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:

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: