Nonparametric estimation of time-dependent ROC curves conditional on a continuous covariate

The receiver-operating characteristic (ROC) curve is the most widely used measure for evaluating the performance of a diagnostic biomarker when predicting a binary disease outcome. The ROC curve displays the true positive rate (or sensitivity) and the false positive rate (or 1-specificity) for different cut-off values used to classify an individual as healthy or diseased. In time-to-event studies, however, the disease status (e.g. death or alive) of an individual is not a fixed characteristic, and it varies along the study. In such cases, when evaluating the performance of the biomarker, several issues should be taken into account: first, the time-dependent nature of the disease status; and second, the presence of incomplete data (e.g. censored data typically present in survival studies). Accordingly, to assess the discrimination power of continuous biomarkers for time-dependent disease outcomes, time-dependent extensions of true positive rate, false positive rate, and ROC curve have been recently proposed. In this work, we present new nonparametric estimators of the cumulative/dynamic time-dependent ROC curve that allow accounting for the possible modifying effect of current or past covariate measures on the discriminatory power of the biomarker. The proposed estimators can accommodate right-censored data, as well as covariate-dependent censoring. The behavior of the estimators proposed in this study will be explored through simulations and illustrated using data from a cohort of patients who suffered from acute coronary syndrome.

Nonparametric estimation of the survival function for ordered multivariate failure time data: a comparative study

In longitudinal studies of disease, patients may experience several events through a follow-up period. In these studies, the sequentially ordered events are often of interest and lead to problems that have received much attention recently. Issues of interest include the estimation of bivariate survival, marginal distributions and the conditional distribution of gap times. In this work we consider the estimation of the survival function conditional to a previous event. Different nonparametric approaches will be considered for estimating these quantities, all based on the Kaplan-Meier estimator of the survival function. We explore the finite sample behavior of the estimators through simulations. The different methods proposed in this article are applied to a data set from a German Breast Cancer Study. The methods are used to obtain predictors for the conditional survival probabilities as well as to study the influence of recurrence in overall survival.

Parametric estimation of pulse arrival time: a robust approach to pulse wave velocity.

Pulse wave velocity (PWV) is a surrogate of arterial stiffness and represents a non-invasive marker of cardiovascular risk. The non-invasive measurement of PWV requires tracking the arrival time of pressure pulses recorded in vivo, commonly referred to as pulse arrival time (PAT). In the state of the art, PAT is estimated by identifying a characteristic point of the pressure pulse waveform. This paper demonstrates that for ambulatory scenarios, where signal-to-noise ratios are below 10 dB, the performance in terms of repeatability of PAT measurements through characteristic points identification degrades drastically. Hence, we introduce a novel family of PAT estimators based on the parametric modeling of the anacrotic phase of a pressure pulse. In particular, we propose a parametric PAT estimator (TANH) that depicts high correlation with the Complior(R) characteristic point D1 (CC = 0.99), increases noise robustness and reduces by a five-fold factor the number of heartbeats required to obtain reliable PAT measurements.

Estimation of time of death in relation to gastric contents

Introduction: Estimation of the time since death based on the gastric content is still a controversy subject. Many studies have been achieved leaving the same incertitude: the intra- and inter-individual variability. Aim: After a homicidal case where a specialized gastroenterologist was cited to estimate the time of death based on the gastric contents and his experience in clinical practice. Consequently we decided to make a review of the scientific literature to see if that method was more reliable nowadays. Material and methods: We chose articles from 1979 that describe the estimation of the gastric emptying rate according to several factors and the forensic articles about the estimation of the time of death in relation with the gastric content. Results: Most of the articles cited by the specialized gastroenterologist were studies about living healthy people and the effects of several factors (medication, supine versus upside-down position, body mass index or different type of food). Forensic articles frequently concluded that the estimation of the time since death by analyzing the gastric content can be used but not as the unique method. Conclusion: Estimation of the time since death by analyze of the gastric contents is a method that can be used nowadays. But it cannot be the only method as the inter- and intra-individual variability remains an important bias.

Sequential estimation of time-varying multipath channel for MIMO-OFDM systems

In this paper, we introduce a pilot-aided multipath channel estimator for Multiple-Input Multiple-Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) systems. Typical estimation algorithms assume the number of multipath components and delays to be known and constant, while theiramplitudes may vary in time. In this work, we focus on the more realistic assumption that also the number of channel taps is unknown and time-varying. The estimation problem arising from this assumption is solved using Random Set Theory (RST), which is a probability theory of finite sets. Due to the lack of a closed form of the optimal filter, a Rao-Blackwellized Particle Filter (RBPF) implementation of the channel estimator is derived. Simulation results demonstrate the estimator effectiveness.

