One of the most common questions I got asked during my nearly 2 decades of being a statistician sounds similar to this: “Which patients have the best response to treatment?”
I’m sure, we all face this situation sooner or later, and not surprisingly lots of research has happened in the last years in this area. In today’s episode, we will help you to understand one of the best approaches I have come across to solve this problem in a rigorous yet sophisticated way: the SIDES approach.
Both Andy Nicholls and I have applied this approach in the past and we’ll use an example, which he presented during a PSI webinar.
Listen to this episode to learn step-by-step how to apply the SIDES method.
Abstract of the related PSI webinar presentation
Using the SIDES algorithm to identify patient phenotypes that have the potential to benefit most from switching to Relvar
In 2016 GSK completed the Salford Lung Study, a 12-month, open-label, randomized, effectiveness study to evaluate fluticasone furoate (FF, GW685698)/vilanterol (VI, GW642444) Inhalation Powder delivered once daily via a Novel Dry Powder Inhaler (NDPI) compared with the existing COPD maintenance therapy alone in subjects with Chronic Obstructive Pulmonary Disease (COPD). Upon completion of the study, the Scientific Committee expressed an interest in using a data-driven approach to identify patient subgroups for which the treatment effect was strongest. In this presentation, we will look at why SIDES was chosen for this analysis, the design parameters, and how it fared.
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Andy Nicholls
Senior Director, Head of Data Science at GSK
Andy is a Statistician with a strong interest in Data Science, having previously worked as a specialist R Consultant and Data Scientist for Mango Solutions. On re-joining GSK in 2017, Andy provided support to the Relvar project, for which he led an exploratory cluster analysis using Salford Lung Study data to try to identify patient subgroups that might experience an additional real-world benefit of Relvar. He now works in GSK’s new Statistical Data Sciences division within BioStats and is Business Systems Owner for the BioStats HPC environment for R.
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This group was set up to help each other to become more effective statisticians. We’ll run challenges in this group, e.g. around writing abstracts for conferences or other projects. I’ll also post into this group further content.
I want to help the community of statisticians, data scientists, programmers and other quantitative scientists to be more influential, innovative, and effective. I believe that as a community we can help our research, our regulatory and payer systems, and ultimately physicians and patients take better decisions based on better evidence.
I work to achieve a future in which everyone can access the right evidence in the right format at the right time to make sound decisions.
When my kids are sick, I want to have good evidence to discuss with the physician about the different therapy choices.
When my mother is sick, I want her to understand the evidence and being able to understand it.
When I get sick, I want to find evidence that I can trust and that helps me to have meaningful discussions with my healthcare professionals.
I want to live in a world, where the media reports correctly about medical evidence and in which society distinguishes between fake evidence and real evidence.
Let’s work together to achieve this.
