The Effective Statistician - in association with PSI

Interview with Mouna Akacha and Kaspar Rufibach The interplay between invention and commercialization for statistical inventions. Potential barriers to success include lack of knowledge/software, resistance to change, and short-term goals over long-term initiatives. Strategies for success include: Clear communication Patience/resilience in long-term initiatives Providing resources/training, promoting diversity/inclusion in teams Networking/communication skills, collaboration with other functions, pragmatism/compromise for success Freedom to innovate Setting up a community of statisticians. We provide valuable insights into driving statistical innovation, overcoming barriers, and strategic planning. The success of statistical innovation requires clear communication, patience, resilience, collaboration, innovation, and pragmatism. By leveraging external resources and setting up a community of statistical methodology leaders, we can bring value to patients and overcome statistical challenges in the pharmaceutical industry. Learn more by listening to this episode now and share this with your friends and colleagues!

Discussion with Alexander Schacht Companies in the pharmaceutical industry must differentiate their compound from other competitors for successful market performance. The value dossiers and data visualizations to set the company apart. Data visualizations are used to demonstrate superiority to target customers and secure pricing/reimbursement. How can you improve your data visualization skills? Join the Data Visualization Special Interest Group, which is an online community of professionals committed to improving their data visualization skills. The group hosts regular events and discussions on best practices and new tools and techniques. Take The Effective Statistician Data Visualization Course, which is designed to help statisticians and other professionals develop their data visualization skills in a practical and effective way. Share this episode with your friends and colleagues who might learn from this!

Discussion with Alexander Schacht SAS is no longer focussing on pharma and has shifted the priority into other industries such as finance. R offers various benefits, including cost savings, innovative methods, universities shifting towards R, simpler/cheaper training due to its open-source approach, and much more. More statisticians are considering transitioning from SAS to R; The Effective Statistician Academy offers a course designed for this purpose. Learning R is not meant to replace SAS but supplement it (at least for the mid-term) Do not limit yourself to SAS only. Take the initiative to learn R and its advantages to enhance your skillset and knowledge. Learning R may seem daunting at first, but with the right guidance, and through The Effective Statistician Academy, you can effectively transition from SAS to R, expanding your skills and knowledge, and ultimately boosting your career. Are you interested to learn R Course extensively? Then, this course is for you! The Effective Statistician From SAS to R Course Share this episode with your friends and colleagues!

Alberto Garcia Hernandez, an expert in estimating policy estimands, discusses the framework for estimating policy estimands, utilizing multiple imputation and analytical models for faster results. They cover topics such as competing intercurrent events and censoring, adjusting treatment effect size, and the importance of understanding research questions. They also mention webinars related to data visualization.

The podcast discusses the importance of statisticians in the pharmaceutical industry and their role in improving communication with stakeholders. It highlights the need to provide physicians and patients with meaningful data for informed decisions. The episode also explores the influence of statisticians in improving communication in the healthcare industry, particularly in the promotional material and claims made to physicians and patients.

Interview with Serg Masis Benefits of IML Interpretability as the ability to understand how a machine learning model works Interpretability is better than explainability. Black-box models can be interpretable if viewed from a different perspective. Glass box models gaining popularity in IML and provide the benefits of both white-box and black-box models. Various modeling approaches related to IML, including rule-based models, decision trees, local linear models, and additive models.

Interview with Kim Hacquoil and Jamie Inshaw A new course explaining how simulations can improve study design and data decision-making. The course outline. Benefit of honing simulation skills for all statisticians, both early-phase and late-phase trials, in order to provide more accurate advice to colleagues, and more! The benefits of simulations include improved protocol development, sample size determination, and data analysis planning. Kim and Jamie's new course, "Mastering Study Design and Strategy with Simulations," offers a comprehensive guide to using simulations in study design and is available from July 2023. By improving their simulation skills, statisticians can provide valuable insights to their colleagues, resulting in better-designed trials and more efficient data analysis. Share this episode with your friends and colleagues now!

Interview with Marius Sieverding Brown's "Dare to Lead" emphasizes the importance of cultivating courage and bravery in leaders Embracing vulnerability, Being true to values Setting boundaries Knowing and understanding their “why” Asking for help Holding oneself accountable Empathy Brené Brown's "Dare to Lead" offers practical advice and research-backed evidence to help leaders develop trust with others through empathy and vulnerability. Through Marius' PSI Book Club review, statisticians could delve deeper into the book, have rich discussions, and apply its principles in the work environment. Emotional connection, accountability, and empathy are just some of the many essential tools leaders might utilize to create a more positive work environment. Ultimately, cultivating these leadership skills can benefit the whole team and lead to better experiences for both employees and employers alike. So listen to this episode now and share this with your friends and colleagues!

Interview with André and Evelyn Hund Clivoffective's platform How connects candidates with hiring managers The exclusive access to networking, training, and career advancement opportunities. Its next-level recruitment process by introducing its 'Active Talent Selection' approach, which ensures that candidates are connected with the right employers. The pre-evaluation process. Clivoffective has revolutionized recruitment in the statistics market. With Clivoffective at the forefront of recruitment in the statistics market, the future looks bright for candidates and employers alike. Listen to this episode now and share this with your friends and colleagues who are looking for opportunities! Click here to learn more about Clivoffective and join the network: Clivoffective

Interview with Debarshi Dey A statistician should act as a detective by asking questions to uncover what is really being asked of them and navigating uncertain spaces to reach conclusions. When acting as a lawyer, statisticians must keep in mind who their audience is in order to present information effectively. A statistician should act as a journalist that understands how evidence is used down the line, such as when presenting figures or tables to clients or stakeholders. Being a killer statistician is not just about number crunching and data analysis. It is about adopting multiple roles to uncover insights, communicate effectively, and ensure real-world impact. Developing these skills requires a combination of methodological proficiency, curiosity, and awareness of the context and audience. By following Debarshi's advice and curating role models, we as statisticians can become successful and impactful. Listen to this episode now and share this with your friends and colleagues who can learn from this! Resource: What makes a killer statistician?