Chemical labelling of oyster shells used for time-calibrated high-resolution Mg/Ca ratios: A tool for estimation of past seasonal temperature variations

The geochemical compositions of biogenic carbonates are increasingly used for palaeoenvironmental reconstructions. The skeletal delta O-18 temperature relationship is dependent on water salinity, so many recent studies have focused on the Mg/Ca and Sr/Ca ratios because those ratios in water do not change significantly on short time scales. Thus, those elemental ratios are considered to be good palaeotemperature proxies in many biominerals, although their use remains ambiguous in bivalve shells. Here, we present the high-resolution Mg/Ca ratios of two modern species of juvenile and adult oyster shells, Crassostrea gigas and Ostrea edulis. These specimens were grown in controlled conditions for over one year in two different locations. In situ monthly Mn-marking of the shells has been used for day calibration. The daily Mg/Ca.ratios in the shell have been measured with an electron microprobe. The high frequency Mg/Ca variation of all specimens displays good synchronism with lunar cycles, suggesting that tides strongly influence the incorporation of Mg/Ca into the shells. Highly significant correlation coefficients (0.70<R<0.83, p<0.0001) between the Mg/Ca ratios and the seawater temperature are obtained only for juvenile C. gigas samples, while metabolic control of Mg/Ca incorporation and lower shell growth rates preclude the use of the Mg/Ca ratio in adult shells as a palaeothermometer. Data from three juvenile C. gigas shells from the two study sites are selected to establish a relationship: T = 3.77Mg/Ca + 1.88, where T is in degrees C and Mg/Ca in mmol/mol. (c) 2012 Elsevier B.V. All rights reserved.

Nets: Network estimation for time series

This work proposes novel network analysis techniques for multivariate time series.We define the network of a multivariate time series as a graph where verticesdenote the components of the process and edges denote non zero long run partialcorrelations. We then introduce a two step LASSO procedure, called NETS, toestimate high dimensional sparse Long Run Partial Correlation networks. This approachis based on a VAR approximation of the process and allows to decomposethe long run linkages into the contribution of the dynamic and contemporaneousdependence relations of the system. The large sample properties of the estimatorare analysed and we establish conditions for consistent selection and estimation ofthe non zero long run partial correlations. The methodology is illustrated with anapplication to a panel of U.S. bluechips.

A detailed urinary excretion time course study of captan and folpet biomarkers in workers for the estimation of dose, main route-of-entry and most appropriate sampling and analysis strategies

Captan and folpet are two fungicides largely used in agriculture, but biomonitoring data are mostly limited to measurements of captan metabolite concentrations in spot urine samples of workers, which complicate interpretation of results in terms of internal dose estimation, daily variations according to tasks performed, and most plausible routes of exposure. This study aimed at performing repeated biological measurements of exposure to captan and folpet in field workers (i) to better assess internal dose along with main routes-of-entry according to tasks and (ii) to establish most appropriate sampling and analysis strategies. The detailed urinary excretion time courses of specific and non-specific biomarkers of exposure to captan and folpet were established in tree farmers (n = 2) and grape growers (n = 3) over a typical workweek (seven consecutive days), including spraying and harvest activities. The impact of the expression of urinary measurements [excretion rate values adjusted or not for creatinine or cumulative amounts over given time periods (8, 12, and 24 h)] was evaluated. Absorbed doses and main routes-of-entry were then estimated from the 24-h cumulative urinary amounts through the use of a kinetic model. The time courses showed that exposure levels were higher during spraying than harvest activities. Model simulations also suggest a limited absorption in the studied workers and an exposure mostly through the dermal route. It further pointed out the advantage of expressing biomarker values in terms of body weight-adjusted amounts in repeated 24-h urine collections as compared to concentrations or excretion rates in spot samples, without the necessity for creatinine corrections.

On the inclusion of channel's time dependence in a hidden Markov model for blind channel estimation

In this paper, the theory of hidden Markov models (HMM) isapplied to the problem of blind (without training sequences) channel estimationand data detection. Within a HMM framework, the Baum–Welch(BW) identification algorithm is frequently used to find out maximum-likelihood (ML) estimates of the corresponding model. However, such a procedureassumes the model (i.e., the channel response) to be static throughoutthe observation sequence. By means of introducing a parametric model fortime-varying channel responses, a version of the algorithm, which is moreappropriate for mobile channels [time-dependent Baum-Welch (TDBW)] isderived. Aiming to compare algorithm behavior, a set of computer simulationsfor a GSM scenario is provided. Results indicate that, in comparisonto other Baum–Welch (BW) versions of the algorithm, the TDBW approachattains a remarkable enhancement in performance. For that purpose, onlya moderate increase in computational complexity is needed.

