Modelling dependence in a ratemaking procedure with multivariate Poisson regression models

When actuaries face with the problem of pricing an insurance contract that contains different types of coverage, such as a motor insurance or homeowner's insurance policy, they usually assume that types of claim are independent. However, this assumption may not be realistic: several studies have shown that there is a positive correlation between types of claim. Here we introduce different regression models in order to relax the independence assumption, including zero-inflated models to account for excess of zeros and overdispersion. These models have been largely ignored to multivariate Poisson date, mainly because of their computational di±culties. Bayesian inference based on MCMC helps to solve this problem (and also lets us derive, for several quantities of interest, posterior summaries to account for uncertainty). Finally, these models are applied to an automobile insurance claims database with three different types of claims. We analyse the consequences for pure and loaded premiums when the independence assumption is relaxed by using different multivariate Poisson regression models and their zero-inflated versions.

Bayesian classification criterion for forensic multivariate data

This study presents a classification criteria for two-class Cannabis seedlings. As the cultivation of drug type cannabis is forbidden in Switzerland, law enforcement authorities regularly ask laboratories to determine cannabis plant's chemotype from seized material in order to ascertain that the plantation is legal or not. In this study, the classification analysis is based on data obtained from the relative proportion of three major leaf compounds measured by gas-chromatography interfaced with mass spectrometry (GC-MS). The aim is to discriminate between drug type (illegal) and fiber type (legal) cannabis at an early stage of the growth. A Bayesian procedure is proposed: a Bayes factor is computed and classification is performed on the basis of the decision maker specifications (i.e. prior probability distributions on cannabis type and consequences of classification measured by losses). Classification rates are computed with two statistical models and results are compared. Sensitivity analysis is then performed to analyze the robustness of classification criteria.

Multimodel inference and multimodel averaging in empirical modeling of occupational exposure levels.

Empirical modeling of exposure levels has been popular for identifying exposure determinants in occupational hygiene. Traditional data-driven methods used to choose a model on which to base inferences have typically not accounted for the uncertainty linked to the process of selecting the final model. Several new approaches propose making statistical inferences from a set of plausible models rather than from a single model regarded as 'best'. This paper introduces the multimodel averaging approach described in the monograph by Burnham and Anderson. In their approach, a set of plausible models are defined a priori by taking into account the sample size and previous knowledge of variables influent on exposure levels. The Akaike information criterion is then calculated to evaluate the relative support of the data for each model, expressed as Akaike weight, to be interpreted as the probability of the model being the best approximating model given the model set. The model weights can then be used to rank models, quantify the evidence favoring one over another, perform multimodel prediction, estimate the relative influence of the potential predictors and estimate multimodel-averaged effects of determinants. The whole approach is illustrated with the analysis of a data set of 1500 volatile organic compound exposure levels collected by the Institute for work and health (Lausanne, Switzerland) over 20 years, each concentration having been divided by the relevant Swiss occupational exposure limit and log-transformed before analysis. Multimodel inference represents a promising procedure for modeling exposure levels that incorporates the notion that several models can be supported by the data and permits to evaluate to a certain extent model selection uncertainty, which is seldom mentioned in current practice.

Consensus summary statement of the International Multidisciplinary Consensus Conference on Multimodality Monitoring in Neurocritical Care : A statement for healthcare professionals from the Neurocritical Care Society and the European Society of Intensive Care Medicine.

