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Computer codes are used in scientific research to study and predict the behaviour of complex systems. Their run times often make uncertainty and sensitivity analyses impractical because of the thousands of runs that are conventionally required, so efficient techniques have been developed based on a statistical representation of the code. The approach is less straightforward for dynamic codes, which represent time-evolving systems. We develop a novel iterative system to build a statistical model of dynamic computer codes, which is demonstrated on a rainfall-runoff simulator.

Existing estimates of the incidence of infectious intestinal disease (IID) caused by norovirus are based on electron microscopy or reverse transcription-polymerase chain reaction (RT-PCR). Neither method accurately represents norovirus disease burden: Electron microscopy has poor diagnostic sensitivity, and RT-PCR has poor diagnostic specificity. In this study, viral load measurements were used to identify cases of norovirus-associated IID and to produce new incidence estimates for England. IID cases were ascertained in the Study of Infectious Intestinal Disease in England (1993-1996), and stool specimens were tested by semiquantitative real-time RT-PCR for norovirus. The age-adjusted community incidence of norovirus-associated IID was 4.5/100 person-years (95% credibility interval: 3.8, 5.2), equating to 2 million episodes/year. Among children aged less than 5 years, the community incidence was 21.4/100 person-years (95% credibility interval: 15.9, 27.7), and the incidence of consultations to general practitioners for norovirus-associated IID was 3.2/100 person-years (95% credibility interval: 2.6, 3.8), with 100,000 children visiting their general practitioner for norovirus-associated IID each year. Norovirus is the most common cause of IID in the community in England and is responsible for a similar number of pediatric primary care consultations as rotavirus.

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The MTC approach to synthesis allows the evidence base on clinical effectiveness to be treated as a coherent whole, include more data, and sometimes relax the assumptions made in the pair-wise approaches. However, MTC models are necessarily more complex than those developed for pair-wise meta-analysis and thus could be seen as less transparent. Therefore, it is important that model details and the assumptions made are carefully reported alongside the results.

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 payoff to proposed research is achieved when its sample sizes and allocation between available treatment options are chosen to maximize 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: 1) a single trial of all the parameters, 2) a clinical trial providing evidence only on clinical endpoints, 3) an epidemiological study of natural history of disease, and 4) 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 optimization algorithms to search the ENBS surface more efficiently over such large dimensions.