The Effective Statistician - in association with PSI

This is an extraordinary episode that takes us deep into the world of modern pharmaceutical research. Today, we are embarking on a captivating journey guided by the insightful dialogue between me and Elena Polverejan. Throughout this episode, we uncover the incredible potential of Estimands Framework, a revolutionary concept born from the collaborative efforts of brilliant minds dedicated to transforming clinical trials. Join us as we delve into the origins of Estimands, discover its practical applications in the complex realm of depression studies, and witness the meticulous process of creating a practical guide that empowers research teams worldwide. This conversation not only sheds light on the complexities of pharmaceutical research, but also reveals a blueprint for progress, illuminating a path towards more accurate, efficient, and impactful clinical trials. So, listen now and get ready for an enlightening exploration into the future of pharmaceutical innovation while we discuss also about the following points:

I'm thrilled to dive into a topic that has been untouched in the past 300 episodes - crossover studies. It's a classical design that offers unique advantages, yet it's often overlooked. Joining me today is Markus Savli, a biostatistician with a diverse background in biomedical engineering, mathematics, and epidemiology. Together, we'll explore the intricacies of crossover studies and their applications in scientific research. .A crossover study is a longitudinal study where subjects receive multiple treatments in a sequence. This design allows subjects to serve as their own controls, enhancing the ability to address confounding factors. Crossover studies are particularly valuable in scenarios where treatments need to be compared, and their effects measured over multiple periods. Listen to this episode now while we walk through the following key concepts:

In the intricate world of statistics organizations, a common challenge often arises: how to balance increasing cost pressures while maintaining the quality and relevance of the produced tables. In this episode, I dive into this recurring scene, shedding light on the art of negotiation on the numbers of tables. I also talk about the following important points:

In today's episode, I discuss the crucial topic of risk management in clinical trials with guest Jess Thompson. Jess, the founder of the Association of Clinical Research Project Managers, shares her extensive experience in the field, emphasizing the importance of proactive risk identification and collaborative communication within project teams. The conversation delves into practical strategies for risk assessment, impact scoring, and contingency planning, highlighting the significance of involving all team members, including statisticians, in the risk management process. We talk more about the following key points:

In this episode, I interview Katharina Schueller, a statistician and entrepreneur, to discuss the importance of ethical guidelines for statisticians. Katharina shares her insights on the ethical aspects of statistics, the responsibilities of statisticians, and the relevance of the Declaration of Ethics by the International Statistics Institute (ISI). We explore various values, examples, and practical applications related to ethical statistical practices. Here are some more points we discuss:

The modern working environment is defined by endless to-do lists, tight deadlines, and the expectation to do more with less. As employees struggle to manage the job responsibilities of 2 or 3 people, exhaustion happens first. Long working hours, with little to no downtime, lead to mental and physical fatigue. Working like this is harmful and not sustainable, but many of us keep going because of the fear of judgment, rejection, or missing out on a promotion. In this episode, I explore the steps that employees can take to prevent burnout and improve their overall experience at work.

Welcome to the third and last part of Moving from SAS to R where Thomas joined us again to talk about transitioning from SAS to R. We also discuss the new SAS to R course of The Effective Statistician and you’ll learn, if this is the right course for you. We provide a couple of learnings from the course for you to get an impression of what we cover in the course. Click here to get to the course overview! We also discuss the following points:

This episode is the second part of Moving from SAS to R with Thomas. We also discuss the new SAS to R course of The Effective Statistician and you’ll learn, if this is the right course for you. We provide a couple of learnings from the course for you to get an impression of what we cover in the course. Click here to get to the course overview! https://theeffectivestatisticianleadershipprogram.teachable.com/p/open-source-programming We also discuss the following points:

The use of R in the pharmaceutical and healthcare industry has been increasing over the years, with larger companies like Roche adopting it as their primary statistical software. Others like GSK, NovoNordisk, and Merck make heavy investments into it. In this episode, Thomas Neitmann discusses various lessons from his career within pharma and the role R played in it. He provides insights into how to start with R and what are key differences to SAS. We also discuss the new SAS to R course of The Effective Statistician and you'll learn, if this is the right course for you. We provide a couple of learnings from the course for you to get an impression of what we cover in the course. Click here to get to the course overview! We also discuss the following points:

The healthcare industry is constantly developing, and with it come new regulations and guidelines that businesses must adhere to. One regulation that has recently been set in Germany is the 15% threshold for relevant improvement for some endpoints used in clinical trials. Many have critiqued this threshold. It's important for all statisticians (not only for the market access experts) to understand what it is and how it can affect drug approval. In this episode, we'll dive deep into the 15% threshold and why it's essential for statisticians beyond the market access experts to be knowledgeable about it. We specifically discuss the following important points:

