The Effective Statistician - in association with PSI

Personal growth is an integral part of every individual’s life. It is a continuous process of self-improvement, learning, and self-discovery. As a statistician or statistician enthusiast, you should always be on a quest for personal and professional development. The journey towards growth is unique, and understanding yourself is a critical step towards achieving your potential. In this 300th episo...

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...

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 nee...

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 strate...

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 thi...

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 l...

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 m...

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 p...

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...

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 Hac...

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 a...

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 stati...

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, lawy...

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,...

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...

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...

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 dis...

As statisticians, we rely on good quality from programmers and data management. And if you talk to your colleagues, everybody has a story about a poor experience. But the questions are: How do we collaborate with other departments to create a quality deliverables? How to strike the right balance between working on processes and managing people for maximum efficiency? How to we take ownership and d...

What knowledge gaps exist regarding the implementation of these new analytical techniques, and how can statisticians and data scientists bridge them to maximize effectiveness of clinical research? Despite the promises of Bayesian approaches, the lack of expert training, software tools, and computational resources has made their adoption slow. Statisticians and data scientists should invest in trai...

For years, creating data visualizations has frustrated statisticians, data analysts and data scientists. Software - especially SAS - hasn't been great and organisation have tried to take the pain out of the process by creating standard figures.   But these standard graphics fail to communicate the message to the intended audience in the best way. Great data visualisations consider the needs of an...

Have you been considering a one-armed observational study?  As a statistician, it is important to understand the various types of studies that are used in data gathering. In this episode, we will be discussing one-armed observational studies and why they are, in most cases, not a good idea. We will also touch on the early days of my career as a statistician and how I initially approached one-armed...

The world of healthcare continues to change, and it’s important to keep up with the latest advances in technology and research. That’s why I'm excited to talk with Shirley Wang, one of the leaders of RCT Duplicate, a study focused on duplicating randomized clinical trials through real world data.  She is currently leading the RCT Duplicate as the first author on some key publications related to th...

Today I'm talking with Rachel Tham, a principal statistician, about a book that has been incredibly impactful in both of our lives—Deep Work. We discuss what deep work is and how it can help statisticians at work.  Deep work is a concept developed by Cal Newport which encourages people to focus intensely on one task for an extended period of time without any distractions. This type of focused work...

Why are companies often running single arm studies in development?  What are the potential drawbacks and why is it not a binary choice between full comparative study and single arm trial? What are solutions in between to find a balance between feasibility and rigour? Single arm studies are a popular method for collecting data despite being critiqued for decades. It is a type of research design in...

Reproducible research is a key part of research in the pharma industry. It allows for transparency, understanding, and accuracy in the research process. But how can you make your research more reproducible? Today, I talk with Heidi Seibold who has dedicated her career to helping researchers become more reproducible. Let's take a look at 3 steps that she recommended for making your research more re...