Neurocritical care depends, in part, on careful patient monitoring but as yet there are little data on what processes are the most important to monitor, how these should be monitored, and whether monitoring these processes is cost-effective and impacts outcome. At the same time, bioinformatics is a rapidly emerging field in critical care but as yet there is little agreement or standardization on what information is important and how it should be displayed and analyzed. The Neurocritical Care Society in collaboration with the European Society of Intensive Care Medicine, the Society for Critical Care Medicine, and the Latin America Brain Injury Consortium organized an international, multidisciplinary consensus conference to begin to address these needs. International experts from neurosurgery, neurocritical care, neurology, critical care, neuroanesthesiology, nursing, pharmacy, and informatics were recruited on the basis of their research, publication record, and expertise. They undertook a systematic literature review to develop recommendations about specific topics on physiologic processes important to the care of patients with disorders that require neurocritical care. This review does not make recommendations about treatment, imaging, and intraoperative monitoring. A multidisciplinary jury, selected for their expertise in clinical investigation and development of practice guidelines, guided this process. The GRADE system was used to develop recommendations based on literature review, discussion, integrating the literature with the participants' collective experience, and critical review by an impartial jury. Emphasis was placed on the principle that recommendations should be based on both data quality and on trade-offs and translation into clinical practice. Strong consideration was given to providing pragmatic guidance and recommendations for bedside neuromonitoring, even in the absence of high quality data.

A Folk Theorem for Games when Frequent Monitoring Decreases Noise

This paper studies frequent monitoring in an infinitely repeated game with imperfect public information and discounting, where players observe the state of a continuous time Brownian process at moments in time of length _. It shows that a limit folk theorem can be achieved with imperfect public monitoring when players monitor each other at the highest frequency, i.e., _. The approach assumes that the expected joint output depends exclusively on the action profile simultaneously and privately decided by the players at the beginning of each period of the game, but not on _. The strong decreasing effect on the expected immediate gains from deviation when the interval between actions shrinks, and the associated increase precision of the public signals, make the result possible in the limit. JEL: C72/73, D82, L20. KEYWORDS: Repeated Games, Frequent Monitoring, Public Monitoring, Brownian Motion.

Statistical evaluation of the influence of writing postures on on-line signatures. Study of the impact of time.

The aim of this study is to investigate the influence of unusual writing positions on a person's signature, in comparison to a standard writing position. Ten writers were asked to sign their signature six times, in each of four different writing positions, including the standard one. In order to take into consideration the effect of the day-to-day variation, this same process was repeated over 12 sessions, giving a total of 288 signatures per subject. The signatures were collected simultaneously in an off-line and on-line acquisition mode, using an interactive tablet and a ballpoint pen. Unidimensional variables (height to width ratio; time with or without in air displacement) and time-dependent variables (pressure; X and Y coordinates; altitude and azimuth angles) were extracted from each signature. For the unidimensional variables, the position effect was assessed through ANOVA and Dunnett contrast tests. Concerning the time-dependent variables, the signatures were compared by using dynamic time warping, and the position effect was evaluated through classification by linear discriminant analysis. Both of these variables provided similar results: no general tendency regarding the position factor could be highlighted. The influence of the position factor varies according to the subject as well as the variable studied. The impact of the session factor was shown to cover the impact that could be ascribed to the writing position factor. Indeed, the day-to-day variation has a greater effect than the position factor on the studied signature variables. The results of this study suggest guidelines for best practice in the area of signature comparisons and demonstrate the importance of a signature collection procedure covering an adequate number of sampling sessions, with a sufficient number of samples per session.

Health And Social Care Inequalities Monitoring System: First Update Bulletin 2004 (PDF 273 KB)

The Inequalities Monitoring System comprises a basket of indicators which are monitored over time to assess area differences in morbidity, utilisation of and access to health and social care services in Northern Ireland. Inequalities between the 20% most deprived electoral wards and Northern Ireland as a whole are measured with deprived areas identified from an update of the Noble Income domain for current ward boundaries. Results for 20% most rural areas were also compared against Northern Ireland overall using population density from the 2001 Census of Population as a measure of rurality. This report is the first annual update of the baseline results presented in Chapter 8 of Equality and Inequalities in Health and Social care in Northern Ireland – A Statistical Overview (DHSSPS 2004) which focused on 2001/2002. The morbidity and utilisation data in this report are the latest available while the locations of services for the accessibility analysis will be updated in subsequent years åÊ

