Cumulative Incidence Function Estimation Based on Population-Based Biobank Data

Many countries have established population-based biobanks, which are being used increasingly in epidemiolgical and clinical research. These biobanks offer opportunities for large-scale studies addressing questions beyond the scope of traditional clinical trials or cohort studies. However, using biobank data poses new challenges. Typically, biobank data is collected from a study cohort recruited over a defined calendar period, with subjects entering the study at various ages falling between $c_L$ and $c_U$. This work focuses on biobank data with individuals reporting disease-onset age upon recruitment, termed prevalent data, along with individuals initially recruited as healthy, and their disease onset observed during the follow-up period. We propose a novel cumulative incidence function (CIF) estimator that efficiently incorporates prevalent cases, in contrast to existing methods, providing two advantages: (1) increased efficiency, and (2) CIF estimation for ages before the lower limit, $c_L$

Case-control survival analysis with a general semiparametric shared frailty model--a pseudo full likelihood approach

In this work we deal with correlated failure time (age at onset) data arising from population-based case-control studies, where case and control probands are selected by population-based sampling and an array of risk factor measures is collected for both cases and con- trols and their relatives. Parameters of interest are e®ects of risk factors on the failure time hazard function and within-family depen- dencies among failure times after adjusting for the risk factors. Due to the retrospective sampling scheme, large sample theory for existing methods has not been established. We develop a novel technique for estimating the parameters of interest under a general semiparamet- ric shared frailty model. We also present a simple, easily computed, and non-iterative nonparametric estimator for the cumulative base- line hazard function. We provide rigorous large sample theory for the proposed method. We also present simulation results and a real data example for illustrating the utility of the proposed method

Climate action and the vantage point of imagined futures: a scenario-based conversation

This paper is a structured dialogue between its four authors on the question “How might future scenarios nourish our thinking about climate action?” A scenario set for the future of European regional inequality in the year 2048, developed by the Horizon Europe funded IMAJINE programme, is used as the prism for this conversation. Each author has a distinct disciplinary and professional background, and initially approaches the question from their own angle. These individual explorations encompass: the nature of climate change and our understanding of it in each IMAJINE scenario; questions of risk and responsibility now and in times to come; the use of scenarios to identify current blind spots and stimulate creative thinking; and the possibility that scenarios might offer fresh perspectives which allow us to reevaluate our notions of the sustainable “good life” and identify vulnerabilities which are overlooked in the present day. The second part of the paper comprises reflections on these individual contributions, with the authors pairing off so that two authors comment on the inputs by the other two, and vice versa. This exemplifies the polyphonic and discursive nature of scenarios, understood as “the art of strategic conversation”. The concluding comments reflect on the wider ability of readers, writers, and researchers to use scenario processes and structured conversations like those in this paper to sustain open spaces of mutual uncertainty, exploration, and generation