UCL Partners Biostatistics Network Seminars - Term 1, Autumn 2012
All seminars will be held in Room 102, Department of Statistical Science, 1-19 Torrington Place (1st floor)
Wednesday 26th September (4-5pm)
Marco Geraci (University College London)
Quantile regression - an overview of methods and applications
In this talk, I will give an overview of quantile regression (QR). Estimation, hypothesis testing, and model selection in QR analysis will be introduced along with an outline of special topics such as, for example, analysis of clustered data, binary and count data, censored data, spatial regression analysis, survival data analysis, and multiple imputation. Some applications as well as available software to perform QR will be presented.
Wednesday 7th November (4-5pm)
Rebecca Turner (MRC Biostatistics Unit, Cambridge)
Making use of external information on heterogeneity and biases in meta-analysis
Many meta-analyses contain only a small number of studies, making it difficult to estimate the extent of between-study heterogeneity. An additional problem is that the original studies are often affected by varying amounts of internal bias caused by methodological flaws. Standard methods for meta-analysis do not acknowledge biases in the studies, and do not allow for imprecision in the estimated between-study heterogeneity variance. In this talk, I will present and discuss methods for incorporating empirical evidence on the likely extent of heterogeneity, and methods for making adjustments for anticipated within-study biases.
Tuesday 11th December (4-5pm)
Stephen Senn (Center for Methodology and Statistics, Luxemburg)
"But to be kind, for the sake of completeness, and because industry and researchers are so keen to pass the blame on to academic journals, we can see if the claim is true….Here again the journals seem blameless: 74 manuscripts submitted to the Journal of the American Association (JAMA) were followed up, and there was no difference in acceptance for significant and non-significant findings." Bad Pharma, p34
A central argument in Ben Goldacre's recent book Bad Pharma is that although trials with negative results are less likely to be published than trials with positive results, the medical journals are blameless: they are just as likely to publish either. I show, however, that this is based on a misreading of the literature and would rely, for its truth, on an assumption that is not only implausible but known to be false, namely that authors are just as likely to submit negative as positive studies. I show that a completely different approach to analysing the data has be used: one which compares accepted papers in terms of quality. When this is done, what studies have been performed, do, in fact, show that there is a bias against negative studies. This explains the apparent inconsistency in results between observational and experimental studies of publication bias.
NB: This seminar will be held in Drayton B20 Jevons Lecture Theatre
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