Health and Social Care Inequalities Monitoring System (PDF 273 KB)

The Inequalities Monitoring System comprises a basket of indicators which are monitored over time to assess area differences in morbidity, utilisation of and access to health and social care services in Northern Ireland. Inequalities between the 20% most deprived electoral wards and Northern Ireland as a whole are measured with deprived areas identified from an update of the Noble Income domain for current ward boundaries. Results for 20% most rural areas were also compared against Northern Ireland overall using population density from the 2001 Census of Population as a measure of rurality. This report is the firståÊ annual update of the baseline results presented in Chapter 8 of Equality and Inequalities in Health and Social care in Northern Ireland – A Statistical Overview (DHSSPS 2004) which focused on 2001/2002. The morbidity and utilisation data in this report are the latest available while the locations of services for the accessibility analysis will be updated in subsequent years. åÊ åÊ

Multivariate QST-FST comparisons: a neutrality test for the evolution of the g matrix in structured populations.

Neutrality tests in quantitative genetics provide a statistical framework for the detection of selection on polygenic traits in wild populations. However, the existing method based on comparisons of divergence at neutral markers and quantitative traits (Q(st)-F(st)) suffers from several limitations that hinder a clear interpretation of the results with typical empirical designs. In this article, we propose a multivariate extension of this neutrality test based on empirical estimates of the among-populations (D) and within-populations (G) covariance matrices by MANOVA. A simple pattern is expected under neutrality: D = 2F(st)/(1 - F(st))G, so that neutrality implies both proportionality of the two matrices and a specific value of the proportionality coefficient. This pattern is tested using Flury's framework for matrix comparison [common principal-component (CPC) analysis], a well-known tool in G matrix evolution studies. We show the importance of using a Bartlett adjustment of the test for the small sample sizes typically found in empirical studies. We propose a dual test: (i) that the proportionality coefficient is not different from its neutral expectation [2F(st)/(1 - F(st))] and (ii) that the MANOVA estimates of mean square matrices between and among populations are proportional. These two tests combined provide a more stringent test for neutrality than the classic Q(st)-F(st) comparison and avoid several statistical problems. Extensive simulations of realistic empirical designs suggest that these tests correctly detect the expected pattern under neutrality and have enough power to efficiently detect mild to strong selection (homogeneous, heterogeneous, or mixed) when it is occurring on a set of traits. This method also provides a rigorous and quantitative framework for disentangling the effects of different selection regimes and of drift on the evolution of the G matrix. We discuss practical requirements for the proper application of our test in empirical studies and potential extensions.

Therapeutic Drug Monitoring of the new targeted anticancer agents imatinib, nilotinib, dasatinib, sunitinib, sorafenib and lapatinib by LC tandem mass spectrometry.

