sclerosis complex. The disease involves the mTOR pathway and the cutaneous manifestation responds to topical treatment with sirolimus (SIR). However, there are no approved topical SIR products and extemporaneous formulations have been sub-optimal. The aims of this study were (i) to develop aqueous formulations of SIR loaded in polymeric micelles prepared using D-α-tocopherol polyethylene glycol 1000 succinate (TPGS) and (ii) to use the cutaneous biodistribution method, in conjunction with a new statistical approach, to investigate the feasibility of SIR delivery to the viable epidermis. Optimized micelle solutions and hydrogels (0.2%) were developed and stable at 4 °C for at least 6 and 3 months, respectively. Cutaneous delivery experiments (infinite and finite dose) using porcine skin demonstrated that both formulations increased SIR cutaneous bioavailability as compared to the control (ointment 0.2%). Moreover, studies with the micellar hydrogel 0.2% demonstrated SIR deposition in the viable epidermis with no transdermal permeation. These encouraging results confirmed that polymeric micelles enabled development of aqueous SIR formulations capable of targeted epidermal delivery. Furthermore, the cutaneous biodistribution provided a detailed insight into drug bioavailability in the different skin compartments that could complement/explain clinical observations of formulation efficacy.

Inertial sensor calibration plays a progressively important role in many areas of research among which navigation engineering. By performing this task accurately, it is possible to significantly increase general navigation performance by correctly filtering out the deterministic and stochastic measurement errors that characterize such devices. While different techniques are available to model and remove the deterministic errors, there has been considerable research over the past years with respect to modelling the stochastic errors which have complex structures. In order to do the latter, different replicates of these error signals are collected and a model is identified and estimated based on one of these replicates. While this procedure has allowed to improve navigation performance, it has not yet taken advantage of the information coming from all the other replicates collected on the same sensor. However, it has been observed that there is often a change of error behaviour between replicates which can also be explained by different (constant) external conditions under which each replicate was taken. Whatever the reason for the difference between replicates, it appears that the model structure remains the same between replicates but the parameter values vary. In this work we therefore consider and study the properties of different approaches that allow to combine the information from all replicates considering this phenomenon, confirming their validity both in simulation settings and also when applied to real inertial sensor error signals. By taking into account parameter variation between replicates, this work highlights how these approaches can improve the average navigation precision as well as obtain reliable estimates of the uncertainty of the navigation solution.

Inertial sensor calibration plays a progressively important role in many areas of research among which navigation engineering. By performing this task accurately, it is possible to significantly increase general navigation performance by correctly filtering out the deterministic and stochastic measurement errors that characterize such devices. While different techniques are available to model and remove the deterministic errors, there has been considerable research over the past years with respect to modelling the stochastic errors which have complex structures. In order to do the latter, different replicates of these error signals are collected and a model is identified and estimated based on one of these replicates. While this procedure has allowed to improve navigation performance, it has not yet taken advantage of the information coming from all the other replicates collected on the same sensor. However, it has been observed that there is often a change of error behaviour between replicates which can also be explained by different (constant) external conditions under which each replicate was taken. Whatever the reason for the difference between replicates, it appears that the model structure remains the same between replicates but the parameter values vary. In this work we therefore consider and study the properties of different approaches that allow to combine the information from all replicates considering this phenomenon, confirming their validity both in simulation settings and also when applied to real inertial sensor error signals. By taking into account parameter variation between replicates, this work highlights how these approaches can improve the average navigation precision as well as obtain reliable estimates of the uncertainty of the navigation solution.

