Transcript

[00:00:00] Alexander: Welcome to a new episode of The Effective Statistician, and today I'm super excited to talk with Sheila about her project that I think is a really important part in the overall puzzle of developing new medicines, new diagnostics, and all kind of other things in our world of healthcare. Welcome to the show, Sheila.
[00:00:30] Sheila: Thanks Alexander. So happy to be here and thank you for the kind words of intro.
[00:00:35] Alexander: Yeah. Maybe for those who don't know you, you can shortly introduce yourself and what you brought into LifeSciHub.
[00:00:44] Sheila: Sure. I'm Sheila Mahoney-Jewels, I've been in pharmaceutical research and development for nearly 25 years. I originally started in regulatory operations on the sponsor side. I moved over to the vendor side. [00:01:00] In 2014, I became an independent consultant. I have been I've evolved beyond regulatory. I'm now a, I consider myself a cross functionalist, having worked directly in projects in almost every domain area of expertise.
Not the lab bench and not the sales force of a pharmaceutical company, but almost everything in between. And I am now focused. In addition to continuing to be an independent consultant myself, directly working on r and d projects, I've created LifeSciHub, which is a new approach to finding non fulltime employee talent.
[00:01:45] Alexander: Yeah. And we'll get into this now with this episode today. I hear so many kind of people talking, oh, it's so hard to find talented people. It's so hard to find [00:02:00] experts. Do you agree with that kind of proposition?
[00:02:04] Sheila: Yes and no. I see an abundance of talent in the LifeSciHub community and network in terms of there are many independent small business of one experts. On the drug sponsor side, I do hear a lot of challenges in finding talent that, as a matter of fact, it's I've been surprised investors that I've spoken to have said that talent, they're very interested in any solutions that are addressing r and d talent. Because access to talent is actually a significant risk to their portfolio companies.
So they've invested in these small biotechs and they are seeing the struggles and the, I'm hearing this from more than one investor. They've actually reached out to LifeSciHub because they're actively seeking talent solutions. So yeah, I think [00:03:00] it's real.
[00:03:01] Alexander: Yeah. Yeah. And these interesting discrepancy. On the one hand you have companies that struggle to get really high quality experts. We are not talking about the people that, come fresh out of university but people that have a deep expertise that are fundamental to the success of smaller companies but also bigger companies.
And on the other hand, we have these. Individuals or these peoples that work, let's say in small companies, let's say they just have a partner or it's a, the three of them, they have formed a new company. Lots of these. So the question is, first for me, why do these, very talented people? Why don't they go for working for big company? Why don't say hire sponsor one of these, big full service providers. I would [00:04:00] say, why don't say Itk?
[00:04:02] Sheila: Could you repeat the question? I apologize.
[00:04:04] Alexander: So why do these very talented individuals, these people, with a deep expertise in their area of specialty, Why don't they work for a big pharma company or a big full service provider?
[00:04:22] Sheila: Ah, I see. So in other words, why are they independent? Instead of working full-time or working for a large pharma. There's a few different reasons. Work-life balance. As a matter of fact, LifeSciHub has conducted a number of studies on this population and or a new talent pool, quote unquote as it could be called very gig economy talent. And work-life balance and flexibility, choice of projects, all of these things come up an awful lot. There's a really great story that really speaks to, I think, a primary reason why

In part 1 of this series, we explored the perspectives on commercializing statistical inventions, leveraging external resources, collaboration, and setting up a community of statistical methodology leaders. Innovation is essential for advancing drug development, and statisticians play a vital role in driving statistical innovation. However, to overcome the barriers to innovation, statisticians need to work together, share information openly, and cultivate a culture of curiosity and experimentation.

In this part 2, Mouna, Kaspar, and I share our insights on the strategies for innovation in statistics for drug development. 

We also talk about these specific points:

Statistical innovation is vital in the pharmaceutical industry as it drives evidence-based decisions and brings value to patients. However, it requires a delicate balance between invention and commercialization to achieve success.

I, together with Mouna Akacha and Kaspar Rufibach - two leaders of statistical innovation groups in big pharma companies, share their insights on the barriers and strategic planning in driving statistical innovation. In this first part of the episode, we will discuss their perspectives on commercializing statistical inventions, leveraging external resources, collaboration, and setting up a community of statistical methodology leaders.