The treatment of some cancer patients has shifted from traditional, non-specific cytotoxic chemotherapy to chronic treatment with molecular targeted therapies. Imatinib mesylate, a selective inhibitor of tyrosine kinases (TKIs) is the most prominent example of this new era and has opened the way to the development of several additional TKIs, including sunitinib, nilotinib, dasatinib, sorafenib and lapatinib, in the treatment of various hematological malignancies and solid tumors. All these agents are characterized by an important inter-individual pharmacokinetic variability, are at risk for drug interactions, and are not devoid of toxicity. Additionally, they are administered for prolonged periods, anticipating the careful monitoring of their plasma exposure via Therapeutic Drug Monitoring (TDM) to be an important component of patients' follow-up. We have developed a liquid chromatography-tandem mass spectrometry method (LC-MS/MS) requiring 100 microL of plasma for the simultaneous determination of the six major TKIs currently in use. Plasma is purified by protein precipitation and the supernatant is diluted in ammonium formate 20 mM (pH 4.0) 1:2. Reverse-phase chromatographic separation of TKIs is obtained using a gradient elution of 20 mM ammonium formate pH 2.2 and acetonitrile containing 1% formic acid, followed by rinsing and re-equilibration to the initial solvent composition up to 20 min. Analyte quantification, using matrix-matched calibration samples, is performed by electro-spray ionization-triple quadrupole mass spectrometry by selected reaction monitoring detection using the positive mode. The method was validated according to FDA recommendations, including assessment of extraction yield, matrix effects variability (<9.6%), overall process efficiency (87.1-104.2%), as well as TKIs short- and long-term stability in plasma. The method is precise (inter-day CV%: 1.3-9.4%), accurate (-9.2 to +9.9%) and sensitive (lower limits of quantification comprised between 1 and 10 ng/mL). This is the first broad-range LC-MS/MS assay covering the major currently in-use TKIs. It is an improvement over previous methods in terms of convenience (a single extraction procedure for six major TKIs, reducing significantly the analytical time), sensitivity, selectivity and throughput. It may contribute to filling the current knowledge gaps in the pharmacokinetics/pharmacodynamics relationships of the latest TKIs developed after imatinib and better define their therapeutic ranges in different patient populations in order to evaluate whether a systematic TDM-guided dose adjustment of these anticancer drugs could contribute to minimize the risk of major adverse reactions and to increase the probability of efficient, long lasting, therapeutic response.

Interest of qualitative and quantitative profiling of endocannabinoids for evaluation of their implication in drug addiction process

Introduction: In the middle of the 90's, the discovery of endogenous ligands for cannabinoid receptors opened a new era in this research field. Amides and esters of arachidonic acid have been identified as these endogenous ligands. Arachidonoylethanolamide (anandamide or AEA) and 2-Arachidonoylglycerol (2-AG) seem to be the most important of these lipid messengers. In addition, virodhamine (VA), noladin ether (2-AGE), and N-arachidonoyl dopamine (NADA) have been shown to bind to CB receptors with varying affinities. During recent years, it has become more evident that the EC system is part of fundamental regulatory mechanisms in many physiological processes such as stress and anxiety responses, depression, anorexia and bulimia, schizophrenia disorders, neuroprotection, Parkinson disease, anti-proliferative effects on cancer cells, drug addiction, and atherosclerosis. Aims: This work presents the problematic of EC analysis and the input of Information Dependant Acquisition based on hybrid triple quadrupole linear ion trap (QqQLIT) system for the profiling of these lipid mediators. Methods: The method was developed on a LC Ultimate 3000 series (Dionex, Sunnyvale, CA, USA) coupled to a QTrap 4000 system (Applied biosystems, Concord, ON, Canada). The ECs were separated on an XTerra C18 MS column (50 × 3.0 mm i.d., 3.5 μm) with a 5 min gradient elution. For confirmatory analysis, an information-dependant acquisition experiment was performed with selected reaction monitoring (SRM) as survey scan and enhanced produced ion (EPI) as dependant scan. Results: The assay was found to be linear in the concentration range of 0.1-5 ng/mL for AEA, 0.3-5 ng/mL for VA, 2-AGE, and NADA and 1-20 ng/mL for 2-AG using 0.5 mL of plasma. Repeatability and intermediate precision were found less than 15% over the tested concentration ranges. Under non-pathophysiological conditions, only AEA and 2-AG were actually detected in plasma with concentration ranges going from 104 to 537 pg/mL and from 2160 to 3990 pg/mL respectively. We have particularly focused our scopes on the evaluation of EC level changes in biological matrices through drug addiction and atherosclerosis processes. We will present preliminary data obtained during pilot study after administration of cannabis on human patients. Conclusion: ECs have been shown to play a key role in regulation of many pathophysiological processes. Medical research in these different fields continues to growth in order to understand and to highlight the predominant role of EC in the CNS and peripheral tissues signalisation. The profiling of these lipids needs to develop rapid, highly sensitive and selective analytical methods.

