Alex Dmitrienko has researched and published so much, that without a doubt, he can be called one of the world class experts in the field of multiplicity. E.g. his book Multiple Testing Problems in Pharmaceutical Statistics contains a wealth of practical information on this topic.
His career has brought him from pharma over CROs to his own company Mediana. Via his company, he provides also online courses on his favourite topic. Please access these here!
As a listener, you will get the following benefits from this episode:
- Understand multiplicity challenges in practical situations
- Learn how to actually implement it
- Learn where to get further information
We cover the following topics during the interview:
- What is multiplicity and why does it matter?
- Should we always test as many objectives as possible or is it better to restrict the list of objectives?
- How do we communicate best those objectives, that are non-significant on the multiple testing adjusted case but are significant on the local level?
- Non-regulatory stakeholders like payers and physicians may have completely different views on the priority or importance of variables. How should we manage this?
- There are many different ways how setting up systems for multiplicity adjustments. What are the best steps to come to an agreement with a cross-functional team on this?
- How do we deal with multiplicity, if the study has different components?
- How do we deal with multiplicity, if we have 2 or more studies and analyse them combined as well as individually?
- What are good resources to learn about multiplicity for beginners?
About Alex Dmitrienko
Alex Dmitrienko, PhD, is Founder and President of Mediana Inc. Dr. Dmitrienko has been involved in pharmaceutical statistics for 20 years and, prior to founding Mediana, worked at Quintiles (Vice President, Innovation Unit) and at Lilly (Research Advisor, Center for Applied Statistical Expertise).
He has been actively involved in biostatistical research and has published over 100 papers on key topics in clinical trial statistics, including multiple comparisons, subgroup analysis, clinical trial optimization and adaptive designs. He has authored/edited two SAS Press books (Analysis of Clinical Trials Using SAS, Pharmaceutical Statistics Using SAS) and two Chapman and Hall/CRC Press books (Multiple Testing Problems in Pharmaceutical Statistics and Clinical Trial Optimization Using R). Dr. Dmitrienko has served as an Associate Editor for The American Statistician, Biometrics and Statistics in Medicine, and is a Fellow of the American Statistical Association.
- Biopharmaceutical Software Working Group
- Mediana (R package for simulation-based power/sample size calculations in fixed-sample trials)
- Mediana (Windows-based package for traditional and simulation-based power/sample size calculations in fixed-sample and group-sequential trials)
- Online training program
- Alosh, M., Bretz, F., Huque, M. (2014). Advanced multiplicity adjustment methods in clinical trials. Statistics in Medicine. 33, 693-713.
- Dmitrienko, A., Tamhane, A.C., Bretz, F. (editors) (2009). Multiple Testing Problems in Pharmaceutical Statistics. Chapman and Hall/CRC Press, New York.
- Dmitrienko, A., D’Agostino, R.B., Huque, M.F. (2013). Key multiplicity issues in clinical drug development. Statistics in Medicine. 32, 1079-1111.
- Dmitrienko, A., D’Agostino, R.B. (2013). Tutorial in Biostatistics: Traditional Multiplicity Adjustment Methods in Clinical Trials. Statistics in Medicine. 32, 5172-5218.
- Dmitrienko, A., Pulkstenis, E. (editors). (2017). Clinical Trial Optimization Using R. Chapman and Hall/CRC Press, New York.
- Dmitrienko, A., D’Agostino, R.B. (2018). Multiplicity considerations in clinical trials. New England Journal of Medicine. 378, 2115-2122.
- European Medicines Agency. Guideline on multiplicity issues in clinical trials. 2017.
- U.S. Food and Drug Administration. Multiple endpoints in clinical trials: guidance for industry. 2017.
- Huque, M.F., Dmitrienko, A., D’Agostino, R.B. (2013). Multiplicity issues in clinical trials with multiple objectives. Statistics in Biopharmaceutical Research. 5, 321-337.
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