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:

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 drive responsibility to learn from mistakes and not blaming each other?

Tension often arises between statisticians, programmers, and data managers when it comes to delivering quality products in a timely manner.

In this episode, Benjamin and I discuss how you can maintain a high level of quality that satisfies everyone. We talk about the importance of good communication. We discuss the balance between processes and people. While sophisticated processes may be necessary, relying too much on documentation can lead to missed opportunities or overlooking potential solutions.

Listen to this episode now while we dive deep into these points below:

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 training programs to enhance their skills in Bayesian modeling, use open-source software for Bayesian modeling, and ensure that they have access to well trained computational analysts. 

It is also essential that statisticians and data scientists effectively communicate the results of these analyses to clinicians, regulatory authorities, and maybe even patient groups to encourage adoption.

Bayesian approaches have great potential to improve the accuracy and efficiency of data analysis in early-phase clinical trials. But Bayesian methods are not without their challenges, but with adequate training, resources, and communication, they provide opportunities for novel ways to cope with complex problems in clinical research.

But, first comes the challenge: prior elicitation from clinical information. Developing prior distributions can be difficult without the right set of tools and resources.

In this episode, Miguel Pereira, a statistical consultant for a German-based company COGITARS specializing in early clinical trial design, and I highlighted ways to tackle these challenges.

We also discuss the following points:

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 audience in terms of language, understanding, and context, as well as the communication channel and the message to be conveyed. With so many elements to take into account, the design space for visualisations can be overwhelming with factors such as color, filtering data, animation, and uncertainties all coming into play.

Unfortunately, many senior people in large statistics organizations focus only on regulatory requirements and don't invest time in understanding the importance of visualizations.

In this episode, I discuss why it is difficult to create a great standard data visualization and what organizations can do to improve their data visualization game.

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 observational studies.

We also discuss the reputation problems of these studies and the scientific problems such as:

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 the initiative. She has been instrumental in helping move the project forward by analyzing data from various sources and developing new methods for collecting information from real-world settings. Her work has helped pave the way for more reliable findings based on real-world evidence—which will ultimately benefit everyone who works in healthcare. 

In this episode, we discussed RCT Duplicate's goals and recommendations for real-world evidence researchers based on findings from the initiative. We also discuss the following points:

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 allows people to produce higher-quality results in less time than if they were trying to multitask or do shallow tasks.

Join us and learn more about deep work concepts and how they can improve your productivity and satisfaction.

Rachel and I discuss the following points:

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 which the investigator only observes one group of participants over time. 

This design is often used when sponsors claim it would be unethical to randomise patients into different groups or when comparing two treatments would be too difficult. 

However, there are several drawbacks associated with single arm studies. First of all, bias can easily creep into the results.

Anja Schiel - the most recognised regulatory statistician in Europe -  and I discuss the potential drawbacks of running single arm studies, as well as ways to balance scientific rigour with feasibility.

Tune in while Anja and I give some of the great advice we have come up with. We also discuss the following points:

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 reproducible: