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You are here: Home / Innovation / Subgroup identification using SIDES and its practical challenges

Subgroup identification using SIDES and its practical challenges

By Alexander on 2019-05-14 0

Subgroup identification using SIDES and its practical challenges
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Interview with Andy Nicholls

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 on this area. In todays 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 the identify patient phenotypes that have the potential to benefit most from switching to Relvar

In 2016 GSK successfully completed the Salford Lung Study, a 12-month, open label, randomised, 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 in order 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.

About Andy Nicholls

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 in order 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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