B Vandewalle

A robust estimator for the tail index of Pareto-type distributions

In extreme value statistics, the extreme value index is a well-known parameter to measure the tail heaviness of a distribution. Pareto-type distributions, with strictly positive extreme value index (or tail index) are considered. The most prominent extreme value methods are constructed on efficient maximum likelihood estimators based on specific parametric models which are fitted to excesses over large thresholds. Maximum likelihood estimators however are often not very robust, which makes them sensitive to few particular observations. Even in extreme value statistics, where the most extreme data usually receive most attention, this can constitute a serious problem. The problem is illustrated on a real data set from geopedology, in which a few abnormal soil measurements highly influence the estimates of the tail index. In order to overcome this problem, a robust estimator of the tail index is proposed, by combining a refinement of the Pareto approximation for the conditional distribution of relative excesses over a large threshold with an integrated squared error approach on partial density component estimation. It is shown that the influence function of this newly proposed estimator is bounded and through several simulations it is illustrated that it performs reasonably well at contaminated as well as uncontaminated data.

A Robust Estimator of the Tail Index Based on an Exponential Regression Model

The objectives of a robust statistical analysis and of an extreme value analysis apparently are contradictory. Where the extreme data are downweighted in robust statistics, these observations receive most attention in an extreme value approach. The most prominent extreme value methods however are constructed on maximum likelihood estimates based on specific parametric models which are fitted to exceedances over large thresholds. So within an extreme value framework some robust algorithms replacing the maximum likelihood part of this methodology can be of use leading to estimates which are less sensitive to few particular observations. This study is motivated by a soil database quality management project, where in the background of Pareto-type tails, automatic identification of suspicious data is needed.

Some comments on the estimation of a dependence index in bivariate extreme value statistics

The estimation of a dependence index introduced in bivariate extreme value methodology by Ledford and Tawn (Biometrika 83(1) (1996) 169) is discussed. It is argued that estimators with bad bias properties are to be avoided. In this spirit we also suggest a new estimator.

Cost-Effectiveness of Single- Versus Generic Multiple-Tablet Regimens for Treatment of HIV-1 Infection in the United States

Background The possibility of incorporating generics into combination antiretroviral therapy and breaking apart once-daily single-tablet regimens (STRs), may result in less efficacious medications and/or more complex regimens with the expectation of marked monetary savings. A modeling approach that assesses the merits of such policies in terms of lifelong costs and health outcomes using adherence and effectiveness data from real-world U.S. settings. Methods A comprehensive computer-based microsimulation model was developed to assess the lifetime health (life expectancy and quality adjusted life-years—QALYs) and economic outcomes in HIV-1 infected patients initiating STRs compared with multiple-table regimens including generic medications where possible (gMTRs). The STRs considered included tenofovir disoproxil fumarate/emtricitabine and efavirenz or rilpivirine or elvitegravir/cobicistat. gMTRs substitutions included each counterpart to STRs, including generic lamivudine for emtricitabine and generic versus branded efavirenz. Results Life expectancy is estimated to be 1.301 years higher (discounted 0.619 QALY gain) in HIV-1 patients initiating a single-tablet regimen in comparison to a generic-based multiple-table regimen. STRs were associated with an average increment of

EPICE-HIV: An Epidemiologic Cost-Effectiveness Model for HIV Treatment

26,547.43 per patient in medication and

Therapeutic and Economic Value of Everolimus Plus Exemestane for the Treatment of Postmenopausal Women with Hormone Receptor-Positive, Her2/Neu Negative Advanced Breast Cancer.

1,824.09 in other medical costs due to longer survival which were partially offset by higher inpatients costs (

Cost-Effectiveness of Lenalidomide-Plus-Dexamethasone in Multiple Myeloma Patients Who Have Received at Least One Prior Therapy: A South Korean Perspective.

12,035.61) with gMTRs treatment. Overall, STRs presented incremental lifetime costs of

Upfront Overall Survival Modelling in Comparison to Real World Data: Lenalidomide for the Treatment of Multiple Myeloma Patients in South Korea.

16,335.91 compared with gMTRs, resulting in an incremental cost-effectiveness ratio of

On univariate extreme value statistics and the estimation of reinsurance premiums

26,383.82 per QALY gained. Conclusions STRs continue to represent good value for money under contemporary cost-effectiveness thresholds despite substantial price reductions of generic medications in the U. S.

Semi-Parametric Probability-Weighted Moments Estimation Revisited

The goal of this research was to establish a new and innovative framework for cost-effectiveness modeling of HIV-1 treatment, simultaneously considering both clinical and epidemiological outcomes. EPICE-HIV is a multi-paradigm model based on a within-host micro-simulation model for the disease progression of HIV-1 infected individuals and an agent-based sexual contact network (SCN) model for the transmission of HIV-1 infection. It includes HIV-1 viral dynamics, CD4+ T cell infection rates, and pharmacokinetics/pharmacodynamics modeling. Disease progression of HIV-1 infected individuals is driven by the interdependent changes in CD4+ T cell count, changes in plasma HIV-1 RNA, accumulation of resistance mutations and adherence to treatment. The two parts of the model are joined through a per-sexual-act and viral load dependent probability of disease transmission in HIV-discordant couples. Internal validity of the disease progression part of the model is assessed and external validity is demonstrated in comparison to the outcomes observed in the STaR randomized controlled clinical trial. We found that overall adherence to treatment and the resulting pattern of treatment interruptions are key drivers of HIV-1 treatment outcomes. Our model, though largely independent of efficacy data from RCT, was accurate in producing 96-week outcomes, qualitatively and quantitatively comparable to the ones observed in the STaR trial. We demonstrate that multi-paradigm micro-simulation modeling is a promising tool to generate evidence about optimal policy strategies in HIV-1 treatment, including treatment efficacy, HIV-1 transmission, and cost-effectiveness analysis.