The field of statistics is constantly growing and evolving, and with it, the expectations of young statisticians towards their management are also changing. Trust, accountability, and transparency are crucial elements in building a successful team, and they are especially important for young statisticians who are starting in their careers. In this episode, Evanthia, Arina, and I discuss the importance of trust and exposure in career development as well as the expectations of young statisticians. We also examine the recent EFSPI Statistical Leaders Meeting and their discussions on trust, vision, and exposure. Stay tuned as we also discuss the following points:

Losing a job is never an easy experience. It can be especially challenging when it happens unexpectedly. Unfortunately, layoffs are a reality for many people in any stage of their career. Even if an individual has been working at the same company for years, they are surely not be immune to layoff decisions. That's why it's crucial to prepare in advance and build a safety net for job security. In this episode, I discuss some tips on creating a Plan B strategy that can help you to stay employable. Here are some points and strategies:

Podcasts have become a popular form of media for sharing information, telling stories, and building an audience. But have you ever wondered what goes on behind the scenes of creating a successful podcast? Today, I have the pleasure of sitting down with Reine Escalona and Kacey Tunac, the podcast production and management team called VVS, who works alongside this successful podcast - The Effective Statistician. As you already know, I started this podcast six years ago and have recorded over 300 episodes with the help of VVS. The podcast has seen steady growth, now with 14,000+ subscribers and over 200,000 downloads. I achieved this growth through the use of effective marketing strategies, consistent content creation, and excellent audience engagement. And of course with my outstanding team at VVS. In this episode, we explore the tools, skills, and strategies needed to start a podcast and grow its audience from the ground up. We also discuss in detail the following points:

You have to be extroverted to be a leader. Only if you have employees are you a leader. Either you have it or you don't. Leaders have a natural gift, you can't learn it. Those are three typical myths about leaders. In today's episode, I talk with Gary about these three false beliefs. We point out why they're false and give some counterexamples. We also discuss why they limit your development and make you a victim, so to speak. As statisticians, we are often introverted. We can use this to our advantage. We can take leadership, in our cross functional teams. And we can learn these leadership skills as well. All good leaders are always learning. Listen to the latest episode and share it with your colleagues.

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 episode, I discuss the three things about yourself that could make a significant difference in your career and personal development. While many self-help resources exist, this episode provides practical insights into understanding yourself better. I talk about the three important things you need to develop to achieve personal and professional growth:

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 here and thank you for the kind words of intro. [00:00:35] Alexander: Yeah. Maybe for those who don't know you, you can shortly introduce yourself and what you brought into LifeSciHub. [00:00:44] Sheila: Sure. I'm Sheila Mahoney-Jewels, I've been in pharmaceutical research and development for nearly 25 years. I originally started in regulatory operations on the sponsor side. I moved over to the vendor side. [00:01:00] In 2014, I became an independent consultant. I have been I've evolved beyond regulatory. I'm now a, I consider myself a cross functionalist, having worked directly in projects in almost every domain area of expertise. Not the lab bench and not the sales force of a pharmaceutical company, but almost everything in between. And I am now focused. In addition to continuing to be an independent consultant myself, directly working on r and d projects, I've created LifeSciHub, which is a new approach to finding non fulltime employee talent. [00:01:45] Alexander: Yeah. And we'll get into this now with this episode today. I hear so many kind of people talking, oh, it's so hard to find talented people. It's so hard to find [00:02:00] experts. Do you agree with that kind of proposition? [00:02:04] Sheila: Yes and no. I see an abundance of talent in the LifeSciHub community and network in terms of there are many independent small business of one experts. On the drug sponsor side, I do hear a lot of challenges in finding talent that, as a matter of fact, it's I've been surprised investors that I've spoken to have said that talent, they're very interested in any solutions that are addressing r and d talent. Because access to talent is actually a significant risk to their portfolio companies. So they've invested in these small biotechs and they are seeing the struggles and the, I'm hearing this from more than one investor. They've actually reached out to LifeSciHub because they're actively seeking talent solutions. So yeah, I think [00:03:00] it's real. [00:03:01] Alexander: Yeah. Yeah. And these interesting discrepancy. On the one hand you have companies that struggle to get really high quality experts. We are not talking about the people that, come fresh out of university but people that have a deep expertise that are fundamental to the success of smaller companies but also bigger companies. And on the other hand, we have these. Individuals or these peoples that work, let's say in small companies, let's say they just have a partner or it's a, the three of them, they have formed a new company. Lots of these. So the question is, first for me, why do these, very talented people? Why don't they go for working for big company? Why don't say hire sponsor one of these, big full service providers. I would [00:04:00] say, why don't say Itk? [00:04:02] Sheila: Could you repeat the question? I apologize. [00:04:04] Alexander: So why do these very talented individuals, these people, with a deep expertise in their area of specialty, Why don't they work for a big pharma company or a big full service provider? [00:04:22] Sheila: Ah, I see. So in other words, why are they independent? Instead of working full-time or working for a large pharma. There's a few different reasons. Work-life balance. As a matter of fact, LifeSciHub has conducted a number of studies on this population and or a new talent pool, quote unquote as it could be called very gig economy talent. And work-life balance and flexibility, choice of projects, all of these things come up an awful lot. There's a really great story that really speaks to, I think, a primary reason why

