School of Mathematical Sciences

Dimensions of Design Space: A Decision-Theoretic Approach to Optimal Research Design menu

Dimensions of Design Space: A Decision-Theoretic Approach to Optimal Research Design

Speaker: 
Stefano Conti
Health Protection Agency
Date/Time: 
Thu, 25/03/2010 - 16:30
Room: 
M203
Seminar series: 

Bayesian decision theory can be used not only to establish the optimal sample
size and its allocation in a single clinical study, but also to identify an optimal
portfolio of research combining different types of study design. Within a single
study, the highest societal pay-off to proposed research is achieved when its
sample sizes, and allocation between available treatment options, are chosen to
maximise the Expected Net Benefit of Sampling (ENBS). Where a number of
different types of study informing different parameters in the decision problem
could be conducted, the simultaneous estimation of ENBS across all dimensions
of the design space is required to identify the optimal sample sizes and allocations
within such a research portfolio. This is illustrated through a simple
example of a decision model of zanamivir for the treatment of influenza. The
possible study designs include:
i) a single trial of all the parameters;
ii) a clinical trial providing evidence only on clinical endpoints;
iii) an epidemiological study of natural history of disease and
iv) a survey of quality of life.
The possible combinations, samples sizes and allocation between trial arms are
evaluated over a range of cost-effectiveness thresholds. The computational challenges
are addressed by implementing optimisation algorithms to search the
ENBS surface more efficiently over such large dimensions.