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This proposal generated some reaction – or at least an attempt to validate this analysis. And that has been done in a subsequent paper analysing the response to treatment, or the prediction to treatment, in a population that has been very well characterised, like the population belonging to a clinical trial, such as ADOPT and RECORD. And what we can see here is, on one side, the predictability of the response on the basis of the cluster in people that are confirmed to respond according to the cluster analysis. On the other side, rather than using the cluster, the classical factors, are used. These are the same ones commonly used to evaluate the success of treatment – in other words, body weight, HbA1c, and so on. And now what this analysis shows is that the classical factors seem to perform at least as well as the cluster analysis. So, is the cluster analysis something that we could use in the future in our clinical setting?

A recent study comparing cluster analysis with more classical models found more variation in HbA1c response explained by models combining age, gender, baseline HbA1c and BMI than clusters.

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Legend
AUC: area under the curve
ADOPT: A Diabetes Outcome Progression Trial
RECORD: Rosiglitazone Evaluated for Cardiac Outcomes and Regulation of Glycaemia in Diabetes trial


Dennis JM, Shields BM, Henley WE, Jones AG, Hattersley AT. Disease progression and treatment response in data-driven subgroups of type 2 diabetes compared with models based on simple clinical features: an analysis using clinical trial data. Lancet Diabetes Endocrinol. 2019 Jun;7(6):442-451.
http://www.ncbi.nlm.nih.gov/pubmed/31047901