Welcome to The Effective Statistician Conference!

After over 250 episodes of The Effective Statistician Podcast and many webinars, I’m very happy to invite you to the first conference of The Effective Statistician.

The conference will take place on April 25th, 2023.

And you will get the registration for free.

The conference will start at 1pm CET (7am East Coast US) and end at 6pm CET (noon East Coast US).

The 5 hours will be filled with short presentations (15-20 minutes each) and Q&A sessions.

Those, who have registered, will also get access to the recordings of the conference.  

We will cover a great collection of topics, which will be relevant to statisticians in healthcare:

  • Estimands
  • Bayesian approaches in early development
  • Medical affairs statistics
  • Digital health applications
  • Simulation of studies and development plans
  • Network meta-analysis
  • Working effectively as a researcher
  • Leading without authority
  • Optimizing your processes
  • Data visualisation


The following speakers have already agreed to present:

  1. Kaspar Rufibach – Expert Statistical Scientist within the Methods, Collaboration and Outreach group at Roche

    Title: Some challenges with implementing estimands in real life

    Ever planned a trial and struggled with features such as potential cure, multiple treatment decision points, or complex interventions such as transplantations or CAR-T? Or been interested in overall survival and wondered how to account for treatment switching after a drug has been approved based on progression-free survival?

    Have you been involved in discussions around how to assess and model the impact of Covid or the war on Ukraine on a clinical trial?

    Have you ever been asked to run analyses for “subgroups” generated by post-randomization variables, such as e.g. occurrence of some toxicity or dose modification?

    And the question of all questions: Can you explain in mathematical terms why we randomize?

    Often, these type of questions are answered using simplified analyses that are only valid under strong assumptions. 

    Using examples we will provide answers to the above questions, by discussing the application of the ICH E9(R1) addendum to clinical trials. To make implicit assumptions transparent we will discuss estimands, estimation methods, and impact on data collection. To explain principal stratification we will give a very brief introduction into potential outcomes and causal inference. This is essential to understand why we actually randomize in clinical trials.

    More on the oncology estimand WG: http://www.oncoestimand.org


  2. Miguel Pereira – statistical consultant working with Cogitars


  3. Jenny Devenport People & Product Leader, Product Development Data Sciences at Roche

    Title: Is Medical Affairs the Wild West of Statistics? (Part II)

    – Never bring a knife to a gunfight

    Abstract: Drug development is a highly regulated process, designed to ensure that market authorizations are granted only after a medicine is demonstrated to be safe and effective in the target patient population.  But believe it or not, market authorization is just the start—getting new medicines to appropriate patients in clinical practice is the finish line and this involves addressing additional stakeholder questions in a somewhat less structured and potentially more resource-constrained environment. Statisticians working in the access and medical affairs arenas must have a thorough understanding of the strengths and limitations of the data leading to health authority approval, curiosity about the needs of different stakeholders, familiarity with the treatment landscape (ie., current and future treatment options), tolerance of ambiguity, and creativity to address evidence gaps.  In this session I will introduce the needs of different stakeholders, their evolving evidence demands, and opportunities for statisticians to add tremendous value.  


  4. Stefan Walzer – CEO, President & Founder at MArS Market Access & Pricing Strategy


  5. Kimberley Hacquoil – Chief Data Scientific Officer at Exploristics

    Title: Fake it to make it: Informing strategy with simulation

    In this new digital era, where we are already inundated with information and data, why would we want to create more data? Doesn’t more data mean more noise, more confusion, and more uncertainty?

    I would argue that if it’s the right data then more data brings transparency, clarity and drives clearer decision-making. In this talk I will be covering why it’s so critical to embrace uncertainty through simulation of appropriate data to inform the design and analysis of clinical trial programs.

  6. Alan Brnabic – Director, research statistics RWE/HTA/medical affairs at Lilly


  7. Heidi Seibold Open and Reproducible Data Science Trainer & Consultant

    Why Reproducible Research?

    Abstract: Reproducibility is a minimum standard when working with data: when the same analysis is conducted on the same data we expect the same results. Yet, making analyses reproducible is something most people struggle with. In this talk I will explain why reproducibility is so crucial and share a few tips for and insights on reproducible research.

  8. Gary Sullivan Leadership Expert/Statistical Scientist at Espirer Consulting


  9. Christian Haupricht  – Shaping and leading tech projects, processes and organizations


  10. Alexander Schacht – CEO at Sanevidence

    Title: How to get more time to work to create great data visualisations!

    The number #1 reason for not creating better data visualisations I hear is a lack of time. And I can understand this very well given the increasing number of responsibilities statisticians have.

    Still, great data visualisations will help you communicate your results much more effectively than tables or the usual bar and line charts. And they will make you stand out from the crowd.

    In this presentation, I will speak about ways how you can get more support and create data visualisations more effectively.

  11. Greg Ball

    Title: How to work together on a multi-disciplinary team – insights from over 20 years as a biostatistician and 8 years of dedicated work in safety evaluation.

    Abstract: We all work on projects with people from other functions, but no other area depends on cross-functional communication and mutual understanding more than safety. Safety, clinical, epidemiology, statistics, and other functions work together to get to a deep understanding of the product safety profile.
    Safety assessment is fundamentally different from efficacy analysis and we cannot continue to simply repurpose the same approaches established for efficacy. Adjusting for multiplicity is one such example.
    In this presentation, Greg will share his rich experience of cross-disciplinary scientific engagement working on safety management teams. However, not only safety statisticians will benefit from this.

  12. Thomas Neitmann

The conference is sponsored by: 

If you would like to sponsor this conference or other activities of The Effective Statistician, please check this brochure

The Conference and the Academy are run in partnership with:

  • VVS – Podcast Management and Production Team

  • Anna Voelske – Photography

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