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:

The field of statistics is rapidly growing, and statisticians play an important role in providing a competitive advantage to companies. However, finding the right job or candidate for a position can be challenging. That's where Clivoffective comes in - a collaboration between Clivolution and The Effective Statistician focused on connecting statisticians with career opportunities.

If you're a statistician or hiring manager in the field, Clivoffective offers a personalized touch that sets it apart. Whether you're looking for a new career opportunity or seeking the right candidate for your company, Clivoffective has resources and expertise that can help make the process smoother and more efficient. 

Join the Clivoffective Network to get exclusive access to networking opportunities and career development resources that can help take your career to the next level.

In this episode, I explore together with Evelyn and André what Clivoffective has to offer and how it benefits both statisticians and hiring managers. We also talk about the following points:

In the field of statistics, we need specific skills necessary for being successfull. Debarshi Dey, currently the head of the Statistics and Programming and Data Management department at Morphosys, has spent the past 13 years in the pharmaceutical industry. With a PhD in statistics from UC Riverside, Debarshi believes that to be a "killer statistician," one must adopt the roles of a detective, lawyer, and storyteller.

Debarshi and I discuss these key characteristics that make a statistician effective.

We also explore these key takeaways from our conversation:

We statisticians have always been important for brining new therapies to patients. We design experiments, analyze and interpret data to provide valuable insights that can help make informed decisions. However, the value of our work increases if we're able to lead others. 

But becoming an inspirational statistician requires more than just technical expertise. As you continue to grow in your career, don't forget to invest in yourself by learning from training programs, books, movies, and other resources that can help you become an inspirational leader.

In this episode, Alun Bedding and I discuss how statisticians can become inspirational leaders and reach a higher level in their careers. We also explore suggestions and give additional insights on how statisticians can become leaders in their field. 

Becoming an inspirational leader as a statistician requires a mindset change from technical proficiency to people skills. Leaders, by inspiring others, can help their and other teams grow and work towards the organization's goal. Building trust is key to becoming an inspirational leader.

Alun and I also discuss these important points in order to level up your career and organisation:

Are you a statistician or data scientist looking to excel in your career and make an impact in the healthcare industry?

Today's episode is inspired by a German podcast that delved into five key reasons for career failure and I fine-tuned this to fit statisticians.  As professionals working with complex projects and tight deadlines, it's important that we recognize these pitfalls before they cause irreparable damage to our success.

Join us as we explore each of these five potential hazards, discuss why they have such potential for harm, and learn from engaging expert advice on how best to prevent or overcome them:

If you're a statistician, data scientist, or programmer looking for quality training opportunities that will help you advance your career, you might want to look into The Effective Statistician Academy. This exclusive academy offers a range of courses that cover essential topics in the field of statistics. From leadership to innovation, knowledge, and excellence, you're sure to find a course that fits your needs and goals.

The Effective Statistician Academy offers four distinct areas of training: Leadership, Innovation, Knowledge, and Excellence. Each area features expert-led courses that are designed to help you gain the skills and knowledge you need to succeed in your career. Whether you're looking to take your leadership skills to the next level or stay up-to-date on the latest innovations in the field, the Academy has you covered.

In this episode, I will discuss more the key features of the Academy, the topics it will cover, the top-notch speakers who have extensive experience and in-depth knowledge of the subject matter, and why you won't have to break the bank to take advantage of this exciting opportunity. 

I also talk about the following points:

What strategies do you use in your work as a statistician to ensure successful outcomes?
How do you set expectations with stakeholders when tackling a project?
Are there any common mistakes statisticians make when setting expectations in their job that you have learned to avoid?
How setting realistic expectations can positively impact your work as a statistician?

As I embark on this journey of discussing expectations, it reminds me of an incident that happened to me recently. During one of my business trips, the coffee I ordered at an airport was quite enjoyable; however upon opening the cup - much to my dismay - only half filled with liquid gold! The size and shape established a certain expectation for what should be inside which created feelings of dissatisfaction due to unfulfilled hopes. It goes without saying that if they would have had appropriately sized cups, then there wouldn't have been any misalignments in regard to meeting expectations. Fascinating how something as small scale such different outcomes can make or break experiences!

As statistical professionals, we frequently encounter situations where our technical expertise is required to communicate complex data insights to a non-statistical audience. Creative data visualizations, tailored to the audience, are an effective tool to help us overcome this communication gap. By presenting information in a clear and understandable manner, we can over-deliver expectations and make a real impact on our work projects.

In this episode, I give more tips on how to set expectations and the following points: