Upcoming seminars

Monday, May 30, 15:15

Robin Evans
Associate Professor, Department of Statistics at the University of Oxford
Parameterizing and Simulating from Causal Models

Many statistical problems in causal inference involve a probability distribution other than the one from which data are actually observed; as an additional complication, the object of interest is often a marginal quantity of this other probability distribution. This creates many practical complications for statistical inference, even where the problem is non-parametrically identified. In particular, it is difficult to perform likelihood-based inference, or even to simulate from the model in a general way.

We introduce the frugal parameterization, which places the causal effect of interest at its centre, and then build the rest of the model around it. We do this in a way that provides a recipe for constructing a regular, non-redundant parameterization using causal quantities of interest. In the case of discrete variables we can use odds ratios to complete the parameterization, while in the continuous case copulas are the natural choice; other possibilities are also discussed.

We introduce the `frugal parameterization’, which places the causal effect of interest at its centre, and then build the rest of the model around it. We do this in a way that provides a recipe for constructing a regular, non-redundant parameterization using causal quantities of interest. In the case of discrete variables we can use odds ratios to complete the parameterization, while in the continuous case copulas are the natural choice; other possibilities are also discussed.

This is joint work with Vanessa Didelez (University of Bremen and Leibniz Institute for Prevention Research and Epidemiology).

Wednesday, June 08, 15:15

Carolin Herrmann
Institute of Biometry and Clinical Epidemiology, Charité – University Medicine Berlin
Sample size adaptations during ongoing clinical trials – possibilities and challenges

One central design aspect of clinical trials is a valid sample size calculation. The sample size needs to be large enough to detect an existing effect with sufficient power and at the same time it needs to be ethically feasible. Sample size calculations are based on the applied test statistic as well as the significance level and desired power. However, it is not always straightforward to determine the underlying parameter values, such as the expected treatment effect size and variance, during the planning stage of a clinical trial.

Adaptive designs provide the possibility of adapting the sample size during an ongoing trial. At so called interim analyses, nuisance parameters can be re-estimated. Alternatively, unblinded interim analyses may be performed where the treatment effect can be re-estimated and the trial may also be stopped early for efficacy or futility. In this talk, we will focus on unblinded interim analyses and its different possibilities for recalculating the sample size. We discuss their performance evaluation as well as possibilities for improving existing and optimizing sample size recalculation approaches.

Monday, September 26, 15:15

Andrew Vickers
TBA

TBA

Map of CSS

You can find CSS next to the Botanical Garden, 5 minutes from Nørreport station.


Meeting room 5.2.46 is the library of the Biostatistics section, located in building 5, 2nd floor, room 46. See the map below for directions inside CSS.