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  • 1. Broberg, Per
    et al.
    Miller, Frank
    Stockholms universitet, Samhällsvetenskapliga fakulteten, Statistiska institutionen.
    Conditional estimation in two-stage adaptive designs2017Inngår i: Biometrics, ISSN 0006-341X, E-ISSN 1541-0420, Vol. 73, nr 3, s. 895-904Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    We consider conditional estimation in two-stage sample size adjustable designs and the consequent bias. More specifically, we consider a design which permits raising the sample size when interim results look rather promising, and which retains the originally planned sample size when results look very promising. The estimation procedures reported comprise the unconditional maximum likelihood, the conditionally unbiased Rao-Blackwell estimator, the conditional median unbiased estimator, and the conditional maximum likelihood with and without bias correction. We compare these estimators based on analytical results and a simulation study. We show how they can be applied in a real clinical trial setting.

  • 2.
    Höhle, Michael
    et al.
    Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen. Robert Koch Institute, Germany.
    an der Heiden, Matthias
    Bayesian Nowcasting during the STEC O104:H4 Outbreak in Germany, 20112014Inngår i: Biometrics, ISSN 0006-341X, E-ISSN 1541-0420, Vol. 70, nr 4, s. 993-1002Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    A Bayesian approach to the prediction of occurred-but-not-yet-reported events is developed for application in real-time public health surveillance. The motivation was the prediction of the daily number of hospitalizations for the hemolytic-uremic syndrome during the large May-July 2011 outbreak of Shiga toxin-producing Escherichia coli (STEC) O104:H4 in Germany. Our novel Bayesian approach addresses the count data nature of the problem using negative binomial sampling and shows that right-truncation of the reporting delay distribution under an assumption of time-homogeneity can be handled in a conjugate prior-posterior framework using the generalized Dirichlet distribution. Since, in retrospect, the true number of hospitalizations is available, proper scoring rules for count data are used to evaluate and compare the predictive quality of the procedures during the outbreak. The results show that it is important to take the count nature of the time series into account and that changes in the delay distribution occurred due to intervention measures. As a consequence, we extend the Bayesian analysis to a hierarchical model, which combines a discrete time survival regression model for the delay distribution with a penalized spline for the dynamics of the epidemic curve. Altogether, we conclude that in emerging and time-critical outbreaks, nowcasting approaches are a valuable tool to gain information about current trends.

  • 3. Sjolander, Arvid
    et al.
    Humphreys, Keith
    Vansteelandt, Stijn
    Bellocco, Rino
    Palmgren, Juni
    Stockholms universitet, Naturvetenskapliga fakulteten, Matematiska institutionen.
    Sensitivity Analysis for Principal Stratum Direct Effects, with an Application to a Study of Physical Activity and Coronary Heart Disease2009Inngår i: Biometrics, ISSN 0006-341X, E-ISSN 1541-0420, Vol. 65, nr 2, s. 514-520Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In many studies, the aim is to learn about the direct exposure effect, that is, the effect not mediated through an intermediate variable. For example, in circulation disease studies it may be of interest to assess whether a suitable level of physical activity can prevent disease, even if it fails to prevent obesity. It is well known that stratification on the intermediate may introduce a so-called posttreatment selection bias. To handle this problem, we use the framework of principal stratification (Frangakis and Rubin, 2002, Biometrics 58, 21-29) to define a causally relevant estimand-the principal stratum direct effect (PSDE). The PSDE is not identified in our setting. We propose a method of sensitivity analysis that yields a range of plausible values for the causal estimand. We compare our work to similar methods proposed in the literature for handling the related problem of ""truncation by death."".

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