Free webinar: Do patient preference have a role in Health Technology Assessment? Current practice and future potential

By Kevin Marsh

Patient preferences can help us to inform the weighting of different outcomes against each other.

Kevin Marsh

In questions like “How much more efficacy is needed to outweight tolerability issues?” preferences by patients or other stakeholders could play a key role in quantitative benefit-risk assessments.

 

The 1-hour webinar takes place on October 24th at 4pm CET, 3pm UK time, and 10am ET in the US.

There will be lots of time for discussions after the presentation.

REGISTER HERE!

Kevin Marsh will speak in our webinar about

Do patient preference have a role in Health Technology Assessment? Current practice and future potential

Abstract: Both regulators and payers are actively exploring how they might use quantitative estimates of patient preferences to support their decisions. This is evident in initiatives such as IMI PREFER.

Feedback on the EFPSI workshop on regulatory statistics

Hi Alexander,

If I were able to attend the workshop, I would be most interested in the topic of the Role of Statisticians.  We can be consultants, contributors, collaborators and in some instances leaders, depending on where statisticians are positioned in their companies.  We can develop, test and/or apply new methods, depending on our training.  Because we carry the stigma of being support staff for clinical trials, a leadership role might require a new frame of mind.   Will you telling the audience what role statisticians most often play?

I liked this line from a recent post, “The understanding of data – its strengths and limitations –is a core competency of statisticians. Thus, they need to play a key role in helping others to understand and interpret the efficacy and safety data correctly.”

This competency sounds a lot like a data scientist, who seem to market themselves better than statisticians.  They remind us that they are in short supply, have a unique blend of cutting edge technical skills, can predict the future and use terms that corporate execs don’t understands such as support vector machines.

Perhaps Benefit-Risk involves more stakeholders than Data Science, making it more difficult to lead.  Perhaps by providing more clarity & transparency to decision-making, Benefit-Riskiticians are less mysterious than Data Scientists. Whatever the case, we can learn something from data scientists.  Will you be telling the audience how statisticians can better promote their abilities and have an equally sexy career as data scientists?

Kind regards,

Mike Colopy, PhD