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:

Are you a statistician working in the pharmaceutical industry and never got in touch with economic modelling and network meta-analyses? Then you should listen to this episode!

The EU HTA will not only affect all statisticians in the pharmaceutical industry with respect to skill sets and collaboration (we talked about that in podcast #3), but will also have impact on the economic modelling that is needed for the reimbursement and pricing decisions in many European countries. Understanding the influence of the joint clinical assessment on the economic modelling, the relationship between estimands and PICOs as well as the pre-specification of statistical analyses and their use in economic modelling is becoming much more important for statisticians in the near future.

In this episode, we will talk about role of economic modelling in the HTA process, and the influence of the EU HTA and the corresponding statistical analyses on that process.

All statisticians in the Pharmaceutical Industry will be impacted by the new EU HTA regulation. Activities around HTA submissions will happen earlier than currently, in parallel to the regulatory approval process for marketing authorization. There will be an increased scope of evidence for the joint clinical assessment to fulfill the needs of all EU member states, and so there will be a large package of statistical analyses that need to be provided in addition to the submission to the regulatory bodies.

This will redefine how you, as a statistician, work, and with whom you need to collaborate. Both HTA and clinical development statisticians will need to join forces to define the value story for the complete lifecycle of the drug. HTA specific analyses will need to be planned in parallel with clinical development.

In this episode, we will discuss the future skill sets that statisticians in the pharmaceutical industry need to adopt with the new EU HTA regulation being applied in 2025 already.