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 need to work together, share information openly, and cultivate a culture of curiosity and experimentation. In this part 2, Mouna, Kaspar, and I share our insights on the strategies for innovation in statistics for drug development. We also talk about these specific points:

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 strategic planning in driving statistical innovation. In this first part of the episode, we will discuss their perspectives on commercializing statistical inventions, leveraging external resources, collaboration, and setting up a community of statistical methodology leaders. Join us while we also discuss the following points:

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 this industry. Specifically, I discuss an experience with helping a competitor with their need to communicate data effectively in order to differentiate their compound from the standard of care. Listen to this episode to learn about the value dossier, innovative data visualizations, and practical tips for improving data visualization skills. I also discuss more points such as the following:

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:

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:

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.

If you’re a statistician working in a regulatory setting, you’re probably familiar with estimands as a way to frame the clinical question of interest. But what’s the role of estimands beyond regulatory approval, in a Health Technology Assessment setting? That question is especially important these days where the framework for pan-European HTA is taking shape. In this episode, we’ll touch on some HTA body views around estimands, and reflect on how current draft guidelines for EU HTA do and don’t address estimands.

Are you a statistician, data scientist, or healthcare specialist looking for the perfect conference to attend? The Effective Statistician Conference 2023 is an exciting opportunity for statisticians, health care specialists, and data scientists to come together and learn from industry experts, exchange ideas, share breakthrough advancements in statistics, collaborate with peers, and stay ahead of the latest trends. This virtual event will happen on the 25th of April and the registration is FREE. Through comprehensive talks delivered by influential and seasoned professional speakers across many disciplines – statistics across the R&D phases, process optimization, data visualisation, leadership, simulation, ... – attendees can make meaningful connections that help them become more effective problem-solvers in the ever-evolving digital age. This highly anticipated event is sure to leave everyone inspired and well-equipped with newfound knowledge ready to apply to their practices. Let's revolutionize how statistics can drive innovation! In this episode, I'll be discussing these points in detail:

As a statistician, have you ever wondered—why are statistical analyses for HTA dossiers differ from those in regulatory dossiers? Aren’t they both just benefit-risk assessments? In this podcast, we review some of the history and background of how HTA and regulatory decision making have common sources of information, but different utility functions for how they use that information. Why have we had a common European regulatory framework, but not a common HTA framework—until the near future. And what is the EU HTA Regulation? Why has it been established, what does it hope to accomplish…..and why does this matter to statisticians working in the pharmaceutical industry? This podcast is the first of a series of 4 exploring the role of statisticians and statistics in HTA analyses, and how the new European HTA Regulation that will first start being applied to medicinal products in 2025, will impact statisticians working with clinical (and non-clinical!) data to support and evaluate HTA decision making. Learn how you can get more involved in shaping the future of EU HTA as a statistician at the local and regional level.

Are you curious about aggregate safety assessment planning? Whether you’re an experienced statistician or just starting out in the field, your work increasingly demands that you apply principles of aggregate safety assessment planning. With an ever-changing and advancing landscape filled with data and technology advancements, it is essential to stay current on this topic. In this episode, Greg and I explore what aggregate safety assessment planning is, its components, its importance for statistically informed decisions, and how it ties into patient safety initiatives. You will be equipped with a better understanding of the fundamentals so that your efforts can make powerful contributions in research settings and support meaningful health outcomes for patients everywhere. Stay tuned while we discuss the following key points:

Anyone can create a data visualization but not everyone can create a data visualization that is effectively communicating the data – and this is what we should aim for. Done wrong they leave the audience confused. In this episode, I’ve gathered up six problems around creating data visualisations, so you can improve the quality of your visualisations. Stay tuned while I talk about these 6 problems:

A few days from today, it will be Christmas already and I am sure you are very thrilled to get ready for Christmas! And it's almost time for another year and as the New Year brings a lot of challenges, I am excited to share with you my plans and projects for 2023 that you and I both will definitely benefit from and these events and projects will surely help you achieve more with your personal growth and especially, your career growth at the same time. Tune in while I discuss the following exciting events and plans: