The SONIA trial made headlines by challenging a major assumption in breast cancer care: that drugs like CDK4/6 inhibitors must be used right away in advanced HR+/HER2– breast cancer. The study found no significant difference in survival between using these drugs early or later. It even showed that later use led to fewer side effects and lower costs. But not everything was so straightforward.
The article by Matsuo et al. (2019) appeared in my newsletter. It is another attempt to sell deep-learning (DL) as a promising alternative to traditional survival analysis. To this end, they compare the performance of their DL model to Cox proportional hazard regression (CPH) when predicting survival for women with newly diagnosed cervical cancer.
Overview Mitani and co-authors’ present a deep-learning algorithm trained with retinal images and participants’ clinical data from the UK Biobank to estimate blood-haemoglobin levels and predict the presence or absence of anaemia (Mitani et al.
Poor quality statistical reporting in the biomedical literature is not uncommon. Here is another example by Cirio et al. (2016). The study itself is well planed, executed and reported. The aim was to assess whether heated and humidified high flow gases delivered through nasal cannula (HFNC) improve exercise performance in severe chronic obstructive pulmonary disease (COPD) patients.