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).

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.

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.