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24 September 2018,
15:15-16:00
Gene expression measurement technology has shifted from microarrays to sequencing, producing ever richer high-througput data for transcriptomics studies. As studies using these data grow in size, frequency, and importance, it is becoming urgent to develop and refine the statistical tools available for their analysis. In particular, there is a need for methods that better control the type-I error as clinical RNA-seq studies are including a growing number of subjects (measurements being cheaper) resulting in larger sample sizes. We model RNA-seq counts as continuous variables using nonparametric regression to account for their inherent heteroscedasticity, in a principled, model-free, and efficient manner for detecting differentially expressed genes from RNA-seq data. Our method can identify the genes whose expression is significantly associated with one or several factors of interest in complex experimental designs, including studies with longitudinal measurement of gene expression. We rely on a powerful variance component score test that can account for both adjustement covariates and data heteroscedasticity without assuming any specific parametric distribution for the (transformed) RNA-seq counts. Despite the presence of a nonparametric component, our test statistic has a simple form and limiting distribution, which can be computed quickly. A permutation version of the test is also derived for small sample sizes, but this leads to issues in controlling the False Discovery Rate. Finally we also propose an extension of the method for Gene Set Analysis. Applied to both simulated data and real benchmark datasets, we show that our test has good statistical properties when compared to state-of-the-art methods limma/voom, edgeR, and DESeq2. In particular, we show that those three methods can all fail to control the type I error and the False Discovery Rate under realistic settings, while our method behaves as expected. We apply our proposed method to two candidate vaccine phase-I studies with repeated gene expression measurements: one public dataset investigating a candidate vaccine against EBOLA, and one original dataset investigating a candidate vaccine against HIV. For an overview of planned future seminars at the section of Biostatistics, UCPH, see http://biostatistics.dk/seminars/ The seminar will be held at CSS (“det gamle Kommunehospital”), Øster Farimagsgade 5, 1353 Copenhagen K, room 5.2.46. Tea will be served in the library of the section of Biostatistics half an hour before the seminar starts.
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27 September 2018,
15:00-16:00
Breastfeeding Duration and Atopic Diseases in Early Childhood: The Role of T Lymphocytes
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28 September 2018,
14:00-17:00
Risk stratification of a population with type 2 diabetes and shared care management of patients with intermediate risk across the primary and secondary health care sectors
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3 October 2018,
13:00-16:00
The Section of Biostatistics are happy to invite colleagues, research collaborators and friends to celebrate the section's 40 years anniversary. Please sign up for the event via Susanne, e-mail: skl@sund.ku.dk no later than Friday 14 September 2018.
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3 October 2018,
16:00-17:30
Assistant Professor Linnet Taylor Tilburg University The increasing digitisation of social and public life around the world, and the emergence of new technologies for collecting and analysing data raise important questions for the study of public health. New sources of digital data about populations worldwide are offering new insights for epidemiology and public health, but access to those insights is currently limited by both researchers’ understanding, and by the proprietary nature of much of the most useful data. This presentation will look at the ways in which digitisation is creating new opportunities for public health. It will also focus critically on the questions these opportunities raise: what does it mean to live in a digital world where all the data we emit tells a story about our health? Can data be seen as a public good, and if so, what kind of public benefits from it? How should the public-private partnerships forming to use the new data sources be governed, and who should decide? The presentation will argue for using the study of technology and datafication to inform a global political economy perspective on public health. Linnet Taylor is Assistant Professor of Data Ethics, Law and Policy at the Tilburg Institute for Law, Technology, and Society (TILT). Her research focuses on global data justice – the development of a conceptual framework for the ethical and beneficial governance of data technologies based on insights from technology users and providers around the world. Everybody is welcome!
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12 October 2018,
10:30-12:00
Roland Bal, Professor of Healthcare governance Erasmus University Rotterdam Healthcare is one of the domains heavily invested with the measurement of quality. Whilst such measurements have a long tradition in healthcare, they have boomed in recent decades with the advance of neoliberal policies and the development of new information technologies. At the same time, however, care is as much a domain in which investments in non-measurability have been quite dominant, as quality of care is seen by many to ‘escape’ enumeration. In my talk I will reflect on research over the last decade in which I have studied qualculation of healthcare in professional and regulatory settings, showing how practitioners and regulators care for and play with the performativity of numbers and analyzing their investments in (non)qualculability of care. I will then take this to analyze the emerging practice of value-based healthcare in the Netherlands. Everybody is welcome!
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23 October 2018,
14:00-17:00
Injury risk and prevention among male workers in Denmark. The public health approach to framing two nationwide follow-up studies of hospital-treated injuries and a workplace safety intervention study
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15 November 2018,
14:00-15:30
Alexandra Middleton, Princeton University More inormation TBA.
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