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This paper proposes a generalized framework to analyze spatial count data under a unilateral regular lattice structure based on thinning type models. We start from the simple spatial integer-valued auto-regressive model of order 1. We extend this model in certain directions. First, we consider various distributions as choices for the innovation distribution to allow for additional overdispersion. Second, we allow for use of covariate information, leading to a non-stationary model. Finally, we derive and use other models related to this simple one by considering simplification on the existing model. Inference is based on conditional maximum likelihood approach. We provide simulation results under different scenarios to understand the behaviour of the conditional maximum likelihood. A real data application is also provided. Remarks on how the results extend to other families of models are also given., Peer Reviewed

PURPOSE To better understand the European Society for Medical Oncology-Magnitude of Clinical Benefit Scale version 1.1 (ESMO-MCBS v1.1) and the ASCO Value Framework Net Health Benefit score version 2 (ASCO-NHB v2), ESMO and ASCO collaborated to evaluate the concordance between the frameworks when used to assess clinical benefit attributable to new therapies. METHODS The 102 randomized controlled trials in the noncurative setting already evaluated in the field testing of ESMO-MCBS v1.1 were scored using ASCO-NHB v2 by its developers. Measures of agreement between the frameworks were calculated and receiver operating characteristic curves used to define thresholds for the ASCO-NHB v2 corresponding to ESMO-MCBS v1.1 categories. Studies with discordant scoring were identified and evaluated to understand the reasons for discordance. RESULTS The correlation of the 102 pairs of scores for studies in the noncurative setting is estimated to be 0.68 (Spearman’s rank correlation coefficient; overall survival, 0.71; progression-free survival, 0.67). Receiver operating characteristic curves identified thresholds for ASCO-NHB v2 for facilitating comparisons with ESMO-MCBS v1.1 categories. After applying pragmatic threshold scores of 40 or less (ASCO-NHB v2) and 2 or less (ESMO-MCBS v1.1) for low benefit and 45 or greater (ASCO-NHB v2) and 4 to 5 (ESMO-MCBS v1.1) for substantial benefit, 37 discordant studies were identified. Major factors that contributed to discordance were different approaches to evaluation of relative and absolute gain for overall survival and progression-free survival, crediting tail of the curve gains, and assessing toxicity. CONCLUSION The agreement between the frameworks was higher than observed in other studies that sought to compare them. The factors that contributed to discordant scores suggest potential approaches to improve convergence between the scales., SCOPUS: ar.j, DecretOANoAutActif, info:eu-repo/semantics/published

SCOPUS: le.j, info:eu-repo/semantics/published

Background The European Society for Medical Oncology (ESMO) has developed the ESMO Magnitude of Clinical Benefit Scale (ESMO-MCBS), a tool to assess the magnitude of clinical benefit from new cancer therapies. Grading is guided by a dual rule comparing the relative benefit (RB) and the absolute benefit (AB) achieved by the therapy to prespecified threshold values. The ESMO-MCBS v1.0 dual rule evaluates the RB of an experimental treatment based on the lower limit of the 95%CI (LL95%CI) for the hazard ratio (HR) along with an AB threshold. This dual rule addresses two goals: inclusiveness: not unfairly penalising experimental treatments from trials designed with adequate power targeting clinically meaningful relative benefit; and discernment: penalising trials designed to detect a small inconsequential benefit. Methods Based on 50 000 simulations of plausible trial scenarios, the sensitivity and specificity of the LL95%CI rule and the ESMO-MCBS dual rule, the robustness of their characteristics for reasonable power and range of targeted and true HRs, are examined. The per cent acceptance of maximal preliminary grade is compared with other dual rules based on point estimate (PE) thresholds for RB. Results For particularly small or particularly large studies, the observed benefit needs to be relatively big for the ESMO-MCBS dual rule to be satisfied and the maximal grade awarded. Compared with approaches that evaluate RB using the PE thresholds, simulations demonstrate that the MCBS approach better exhibits the desired behaviour achieving the goals of both inclusiveness and discernment. Conclusions RB assessment using the LL95%CI for HR rather than a PE threshold has two advantages: it diminishes the probability of excluding big benefit positive studies from achieving due credit and, when combined with the AB assessment, it increases the probability of downgrading a trial with a statistically significant but clinically insignificant observed benefit., SCOPUS: ar.j, info:eu-repo/semantics/published

A number of studies suggest that response to antihuman epidermal growth factor receptor-2 (currently known as ERBB2, butreferred to asHER2 in this study) agents differs by estrogen receptor (ER) level status. The clinical relevance of this is unknown., SCOPUS: ar.j, info:eu-repo/semantics/published

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.

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.

When actuaries face with the problem of pricing an insurance contract that contains di®erent 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 di®erent multivariate Poisson regression models in order to relax the independence assumption, including zero-in°ated models to account for excess of zeros and overdispersion. These models have been largely ignored to 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 di®erent types of claims. We analyse the consequences for pure and loaded pre- miums when the independence assumption is relaxed by using di®erent multivariate Poisson regression models and their zero-in°ated versions.

When actuaries face with the problem of pricing an insurance contract that contains di®erent 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 di®erent multivariate Poisson regression models in order to relax the independence assumption, including zero-in°ated models to account for excess of zeros and overdispersion. These models have been largely ignored to 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 di®erent types of claims. We analyse the consequences for pure and loaded pre- miums when the independence assumption is relaxed by using di®erent multivariate Poisson regression models and their zero-in°ated versions.

The problem of clustering large data sets has attracted a lot of current research. The approaches taken are mainly based either on the more efficient implementation or modification of existing methods or/and on the construction of clusters from a small sub-sample of the data and then the assignment of all observations in those clusters. The current paper focuses on the latter direction. An alternative supervised procedure to create the clusters is proposed. For learning the clusters, the procedure is using subsets of the data which are still constructed via sub-sampling but within partitions of the observation space. The general applicability of the approach is discussed together with tuning the parameters that it depends on to increase its ability. The procedure is applied to clustering the navigation patterns in the msnbc.com database.

Regression modelling involving heavy-tailed response distributions, which have heavier tails than the exponential distribution, has become increasingly popular in many insurance settings including non-life insurance. Mixed Exponential models can be considered as a natural choice for the distribution of heavy-tailed claim sizes since their tails are not exponentially bounded. This paper is concerned with introducing a general family of mixed Exponential regression models with varying dispersion which can efficiently capture the tail behaviour of losses. Our main achievement is that we present an Expectation-Maximization (EM)-type algorithm which can facilitate maximum likelihood (ML) estimation for our class of mixed Exponential models which allows for regression specifications for both the mean and dispersion parameters. Finally, a real data application based on motor insurance data is given to illustrate the versatility of the proposed EM-type algorithm.

In view of the economic importance of motor third-party liability insurance in developed countries the construction of optimal BMS has been given considerable interest. However, a major drawback in the construction of optimal BMS is that they fail to account for the variability on premium calculations which are treated as point estimates. The present study addresses this issue. Specifically, nonparametric mixtures of Poisson laws are used to construct an optimal BMS with a finite number of classes. The mixing distribution is estimated by nonparametric maximum likelihood (NPML). The main contribution of this paper is the use of the NPML estimator for the construction of confidence intervals for the premium rates derived by updating the posterior mean claim frequency. Furthermore, we advance one step further by improving the performance of the confidence intervals based on a bootstrap procedure where the estimated mixture is used for resampling. The construction of confidence intervals for the individual premiums based on the asymptotic maximum likelihood theory is beneficial for the insurance company as it can result in accurate and effective adjustments to the premium rating policies from a practical point of view.