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CSM statistician Peter Dutton has developed a software package that optimises the design of single-arm clinical trials in rare diseases.

Head and shoulders photograph of Peter Dutton alongside four example graph outputs from the developed software.
The newly developed software helps to choose the number of participants that a trial needs to have a good chance of finding out whether the drug under investigation works (green) or is too toxic to use (red) without needing more participants (yellow).

A new software package developed by Mr Peter Dutton of the Centre for Statistics in Medicine, NDORMS, will allow clinical trial designers to compare vastly different trial designs to choose the best one for their project. Optimising the design of trials in rare diseases will improve their chances of success.

Every year, over half a million people in the UK join clinical research studies. However, clinical trials often fail in recruiting enough patients to conclusively show an effect. These failed trials waste the efforts of those already recruited. With this enormous investment of patient time and health, every trial design must be optimised to protect patients and maximise the gift of their involvement.

Mr Dutton is the trial statistician for two clinical trials on sarcoma of the bone. These bone cancers are rare and highly malignant, and patients have few treatment options. When designing the single-armed trials, Mr Dutton found that the usual challenges of any trial were amplified: the drugs involved are particularly toxic and the patient pool for recruitment is particularly small. The trials had to be optimised for maximum efficiency and the standard trial designs just weren’t good enough.

As he spread the net for possible designs as far as possible, Mr Dutton found himself comparing trial designs that are not usually considered together. They were so different, there were no standard measures to compare them with one another. Frequentist designs are usually compared by frequentist properties, and Bayesian designs by theirs. Mr Dutton made the leap to calculate the frequentist properties for the Bayesian designs and vice versa, allowing direct comparisons.

With both trial designs completed and recruitment now underway, Mr Dutton turned his attention to the future. He has developed software to allow the designers of future trials in rare diseases to compare the same vast suite of designs he considered with just the click of a button.

The new software package, EurosarcBayes, has been published by CRAN and is available for free download and use in R. It includes frequentist and Bayesian single-arm trial designs that use one or two binary endpoints, and single or repeated analyses. A graphical user interface allows sample sizes and other parameters of different designs to be compared easily. Any trial design can also be easily compared with any other using both frequentist and Bayesian properties.

Both of the trials Mr Dutton works on are run by the EUROpean clinical trials in rare SARComas within an integrated translational trial network (euroSARC) programme, which aims to design, structure and implement nine innovative investigator-driven clinical trials of different scales on a multinational level that evaluate novel treatment strategies. Sarcomas are rare, highly malignant cancers of the bone, muscle, nerves, cartilage, tendons, blood vessels and fatty and fibrous tissues. The euroSARC programme and Mr Dutton’s methodological work are funded by the European Union Seventh Framework Programme.