Unifying Time to Contact Estimation and Collision Avoidance across Species

The -function and the -function are phenomenological models that are widely used in the context of timing interceptive actions and collision avoidance, respectively. Both models were previously considered to be unrelated to each other: is a decreasing function that provides an estimation of time-to-contact (ttc) in the early phase of an object approach; in contrast, has a maximum before ttc. Furthermore, it is not clear how both functions could be implemented at the neuronal level in a biophysically plausible fashion. Here we propose a new framework the corrected modified Tau function capable of predicting both -type ("") and -type ("") responses. The outstanding property of our new framework is its resilience to noise. We show that can be derived from a firing rate equation, and, as , serves to describe the response curves of collision sensitive neurons. Furthermore, we show that predicts the psychophysical performance of subjects determining ttc. Our new framework is thus validated successfully against published and novel experimental data. Within the framework, links between -type and -type neurons are established. Therefore, it could possibly serve as a model for explaining the co-occurrence of such neurons in the brain.

Real-time tree-foliage surface estimation using a ground laser scanner

The optimization of most pesticide and fertilizer applications is based on overall grove conditions. In this work we measurements. Recently, Wei [9, 10] used a terrestrial propose a measurement system based on a ground laser scanner to LIDAR to measure tree height, width and volume developing estimate the volume of the trees and then extrapolate their foliage a set of experiments to evaluate the repeatability and surface in real-time. Tests with pear trees demonstrated that the accuracy of the measurements, obtaining a coefficient of relation between the volume and the foliage can be interpreted as variation of 5.4% and a relative error of 4.4% in the linear with a coefficient of correlation (R) of 0.81 and the foliar estimation of the volume but without real-time capabilities. surface can be estimated with an average error less than 5 %.

Unifying Time to Contact Estimation and Collision Avoidance across Species

The -function and the -function are phenomenological models that are widely used in the context of timing interceptive actions and collision avoidance, respectively. Both models were previously considered to be unrelated to each other: is a decreasing function that provides an estimation of time-to-contact (ttc) in the early phase of an object approach; in contrast, has a maximum before ttc. Furthermore, it is not clear how both functions could be implemented at the neuronal level in a biophysically plausible fashion. Here we propose a new framework- the corrected modified Tau function- capable of predicting both -type ("") and -type ("") responses. The outstanding property of our new framework is its resilience to noise. We show that can be derived from a firing rate equation, and, as , serves to describe the response curves of collision sensitive neurons. Furthermore, we show that predicts the psychophysical performance of subjects determining ttc. Our new framework is thus validated successfully against published and novel experimental data. Within the framework, links between -type and -type neurons are established. Therefore, it could possibly serve as a model for explaining the co-occurrence of such neurons in the brain.

Limited sampling strategies for estimation of cyclosporine exposure in pediatric hematopoietic stem cell transplant recipients : methodological improvement and introduction of sampling time deviation analysis.

