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Are soccer players with dark skin tone more likely to receive red cards?

And why does this matter in drug development?

Statistical analytical methods are often taken for granted. In a recent crowdsourcing data analysis project, Nosek and co-workers found 29 research teams willing to analyze the same dataset [1]. The results varied from positive to neutral. How is this possible and what are the consequences? 

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Topics: Clinical Trial Methods

Posted by Prof. Hans de Vries on Mar 12, 2019 5:11:00 PM
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Artificial Intelligence to Screen for Diabetic Retinopathy; The Times They Are A-Changin‘.

The far majority of clinical trials in diabetes exclude patients with active retinal disease, as interventions that lower glucose rapidly can temporarily worsen retinopathy. This was originally shown in type 1 diabetes [1] but more recently also in type 2 diabetes [2, 3]. Screening for diabetic retinopathy before inclusion in a clinical trial is relatively cumbersome, also because it often involves a separate visit to an ophthalmologist. The use of a fundus camera with offline interpretation by an ophthalmologist has gained widespread use in clinical practice, but not so much in the field of clinical trials.

In this blog I will briefly describe four recent studies on artificial intelligence approaches to automate the interpretation of retinal images. I will conclude with an outlook on how this may facilitate the screening of potential trial participants for diabetic retinopathy. But first a brief introduction to deep learning, the methodology applied in all these papers.

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Topics: The Science behind Diabetes, Treating Diabetes, Diabetes Technology

Posted by Prof. Hans de Vries on Oct 23, 2018 5:18:00 PM
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Cardiovascular outcome trials of incretin drugs: what have we learned?

In 2008, the US Food and Drug Administration (FDA) released a Guidance for industry on evaluation of cardiovascular safety of new drugs for type 2 diabetes [1]. This promoted large-scale global cardiovascular outcome trials (CVOT’s), where the addition of the investigational drug to existing treatment was compared to placebo. So far, three trials on dipeptidyl peptidase 4 (DPP-4) inhibitors  and four trials on glucagon-like peptide 1 (GLP-1) receptor agonists have been reported, all in the New England Journal of Medicine [2,3,4,5,6,7,8]. In total, the staggering number of exactly 70,000 patients were randomized in these trials. Overall, the results range from reassuring to positive, but also give rise to new uncertainties. These uncertainties stem from the much larger datasets on efficacy and safety now available for these new drugs, thus providing us with much more granular knowledge than we used to have for drugs on the market. In other words, with these datasets also the frequency of more infrequent side effects can be assessed with some detail. However, even these large-scale trials are of course not powered for really rare side effects, adding a new level of uncertainty if a non-significant signal is seen.

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Topics: Clinical Trials in Diabetes, Treating Diabetes, Diabetes Technology

Posted by Prof. Hans de Vries on Apr 18, 2018 5:09:00 PM
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