Spatio-temporal epidemic modelling using additive-multiplicative intensity models

An extension of the stochastic susceptible-infectious-recovered (SIR) model is proposed in order to accommodate a regression context for modelling infectious disease surveillance data. The proposal is based on a multivariate counting process specified by conditional intensities, which contain an additive epidemic component and a multiplicative endemic component. This allows the analysis of endemic infectious diseases by quantifying risk factors for infection by external sources in addition to infective contacts. Simulation from the model is straightforward by Ogata's modified thinning algorithm. Inference can be performed by considering the full likelihood of the stochastic process with additional parameter restrictions to ensure non-negative conditional intensities. As an illustration we analyse data provided by the Federal Research Centre for Virus Diseases of Animals, Wusterhausen, Germany, on the incidence of the classical swine fever virus in Germany during 1993-2004

Point and interval estimation in two-stage adaptive designs with time to event data and biomarker-driven subpopulation selection

In personalized medicine, it is often desired to determine if all patients or only a subset of them benefit from a treatment. We consider estimation in two‐stage adaptive designs that in stage 1 recruit patients from the full population. In stage 2, patient recruitment is restricted to the part of the population, which, based on stage 1 data, benefits from the experimental treatment. Existing estimators, which adjust for using stage 1 data for selecting the part of the population from which stage 2 patients are recruited, as well as for the confirmatory analysis after stage 2, do not consider time to event patient outcomes. In this work, for time to event data, we have derived a new asymptotically unbiased estimator for the log hazard ratio and a new interval estimator with good coverage probabilities and probabilities that the upper bounds are below the true values. The estimators are appropriate for several selection rules that are based on a single or multiple biomarkers, which can be categorical or continuous

Short-term and long-term effects of United Nations peace operations

Earlier studies have shown that United Nations peace operations make a positive contribution to peacebuilding efforts after civil wars. But do these effects carry over to the period after the peacekeepers leave? And how do the effects of UN peace operations interact with other determinants of peacebuilding in the long run? The author addresses these questions using a revised version of the Doyle and Sambanis dataset and applying different estimation methods to estimate the short-term and long-term effects of UN peace missions. He finds that UN missions have robust, positive effects on peacebuilding in the short term. UN missions can help parties implement peace agreements but the UN cannot fight wars, and UN operations contribute more to the quality of the peace where peace is based on participation, than to the longevity of the peace, where peace is simply the absence of war. The effects of UN missions are also felt in the long run, but they dissipate over time. What is missing in UN peacebuilding is a strategy to foster the self-sustaining economic growth that could connect increased participation with sustainable peace.Post Conflict Reintegration,Peace&Peacekeeping,International Affairs,Post Conflict Reconstruction,Politics and Government

Determination of the most appropriate method for extrapolating overall survival data from a placebo-controlled clinical trial of lenvatinib for progressive, radioiodine-refractory differentiated thyroid cancer

Background: Cost-effectiveness models for the treatment of long-term conditions often require information on survival beyond the period of available data. Objectives: This paper aims to identify a robust and reliable method for the extrapolation of overall survival (OS) in patients with radioiodine-refractory differentiated thyroid cancer receiving lenvatinib or placebo. Methods: Data from 392 patients (lenvatinib: 261, placebo: 131) from the SELECT trial are used over a 34-month period of follow-up. A previously published criterion-based approach is employed to ascertain credible estimates of OS beyond the trial data. Parametric models with and without a treatment covariate and piecewise models are used to extrapolate OS, and a holistic approach, where a series of statistical and visual tests are considered collectively, is taken in determining the most appropriate extrapolation model. Results: A piecewise model, in which the Kaplan–Meier survivor function is used over the trial period and an extrapolated tail is based on the Exponential distribution, is identified as the optimal model. Conclusion: In the absence of long-term survival estimates from clinical trials, survival estimates often need to be extrapolated from the available data. The use of a systematic method based on a priori determined selection criteria provides a transparent approach and reduces the risk of bias. The extrapolated OS estimates will be used to investigate the potential long-term benefits of lenvatinib in the treatment of radioiodine-refractory differentiated thyroid cancer patients and populate future cost-effectiveness analyses

Responder Identification in Clinical Trials with Censored Data

We present a newly developed technique for identification of positive and negative responders to a new treatment which was compared to a classical treatment (or placebo) in a randomized clinical trial. This bump-hunting-based method was developed for trials in which the two treatment arms do not differ in survival overall. It checks in a systematic manner if certain subgroups, described by predictive factors do show difference in survival due to the new treatment. Several versions of the method were discussed and compared in a simulation study. The best version of the responder identification method employs martingale residuals to a prognostic model as response in a stabilized through bootstrapping bump hunting procedure. On average it recognizes 90% of the time the correct positive responder group and 99% of the time the correct negative responder group

Portfolio saliency and ministerial turnover: Dynamics in Scandinavian postwar cabinets

© 2013 The Author(s) Scandinavian Political Studies © 2013 Nordic Political Science Association. This is the accepted version of the following article: Hansen, M. E., Klemmensen, R., Hobolt, S. B. and Bäck, H. (2013), Portfolio Saliency and Ministerial Turnover: Dynamics in Scandinavian Postwar Cabinets. Scandinavian Political Studies, 36: 227–248, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/1467-9477.12004/abstract.Why do certain ministers remain in their post for years while others have their time in office cut short? Drawing on the broader literature on portfolio allocation, this article argues that the saliency of individual portfolios shapes ministerial turnover. The main argument is that ministerial dismissals are less likely to occur the higher the saliency attributed to the ministerial portfolio since ministers appointed to important posts are more likely to have been through extensive screening before appointment. Importantly, it is also posited in the article that the effect of portfolio salience is conditioned by government approval ratings: when government ratings are on the decline, prime ministers are less likely to reshuffle or fire important ministers than when approval ratings are improving. To test these claims, Cox proportional hazards models are applied to a new dataset on ministerial turnover in Scandinavia during the postwar period. The results strongly support the proposition that portfolio saliency matters for ministerial survival, and that this effect is moderated by government popularity