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  • 1.
    Magnúsdóttir, Bergrún Tinna
    et al.
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Nyquist, Hans
    Stockholm University, Faculty of Social Sciences, Department of Statistics.
    Simultaneous estimation of parameters in the bivariate Emax model2015In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 34, no 28, p. 3714-3723Article in journal (Refereed)
    Abstract [en]

    In this paper, we explore inference in multi-response, nonlinear models. By multi-response, we mean models with m > 1 response variables and accordingly m relations. Each parameter/explanatory variable may appear in one or more of the relations. We study a system estimation approach for simultaneous computation and inference of the model and (co)variance parameters. For illustration, we fit a bivariate Emax model to diabetes dose-response data. Further, the bivariate Emax model is used in a simulation study that compares the system estimation approach to equation-by-equation estimation. We conclude that overall, the system estimation approach performs better for the bivariate Emax model when there are dependencies among relations. The stronger the dependencies, the more we gain in precision by using system estimation rather than equation-by-equation estimation.

  • 2.
    Norén, G. Niklas
    et al.
    Stockholm University, Faculty of Science, Department of Mathematics.
    Sundberg, Rolf
    Stockholm University, Faculty of Science, Department of Mathematics.
    Bate, Andrew
    Edwards, Ralph
    A statistical methodology for drug–drug interaction surveillance2008In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 27, no 16, p. 3057-3070Article in journal (Refereed)
    Abstract [en]

    Interaction between drug substances may yield excessive risk of adverse drug reactions (ADRs) when two drugs are taken in combination. Collections of individual case safety reports (ICSRs) related to suspected ADR incidents in clinical practice have proven to be very useful in post-marketing surveillance for pairwise drug–ADR associations, but have yet to reach their full potential for drug–drug interaction surveillance. In this paper, we implement and evaluate a shrinkage observed-to-expected ratio for exploratory analysis of suspected drug–drug interaction in ICSR data, based on comparison with an additive risk model. We argue that the limited success of previously proposed methods for drug–drug interaction detection based on ICSR data may be due to an underlying assumption that the absence of interaction is equivalent to having multiplicative risk factors. We provide empirical examples of established drug–drug interaction highlighted with our proposed approach that go undetected with logistic regression. A database wide screen for suspected drug–drug interaction in the entire WHO database is carried out to demonstrate the feasibility of the proposed approach. As always in the analysis of ICSRs, the clinical validity of hypotheses raised with the proposed method must be further reviewed and evaluated by subject matter experts.

  • 3. Weidemann, Felix
    et al.
    Dehnert, Manuel
    Koch, Judith
    Wichmann, Ole
    Höhle, Michael
    Stockholm University, Faculty of Science, Department of Mathematics. Robert Koch Institute, Germany.
    Bayesian parameter inference for dynamic infectious disease modelling: Rotavirus in Germany2014In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 33, no 9, p. 1580-1599Article in journal (Refereed)
    Abstract [en]

    Understanding infectious disease dynamics using epidemic models based on ordinary differential equations requires the calibration of model parameters from data. A commonly used approach in practice to simplify this task is to fix many parameters on the basis of expert or literature information. However, this not only leaves the corresponding uncertainty unexamined but often also leads to biased inference for the remaining parameters because of dependence structures inherent in any given model. In the present work, we develop a Bayesian inference framework that lessens the reliance on such external parameter quantifications by pursuing a more data-driven calibration approach. This includes a novel focus on residual autocorrelation combined with model averaging techniques in order to reduce these estimates’ dependence on the underlying model structure. We applied our methods to the modelling of age-stratified weekly rotavirus incidence data in Germany from 2001 to 2008 using a complex susceptible–infectious–susceptible-type model complemented by the stochastic reporting of new cases. As a result, we found the detection rate in the eastern federal states to be more than four times higher compared with that of the western federal states (19.0% vs 4.3%), and also the infectiousness of symptomatically infected individuals was estimated to be more than 10 times higher than that of asymptomatically infected individuals (95% credibility interval: 8.1–19.6). Not only do these findings give valuable epidemiological insight into the transmission processes, we were also able to  examine the considerable impact on the model-predicted transmission dynamics when fixing parameters beforehand.

  • 4.
    Wienke, Andreas
    et al.
    Stockholm University, Faculty of Science, Department of Mathematics.
    Ripatti, Samuli
    Palmgren, Juni
    Stockholm University, Faculty of Science, Department of Mathematics.
    Yashin, Anatoli
    A bivariate survival model with compound Poisson frailty2010In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 29, no 2, p. 275-283Article in journal (Refereed)
    Abstract [en]

    A correlated frailty model is suggested for analysis of bivariate time-to-event data. The model is an extension of the correlated power variance function (PVF) frailty model (correlated three-parameter frailty model) (J. Epidemiol. Biostat. 1999; 4:53-60). It is based on a bivariate extension of the compound Poisson frailty model in univariate survival analysis (Ann. Appl. Probab. 1992; 4:951-972). It allows for a non-susceptible fraction (of zero frailty) in the population, overcoming the common assumption in survival analysis that all individuals are susceptible to the event under study. The model contains the correlated gamma frailty model and the correlated inverse Gaussian frailty model as special cases. A maximum likelihood estimation procedure for the parameters is presented and its properties are studied in a small simulation study. This model is applied to breast cancer incidence data of Swedish twins. The proportion of women susceptible to breast cancer is estimated to be 15 per cent.

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