Oral cancer treatments and adherence: medication event monitoring system assessment for capecitabine

Background: Oncological treatments are traditionally administered via intravenous injection by qualified personnel. Oral formulas which are developing rapidly are preferred by patients and facilitate administration however they may increase non-adherence. In this study 4 common oral chemotherapeutics are given to 50 patients, who are still in the process of inclusion, divided into 4 groups. The aim is to evaluate adherence and offer these patients interdisciplinary support with the joint help of doctors and pharmacists. We present here the results for capecitabine. Materials and Methods: The final goal is to evaluate adhesion in 50 patients split into 4 groups according to oral treatments (letrozole/exemestane, imatinib/sunitinib, capecitabine and temozolomide) using persistence and quality of execution as parameters. These parameters are evaluated using a medication event monitoring system (MEMS®) in addition to routine oncological visits and semi-structured interviews. Patients were monitored for the entire duration of treatment up to a maximum of 1 year. Patient satisfaction was assessed at the end of the monitoring period using a standardized questionary. Results: Capecitabine group included 2 women and 8 men with a median age of 55 years (range: 36−77 years) monitored for an average duration of 100 days (range: 5-210 days). Persistence was 98% and quality of execution 95%. 5 patients underwent cyclic treatment (2 out of 3 weeks) and 5 patients continuous treatment. Toxicities higher than grade 1 were grade 2−3 hand-foot syndrome in 1 patient and grade 3 acute coronary syndrome in 1 patient both without impact on adherence. Patients were satisfied with the interviews undergone during the study (57% useful, 28% very useful, 15% useless) and successfully integrated the MEMS® in their daily lives (57% very easily, 43% easily) according to the results obtained by questionary at the end of the monitoring period. Conclusion: Persistence and quality of execution observed in our Capecitabine group of patients were excellent and better than expected compared to previously published studies. The interdisciplinary approach allowed us to better identify and help patients with toxicities to maintain adherence. Overall patients were satisfied with the global interdisciplinary follow-up. With longer follow up better evaluation of our method and its impact will be possible. Interpretation of the results of patients in the other groups of this ongoing trial will provide us information for a more detailed analysis.

Modern statistical models for forensic fingerprint examinations : a critical review

Over the last decade, the development of statistical models in support of forensic fingerprint identification has been the subject of increasing research attention, spurned on recently by commentators who claim that the scientific basis for fingerprint identification has not been adequately demonstrated. Such models are increasingly seen as useful tools in support of the fingerprint identification process within or in addition to the ACE-V framework. This paper provides a critical review of recent statistical models from both a practical and theoretical perspective. This includes analysis of models of two different methodologies: Probability of Random Correspondence (PRC) models that focus on calculating probabilities of the occurrence of fingerprint configurations for a given population, and Likelihood Ratio (LR) models which use analysis of corresponding features of fingerprints to derive a likelihood value representing the evidential weighting for a potential source.

Multivariate ARIMA Compositional Time Series Analysis

A compositional time series is obtained when a compositional data vector is observed atdifferent points in time. Inherently, then, a compositional time series is a multivariatetime series with important constraints on the variables observed at any instance in time.Although this type of data frequently occurs in situations of real practical interest, atrawl through the statistical literature reveals that research in the field is very much in itsinfancy and that many theoretical and empirical issues still remain to be addressed. Anyappropriate statistical methodology for the analysis of compositional time series musttake into account the constraints which are not allowed for by the usual statisticaltechniques available for analysing multivariate time series. One general approach toanalyzing compositional time series consists in the application of an initial transform tobreak the positive and unit sum constraints, followed by the analysis of the transformedtime series using multivariate ARIMA models. In this paper we discuss the use of theadditive log-ratio, centred log-ratio and isometric log-ratio transforms. We also presentresults from an empirical study designed to explore how the selection of the initialtransform affects subsequent multivariate ARIMA modelling as well as the quality ofthe forecasts