Join us while we also discuss the following points:

In any industry, staying competitive and relevant requires a constant focus on innovation and the ability to communicate effectively with customers. In the pharmaceutical industry, this is particularly true as companies race to develop and market new compounds that can improve patient outcomes.

In this episode, I share insights into the importance of effective data communication for success in this industry. Specifically, I discuss an experience with helping a competitor with their need to communicate data effectively in order to differentiate their compound from the standard of care.

Listen to this episode to learn about the value dossier, innovative data visualizations, and practical tips for improving data visualization skills.

I also discuss more points such as the following:

As a statistician, you're likely already familiar with SAS and the value it brings to data analysis. But in this ever-evolving world of data science, relying on your go-to language might not be enough to stay competitive anymore. The open-source language R is gaining traction within many industries as a powerful tool for analyzing complex datasets.

In this episode, join me while I talk about why learning R can help you stay ahead of the game—and why now is the perfect time to dive into its growing popularity among healthcare specialists and statisticians alike.

 I specifically talk about the following points:

In the world of statistics, information can sometimes be missing in data sets, leading to a challenge in understanding how well treatments may work. Policy estimands are used to understand efficacy based on early treatment decisions. Various approaches, like reference-based imputation and delta adjustment, exist to speculate what may have happened after treatment was discontinued. However, these methods are often inconsistent, and more efficient methods are required.

In this episode, Alberto and I discuss how his new approach can handle different scenarios with missing data and can estimate policy estimands for faster results. As a 26-year veteran of the pharma statistics industry that recently completed his PhD research, Garcia brings a wealth of knowledge and experience to this topic.

So, let's dive into the details of this innovative framework for estimating policy estimands such as the following:

Statisticians play a significant role in the clinical development of pharmaceutical products and medical devices. Their primary focus is to design and analyze clinical trials to provide evidence for regulatory approval and Health Technology Assessment (HTA). However, despite their valuable contributions, statisticians rarely get the chance to communicate directly with physicians, caregivers, and patients, the last hurdle in the medical affairs of pharmaceutical products. Communication often relies on multiple layers such as publications, slide sets, promotional material, and sales representatives. Poor communication of data can lead to misunderstandings, harm patients and even lead to legal issues. This is where statisticians can have a big impact.

In this episode, we discuss how statisticians can enhance their capabilities to reach physicians and patients beyond regulatory and HTA success. We also talk about the following points:

Interpretable machine learning (IML) is rapidly gaining popularity in the data science community. It offers a new way to build and interpret models that are more transparent and understandable. In this episode, we have the privilege of interviewing Serg Masis. Serg authored the book "Interpretable Machine Learning with Python: Learn to build interpretable high-performance models with hands-on real-world examples".

You'll see, that this concept not only applies to very complex models but even to simple regression models with several factors. 

He walks us through the concept of interpretability and explains why it is better than explainability. He also discusses black-box and white-box models. Additionally, he introduces us to glass box models and explores various topics and modeling approaches related to IML that you don't want to miss.

We also, discuss the following points:

When it comes to clinical trial design, there are many challenges to overcome, including recruitment, protocol design, and data analysis, among others. Fortunately, simulations can provide a solution to some of those challenges. Simulations allow researchers to test hypotheses, optimize designs, and understand probabilities of success before running the real-world experiment.

Together with Kim Hacquoil and Jamie Inshaw in this episode, we discuss the benefits of using simulations as a study design tool. Kim and Jamie suggest that simulations can be incredibly helpful at different stages of study design, including protocol development, sample size determination, and data analysis planning. By using simulations, researchers can optimize their designs, identify potential problems, and get a better understanding of the probabilities of success. 

If you're interested in mastering study design and strategy with simulations, this episode is perfect for you! So tune in now while we dive in to these important points:

In today's highly competitive work environment, developing leadership skills such as vulnerability and empathy can mean the difference between success and failure.

In this podcast episode, Marius and I discuss Brené Brown's "Dare to Lead," a book that dives into the importance of vulnerability-based leadership. This episode will explore some of the key takeaways from the book and how they can be applied in the work environment.

We also discuss the following points: