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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.
    Full-text · Article · Jul 2013 · Statistics in Medicine
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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.
    Full-text · Article · Feb 2013 · Statistics in Biopharmaceutical Research
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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.
    Full-text · Article · Jan 2013 · Biostatistics
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