Le suivi thérapeutique est recommandé pour l’ajustement de la dose des agents immunosuppresseurs. La pertinence de l’utilisation de la surface sous la courbe (SSC) comme biomarqueur dans l’exercice du suivi thérapeutique de la cyclosporine (CsA) dans la transplantation des cellules souches hématopoïétiques est soutenue par un nombre croissant d’études. Cependant, pour des raisons intrinsèques à la méthode de calcul de la SSC, son utilisation en milieu clinique n’est pas pratique. Les stratégies d’échantillonnage limitées, basées sur des approches de régression (R-LSS) ou des approches Bayésiennes (B-LSS), représentent des alternatives pratiques pour une estimation satisfaisante de la SSC. Cependant, pour une application efficace de ces méthodologies, leur conception doit accommoder la réalité clinique, notamment en requérant un nombre minimal de concentrations échelonnées sur une courte durée d’échantillonnage. De plus, une attention particulière devrait être accordée à assurer leur développement et validation adéquates. Il est aussi important de mentionner que l’irrégularité dans le temps de la collecte des échantillons sanguins peut avoir un impact non-négligeable sur la performance prédictive des R-LSS. Or, à ce jour, cet impact n’a fait l’objet d’aucune étude. Cette thèse de doctorat se penche sur ces problématiques afin de permettre une estimation précise et pratique de la SSC. Ces études ont été effectuées dans le cadre de l’utilisation de la CsA chez des patients pédiatriques ayant subi une greffe de cellules souches hématopoïétiques. D’abord, des approches de régression multiple ainsi que d’analyse pharmacocinétique de population (Pop-PK) ont été utilisées de façon constructive afin de développer et de valider adéquatement des LSS. Ensuite, plusieurs modèles Pop-PK ont été évalués, tout en gardant à l’esprit leur utilisation prévue dans le contexte de l’estimation de la SSC. Aussi, la performance des B-LSS ciblant différentes versions de SSC a également été étudiée. Enfin, l’impact des écarts entre les temps d’échantillonnage sanguins réels et les temps nominaux planifiés, sur la performance de prédiction des R-LSS a été quantifié en utilisant une approche de simulation qui considère des scénarios diversifiés et réalistes représentant des erreurs potentielles dans la cédule des échantillons sanguins. Ainsi, cette étude a d’abord conduit au développement de R-LSS et B-LSS ayant une performance clinique satisfaisante, et qui sont pratiques puisqu’elles impliquent 4 points d’échantillonnage ou moins obtenus dans les 4 heures post-dose. Une fois l’analyse Pop-PK effectuée, un modèle structural à deux compartiments avec un temps de délai a été retenu. Cependant, le modèle final - notamment avec covariables - n’a pas amélioré la performance des B-LSS comparativement aux modèles structuraux (sans covariables). En outre, nous avons démontré que les B-LSS exhibent une meilleure performance pour la SSC dérivée des concentrations simulées qui excluent les erreurs résiduelles, que nous avons nommée « underlying AUC », comparée à la SSC observée qui est directement calculée à partir des concentrations mesurées. Enfin, nos résultats ont prouvé que l’irrégularité des temps de la collecte des échantillons sanguins a un impact important sur la performance prédictive des R-LSS; cet impact est en fonction du nombre des échantillons requis, mais encore davantage en fonction de la durée du processus d’échantillonnage impliqué. Nous avons aussi mis en évidence que les erreurs d’échantillonnage commises aux moments où la concentration change rapidement sont celles qui affectent le plus le pouvoir prédictif des R-LSS. Plus intéressant, nous avons mis en exergue que même si différentes R-LSS peuvent avoir des performances similaires lorsque basées sur des temps nominaux, leurs tolérances aux erreurs des temps d’échantillonnage peuvent largement différer. En fait, une considération adéquate de l'impact de ces erreurs peut conduire à une sélection et une utilisation plus fiables des R-LSS. Par une investigation approfondie de différents aspects sous-jacents aux stratégies d’échantillonnages limités, cette thèse a pu fournir des améliorations méthodologiques notables, et proposer de nouvelles voies pour assurer leur utilisation de façon fiable et informée, tout en favorisant leur adéquation à la pratique clinique.

Achieving Real-Time Mode Estimation through Offline Compilation

As exploration of our solar system and outerspace move into the future, spacecraft are being developed to venture on increasingly challenging missions with bold objectives. The spacecraft tasked with completing these missions are becoming progressively more complex. This increases the potential for mission failure due to hardware malfunctions and unexpected spacecraft behavior. A solution to this problem lies in the development of an advanced fault management system. Fault management enables spacecraft to respond to failures and take repair actions so that it may continue its mission. The two main approaches developed for spacecraft fault management have been rule-based and model-based systems. Rules map sensor information to system behaviors, thus achieving fast response times, and making the actions of the fault management system explicit. These rules are developed by having a human reason through the interactions between spacecraft components. This process is limited by the number of interactions a human can reason about correctly. In the model-based approach, the human provides component models, and the fault management system reasons automatically about system wide interactions and complex fault combinations. This approach improves correctness, and makes explicit the underlying system models, whereas these are implicit in the rule-based approach. We propose a fault detection engine, Compiled Mode Estimation (CME) that unifies the strengths of the rule-based and model-based approaches. CME uses a compiled model to determine spacecraft behavior more accurately. Reasoning related to fault detection is compiled in an off-line process into a set of concurrent, localized diagnostic rules. These are then combined on-line along with sensor information to reconstruct the diagnosis of the system. These rules enable a human to inspect the diagnostic consequences of CME. Additionally, CME is capable of reasoning through component interactions automatically and still provide fast and correct responses. The implementation of this engine has been tested against the NEAR spacecraft advanced rule-based system, resulting in detection of failures beyond that of the rules. This evolution in fault detection will enable future missions to explore the furthest reaches of the solar system without the burden of human intervention to repair failed components.