In the UK we collect very large amounts of data on cancer patients, held by the National Cancer Registration and Analysis Service (NCRAS). Given that we collect all this data, it makes sense to think about what we can do with it: can we use it to robustly compare the real world effectiveness of cancer treatments? The issue with analysing observational data is that there are lots of potential biases – for example, confounding by indication, where patients who receive one treatment may differ prognostically from patients who receive a different treatment. This makes comparisons of treatment effectiveness difficult. However, using methods from the causal inference literature in a “Target Trial” framework may overcome these problems, if the data used to conduct the analyses are comprehensive enough. In Sheffield a programme of work is being undertaken to investigate whether cancer registry data collected in the UK are sufficient for comparing the effectiveness of cancer treatments used in clinical practice, mirroring work also being done in the US and Canada. I will describe the research and comment on progress made so far.
Bio:
Professor Nicholas Latimer
Nick is a Professor of Health Economics. He joined ScHARR in 2008 having previously worked in consultancy and the pharmaceutical industry. He has a degree in Economics from the University of Nottingham (2003), an MSc in Health Economics from the University of York (2004) and a PhD in Health Economics from the University of Sheffield (2012). Since joining ScHARR Nick has worked on a variety of projects, including economic evaluations alongside clinical trials, NICE Technology Appraisals, and consultancy projects. His research has focused on the incorporation of survival analysis in economic evaluations and he has completed NIHR Doctoral and Post-Doctoral Research Fellowships on this topic. In 2019 Nick began a Senior Research Fellowship funded by Yorkshire Cancer Research in which he is investigating the application of causal inference techniques to estimate comparative effectiveness from cancer registry datasets. Nick has authored NICE Decision Support Unit technical support documents on survival analysis (TSD14, 2011), treatment switching (TSD16, 2014), and partitioned survival analysis (TSD19, 2017), and is a member of NICE Technology Appraisal Committee B.
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