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    ABSTRACT: Analysis of the occurrence of adverse events, and in particular of solicited symptoms, following vaccination is often needed for the safety and benefit-risk evaluation of any candidate vaccine, and typically involves taking repeated measurements. In this article, it is shown that Linear Categorical Marginal Models are well-suited to take the dependencies in the data arising from the repeated measurements into account and provide detailed and useful information for comparing safety profiles of different products while remaining relatively easy to interpret. Linear Categorical Marginal Models are presented and applied to a Phase III clinical trial of a candidate meningoccocal pediatric vaccine.
    Statistics in Biopharmaceutical Research 02/2013; 5(1).
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    ABSTRACT: Case-control studies are particularly prone to selection bias, which can affect odds ratio estimation. Approaches to discovering and adjusting for selection bias have been proposed in the literature using graphical and heuristic tools as well as more complex statistical methods. The approach we propose is based on a survey-weighting method termed Bayesian post-stratification and follows from the conditional independences that characterise selection bias. We use our approach to perform a selection bias sensitivity analysis by using ancillary data sources that describe the target case-control population to re-weight the odds ratio estimates obtained from the study. The method is applied to two case-control studies, the first investigating the association between exposure to electromagnetic fields and acute lymphoblastic leukaemia in children and the second investigating the association between maternal occupational exposure to hairspray and a congenital anomaly in male babies called hypospadias. In both case-control studies, our method showed that the odds ratios were only moderately sensitive to selection bias. Copyright © 2013 John Wiley & Sons, Ltd.
    Statistics in Medicine 01/2013;
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    ABSTRACT: Epidemics are often modeled using non-linear dynamical systems observed through partial and noisy data. In this paper, we consider stochastic extensions in order to capture unknown influences (changing behaviors, public interventions, seasonal effects, etc.). These models assign diffusion processes to the time-varying parameters, and our inferential procedure is based on a suitably adjusted adaptive particle Markov chain Monte Carlo algorithm. The performance of the proposed computational methods is validated on simulated data and the adopted model is applied to the 2009 H1N1 pandemic in England. In addition to estimating the effective contact rate trajectories, the methodology is applied in real time to provide evidence in related public health decisions. Diffusion-driven susceptible exposed infected retired-type models with age structure are also introduced.
    Biostatistics 01/2013;
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    ABSTRACT: We model a defaultable asset as solution to a stochastic differential equation driven by both a Brownian motion and the counting process martingale associated to the one-jump process. We discuss in this framework the minimal entropy martingale measure as well as the linear Esscher and the minimal martingale measure. In particular we deal with some rather delicate verification issues.
    Stochastic Processes and their Applications 08/2012; 122(8):2870–2884.
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    ABSTRACT: The paper assesses biased-coin designs for sequential treatment allocation in clinical trials. Comparisons emphasise the importance of considering randomness, as well as treatment balance, which are calculated as bias and loss. In the numerical examples, the responses are assumed normally distributed, perhaps after transformation, and balance is required over a set of covariates. The effect of covariate distribution on the properties of five allocation rules is investigated, with an emphasis on methods of comparison, which also apply to other forms of response. The concept of admissibility shows that the widely used minimisation rule is outperformed by Atkinson's rule derived from the theory of optimum experimental design. We present a simplified form of this rule. For this rule, the ability to guess the next treatment allocation decreases with study size. For the other rules, it is constant. Copyright © 2012 John Wiley & Sons, Ltd.
    Statistics in Medicine 06/2012;
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    ABSTRACT: In this paper, we consider a risk process with the arrival of claims modelled by a dynamic contagion process, a generalisation of the Cox process and Hawkes process introduced by Dassios and Zhao (2011). We derive results for the infinite horizon model that are generalisations of the Cramér–Lundberg approximation, Lundberg’s fundamental equation, some asymptotics as well as bounds for the probability of ruin. Special attention is given to the case of exponential jumps and a numerical example is provided.
    Insurance Mathematics and Economics 08/2011;
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    Latent Variable Models and Factor Analysis: A Unified Approach, 3rd Edition, 06/2011: pages 1 - 18; , ISBN: 9781119970583
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    ABSTRACT: ABSTRACT: Observational studies of human health and disease (basic, clinical and epidemiological) are vulnerable to methodological problems -such as selection bias and confounding- that make causal inferences problematic. Gene-disease associations are no exception, as they are commonly investigated using observational designs. A rich body of knowledge exists in medicine and epidemiology on the assessment of causal relationships involving personal and environmental causes of disease; it includes seminal causal criteria developed by Austin Bradford Hill and more recently applied directed acyclic graphs (DAGs). However, such knowledge has seldom been applied to assess causal relationships in clinical genetics and genomics, even in studies aimed at making inferences relevant for human health. Conversely, incorporating genetic causal knowledge into clinical and epidemiological causal reasoning is still a largely unexplored area.As the contribution of genetics to the understanding of disease aetiology becomes more important, causal assessment of genetic and genomic evidence becomes fundamental. The method we develop in this paper provides a simple and rigorous first step towards this goal. The present paper is an example of integrative research, i.e., research that integrates knowledge, data, methods, techniques, and reasoning from multiple disciplines, approaches and levels of analysis to generate knowledge that no discipline alone may achieve.
    Emerging Themes in Epidemiology 06/2011; 8(1):5.
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    ABSTRACT: We consider the indifference valuation of an uncertain monetary payoff from the perspective of an uncertainty averse decision maker. We study how the indifference valuation depends on the decision maker's attitudes toward uncertainty. We obtain a characterization of comparative uncertainty aversion and various characterizations of increasing, decreasing, and constant uncertainty aversion.
    05/2011;
  • Encyclopedia of Quantitative Finance, 05/2010; , ISBN: 9780470061602
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