Data management
In clinical trials, tumour scans must be reported on using the RECIST framework, which isn’t ordinarily used in hospitals.
advanced, precision medicines, which might target a specific molecule or cancer-causing gene. This means trial designs are now more complex and that it’s tougher to find and recruit patients who are suitable for a treatment. And, when patients are enrolled, they can often be too ill to attend study visits or complete questionnaires.
All of these factors feed into a central challenge to trial success: having the data you need to determine if the treatment is any good. This is a question of not only collecting the right information, but also ensuring it’s correct and complete and putting it into an appropriate format for analysis. And with failed oncology trials estimated to cost $50-60bn each year, getting to the bottom of data management issues can be the difference between success and sizable sunk cost.
Collecting data
One of the first hurdles of good data management is ensuring you’ve collected all the information required. “It’s always difficult to get 100% of the patients to fill in the questionnaires or respond,” says Peter Hall, senior clinical lecturer in cancer informatics at the University of Edinburgh. “You can’t go back and collect that data retrospectively.” In cancer trials, patients can be too ill to attend study appointments or even to fill out forms from home. This can create bias in the dataset, Hall explains: if a treatment made more people sick then they would be less likely to complete questionnaires, and the data collected would mostly represent those who were comparatively well. While statistical methods can be used to try and adjust for the missing data, this doesn’t completely remove the bias, he adds. To help fill in the gaps, we might try collecting routine health information in parallel to the trial. Hall gives an example:
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“There might be surrogates for poor quality of life, such as patients spending too long in hospital or taking painkillers.” It’s currently possible to collect routine health data from the NHS for use in a trial, but it’s cumbersome. “You have to go through a whole application process to get that data, and it takes years in some cases,” says head of Data Management and Information Systems at the University of Southampton’s Clinical Trials Unit, Charlotte Stuart.
Despite calls from many in the field to make better use of real-world patient data in clinical trials, Health Data Research UK reports that just 5% of all trials in Britain used data from routine care systems between 2013-2018. In future, Hall hopes to see this become common practice.
Standardising data In order to analyse the data you’ve collected, you need to clean it – remove and fix any errors –and put it into a standardised format. In oncology, this can be quite the task: data is often received in large volumes and from multiple sources.
Real-world data sets from the NHS tend to be in different formats due to the various ways that centres across the country record their results, while readings from CT or MRI tumour scans are down to a radiologist’s interpretation. “You get some lab results coming from genomics labs that just come via an Excel spreadsheet,” says Stuart. Data management systems such as SAS can ease the burden of sorting through results, but often you still need to dig into them yourself, says Stuart. “It’s a very manual process a lot of the time.” Here, it helps to plan what you want your data to look like before you start collecting it, she explains. For example, you could ask the labs you’re working
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