Breast cancer is one of the most frequent cancers affecting women. Non-coding micro RNAs (miRNAs) seem to play an important role in the regulation of pathways involved in tumor occurrence and progression. Extending on the research in Haakensen et al., where significant miRNAs were selected as being associated with the progression from normal breast tissue to breast cancer, in this work we put forward 112 sets of miRNA combinations, each including at most 5 expressions with high accuracy in discriminating healthy breast tissue from breast carcinoma. Our results are based on a recently developed machine learning technique which, instead of selecting a single model (or combination of features), delivers a set of models with equivalent predictive capabilities that allow to interpret and visualize the interaction of these features. These results shed new light on the biological action of the selected miRNAs which can behave in different ways according to the miRNA network with which they interact.Indeed, these revealed connections may contribute to explain why, in some cases, different studies attribute opposite functions to the same miRNA. It is therefore possible to understand how the role of a genomic variable may change when considered in interaction with other sets of variables, as opposed to only considering its effect when it is evaluated within a unique combination of features. The approach proposed in this work provides a statistical basis for the notion of chameleon miRNAs and is inspired by the emerging field of systems biology.Author SummaryThe notion of a single predictive genomic (statistical) model is replaced by that of a set of models that can be considered as exchangeable due to their indistinguishable (optimal) predictive abilities;Our results indicate that the role of miRNAs cannot be interpreted independently from the combination of features with which they interact and can therefore vary considerably when considered in a network of different combinations. Some miRNAs may act as chameleons and behave in opposite manners thereby showing some kind of antagonistic duality;Some miRNAs are exchangeable inside models with equivalent predictive ability and seem to point to latent biological functions.

ObjectiveTo set up simple and reliable predictive scores for intensive care admissions and deaths in COVID-19 patients. These scores adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guidelines.DesignMonocentric retrospective cohort study run from early March to end of May in Clinique Saint-Pierre Ottignies, a secondary care hospital located in Ottignies-Louvain-la-Neuve, Belgium. The outcomes of the study are (i) admission in the Intensive Care Unit and (ii) death.Data sourcesAll patients admitted in the Emergency Department with a positive RT-PCR SARS-CoV-2 test were included in the study. Routine clinical and laboratory data were collected at their admission and during their stay. Chest X-Rays and CT-Scans were performed and analyzed by a senior radiologist.MethodsA recently published predictive score conducted on a large scale was used as a benchmark value (Liang score)1. Logistic regressions were used to develop predictive scores for (i) admission to ICU among emergency ward patients; (ii) death among ICU patients on 40 clinical variables. These models were based on medical intuition and simple model selection tools. Their predictive capabilities were then compared to Liang score.ResultsOur results suggest that Liang score may not provide reliable guidance for ICU admission and death. Moreover, the performance of this approach is clearly outperformed by models based on simple markers. For example, a logistic regression considering only the LDH yields to similar sensitivity and greater specificity. Finally, all models considered in this study lead to levels of specificity under or equal to 50%.ConclusionsIn our experience, the results of a predictive score based on a large-scale Chinese study cannot be applied in the Belgian population. However, in our small cohort it appears that LDH above 579 UI/L and venous lactate above 3.02 mmol/l may be considered as good predictive biological factors for ICU admission. With regards to death risk, NLR above 22.1, tobacco abuse status and80 % ofrespiratory impairment appears to be relevant predictive factors. A predictive score for admission to ICU or death is desperately needed in secondary hospitals. Optimal allocation of resources guided by evidence-based indicators will best guide patients at time of admission and avoid futile treatments in intensive care units.

Different inertial sensor calibration techniques have been proposed to consider the sources of measurement error from inertial sensors. There has been a significant amount of literature which studies the stochastic errors calibration of such devices. The recent results of [1] have proved that among all possible methods the (Generalized Method of Wavelet Moments) (GMWM) presents various optimality and is computationally reliable. However, the GMWM estimators depend on weight matrix which considerably impact the quality of the estimated stochastic error models. In addition, such models are made of different components (typically high-frequency and low-frequency components) whose impacts on navigation vary depending on the context. For example, the high-frequency component of the error model may be more important when considering low-cost IMUs mounted on small size drones used for short-term missions. On the other hand, the situation may be reversed when considering navigational grade IMUs used, often autonomously, for long-term missions. With these differences, one may wish to select a GMWM estimator whose weight matrix has been tailored to estimate more reliably the elements of an error model believed to have the greatest impacts on navigation accuracy. In this article, we provide a formal answer to this question by proposing an optimally weighted GMWM estimator. Our results show that the proposed estimator is optimal for all parameters of the sensor error model we wish to estimate with the smallest possible uncertainty of the estimation. Therefore, regardless of the application, and independently of the context, the same optimally weighted estimator can be employed.

This paper presents a fragility-based Bayesian formulation to predict earthquake casualty rates for countries worldwide. The earthquake casualty rate of a community is defined as the probab...