Carole Siani

Analysing the performance of bootstrap neural tests for conditional heteroskedasticity in ARCH-M models

The robustness against strongly non-linear forms for the conditional variance of tests for detecting conditional heteroskedasticity using both artificial neural network techniques and bootstrap methods combined, is analysed in the context of ARCH-M models. The size and the power properties in small samples of these tests are examined by using out Monte-Carlo experiments with various standard and non-standard models of conditional heteroskedasticity. The P value functions are explored in order to select particularly problematic cases. Graphical presentations, based on the principle of size correction, are used for presenting the true power of the tests, rather than a spurious nominal power as it is usually made in the literature. In addition, graphics linking the process dynamics with the heteroskedasticity forms are shown for analysing in which circumstances the neural networks are effective.

Impact of uncertainty on cost-effectiveness analysis of medical strategies: The case of high-dose chemotherapy for breast cancer patients

OBJECTIVESnThe object of this study was to determine, taking into account uncertainty on cost and outcome parameters, the cost-effectiveness of high-dose chemotherapy (HDC) compared with conventional chemotherapy for advanced breast cancer patients.nnnMETHODSnAn analysis was conducted for 300 patients included in a randomized clinical trial designed to evaluate the benefits, in terms of disease-free survival and overall survival, of adding a single course of HDC to a four-cycle conventional-dose chemotherapy for breast cancer patients with axillary lymph node invasion. Costs were estimated from a detailed observation of physical quantities consumed, and the Kaplan-Meier method was used to evaluate mean survival times. Incremental cost-effectiveness ratios were evaluated successively considering disease-free survival and overall survival outcomes. Handling of uncertainty consisted in construction of confidence intervals for these ratios, using the truncated Fieller method.nnnRESULTSnThe cost per disease-free life year gained was evaluated at 13,074 Euros, a value that seems to be acceptable to society. However, handling uncertainty shows that the upper bound of the confidence interval is around 38,000 Euros, which is nearly three times higher. Moreover, as no difference was demonstrated in overall survival between treatments, cost-effectiveness analysis, that is a cost minimization, indicated that the intensive treatment is a dominated strategy involving an extra cost of 7,400 Euros, for no added benefit.nnnCONCLUSIONSnAdding a single course of HDC led to a clinical benefit in terms of disease-free survival for an additional cost that seems to be acceptable, considering the point estimate of the ratio. However, handling uncertainty indicates a maximum ratio for which conclusions have to be discussed.

An artificial neural network based heterogeneous panel unit root test in case of cross sectional independence

In this paper we propose an artificial neural network (ANN) based panel unit root test, extending [1] neural test to a dynamic heterogeneous panel context, and following the [2] panel methodology. New asymptotic results are obtained both for the individual ANN-t test statistics for unit root, and the panel unit root test statistic. An application to a panel of bilateral real exchange rate series with the US Dollar from the 20 major OECD countries is provided.

Predictive models to estimate utility from clinical questionnaires in schizophrenia: findings from EuroSC

ObjectiveThe clinical symptoms of schizophrenia are associated with serious social, quality of life and functioning alterations. Typically, data on health utilities are not available in clinical studies in schizophrenia. This makes the economic evaluation of schizophrenia treatments challenging. The purpose of this article was to provide a mapping function to predict unobserved utility values in patients with schizophrenia from the available clinical and socio-demographic information.MethodsThe analysis was performed using data from EuroSC, a 2-year, multi-centre, cohort study conducted in France (Nxa0=xa0288), Germany (Nxa0=xa0618), and the UK (Nxa0=xa0302), totalling 1208 patients. Utility was calculated based on the EQ-5D questionnaire. The relationships between the utility values and the patients’ socio-demographic and clinical characteristics (Positive and Negative Syndrome Scale—PANSS, Calgary Depression Scale for Schizophrenia—CDSS, Global Assessment of Functioning—GAF, extra-pyramidal symptoms measured by Barnes Akathisia Scale—BAS, age, sex, country, antipsychotic type) were modelled using a random and a fixed individual effects panel linear model.ResultsThe analysis demonstrated the prediction ability of the used parameters for estimating utility measures in patients with schizophrenia. Although there are small variations between countries, the same variables appear to be the key predictors. From a clinical perspective, age, gender, psychopathology, and depression were the most important predictors associated with the EQ-5D.ConclusionThis paper proposed a reliable, robust and easy-to-apply mapping method to estimate EQ-5D utilities based on demographic and clinical measures in schizophrenia.

Neural Tests for Conditional Heteroskedasticity in ARCH-M Models

This paper deals with tests for detecting conditional heteroskedasticity in ARCH-M models using three kinds of methods: neural networks techniques, bootstrap methods and both combined.As regards the ARCH models, Péguin-Feissolle (2000) developed tests based on the modelling techniques with neural network. However, as regards the ARCH-M models, a nuisance parameter is not identified and the tests are not applicable. To solve this problem, we propose to adapt these neural tests to Davies procedure (1987) leading to new tests. The performance of these latter tests are compared with those of Bera and Ra test (1995).However, Bera and Ra test has not really satisfactory performance and suffer from serious size distortion. Our neural test will have the same problem. To solve this second problem, without loss of power, we apply parametric and nonparametric bootstrap methods on the underlying test statistics.Lastly, to examine the size and the power properties of the tests in small samples, Monte Carlo simulations are carried out with various standard and non-standard models for conditional heteroskedasticity as to illustrate a variety of situations. In addition, the graphical presentation of Davidson and MacKinnon (1998a) is used to show the true power of the tests and not only the (nominal) power, as it is often the case, that can be meaningless.

Algorithm for Making Decision with the Incremental Cost-Effectiveness Ratio handling the Mirror Decision-Making Problem

This paper deals with the handling of uncertainty in the context of cost-effectiveness analyses, through the building of confidence regions for the incremental cost-effectiveness ratio (ICER), to provide a statistical descriptive tool for helping medico-economic decision-making. However, the confidence region for the ICER, mathematically provided, cannot be directly usable for decision-making, because two identical ICERs can be associated with two totally opposite findings. To solve this problem, algorithms are developed in this paper, to provide on the one hand a directed confidence region usable for decision-making, and on the other hand a decision directly from the data.