Analyzing the Analysts: Reproducibility with Nick Huntington-Klein | Season 5 Episode 4

Aaditya Ramdas is an assistant professor at Carnegie Mellon University, in the Departments of Statistics and Machine Learning. His research interests include game-theoretic statistics and sequential anytime-valid inference, multiple testing and post-selection inference, and uncertainty quantification for machine learning (conformal prediction, calibration). His applied areas of interest include neuroscience, genetics and auditing (real-estate, finance, elections). Aaditya received the IMS Peter Gavin Hall Early Career Prize, the COPSS Emerging Leader Award, the Bernoulli New Researcher Award, the NSF CAREER Award, the Sloan fellowship in Mathematics, and faculty research awards from Adobe and Google. He also spends 20% of his time at Amazon working on causality and sequential experimentation. Aaditya’s website: https://www.stat.cmu.edu/~aramdas/ Game theoretic statistics resources Aaditya’s course, Game-theoretic probability, statistics, and learning: https://www.stat.cmu.edu/~aramdas/gtpsl/index.html Papers of interest: Time-uniform central limit theory and asymptotic confidence sequences: https://arxiv.org/abs/2103.06476 Game-theoretic statistics and safe anytime-valid inference: https://arxiv.org/abs/2103.06476 Discussion papers: Safe Testing: https://arxiv.org/abs/1906.07801 Testing by Betting: https://academic.oup.com/jrsssa/article/184/2/407/7056412 Estimating means of bounded random variables by betting: https://academic.oup.com/jrsssb/article/86/1/1/7043257  Follow along on Twitter: The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats 🎶 Our intro/outro music is courtesy of Joseph McDadeEdited by Cameron Bopp

Ingrid is a doctoral student in Epidemiology at the Dalla Lana School of Public Health at the University of Toronto.  Winning cookie recipe Follow along on Twitter: The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats 🎶 Our intro/outro music is courtesy of Joseph McDadeEdited by Cameron Bopp

Nick Huntington-Klein is an Assistant Professor, Department of Economics, Albers School of Business and Economics, Seattle University. His research focus is econometrics, causal inference, and higher education policy. He’s also the author of an introductory causal inference textbook called The Effect and the creator of a number of Stata packages for implementing causal effect estimation procedures. Nick’s book, online version: https://theeffectbook.net/ The Paper of How: https://onlinelibrary.wiley.com/share/W2FMEESMMSJMWDEZYY8Y?target=10.1111/obes.12598 Nick’s twitter & BlueSky: @nickchk Nick’s website: https://nickchk.com Follow along on Twitter: The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats 🎶 Our intro/outro music is courtesy of Joseph McDadeEdited by Cameron Bopp    

Lucy and Ellie chat about immortal time bias, discussing a new paper Ellie co-authored on clone-censor-weights.  The Clone-Censor-Weight Method in Pharmacoepidemiologic Research: Foundations and Methodological Implementation: https://link.springer.com/article/10.1007/s40471-024-00346-2  Immortal time in pregnancy: https://pubmed.ncbi.nlm.nih.gov/36805380/  Follow along on Twitter: The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats 🎶 Our intro/outro music is courtesy of Joseph McDadeEdited by Cameron Bopp

Mark van der Laan is a professor of statistics at the University of California, Berkeley. His research focuses on developing statistical methods to estimate causal and non-causal parameters of interest, based on potentially complex and high dimensional data from randomized clinical trials or observational longitudinal studies, or from cross-sectional studies.  Center for Targeted Learning, Berkeley: https://ctml.berkeley.edu/ A causal roadmap: https://pubmed.ncbi.nlm.nih.gov/37900353/  Short course on causal learning: https://ctml.berkeley.edu/introduction-causal-inference  Handbook on the TLverse (Targeted Learning in R): https://ctml.berkeley.edu/publications/targeted-learning-handbook-causal-machine-learning-and-inference-tlverse-r-software  Mark on twitter: @mark_vdlaan Follow along on Twitter: The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats 🎶 Our intro/outro music is courtesy of Joseph McDadeEdited by Cameron Bopp

Ellie and Lucy kick off the season and introduce our new executive buzzer, Melita! Melita is a masters student in statistics at Wake Forest University and will be helping out with the podcast (and keeping Lucy and Ellie from using too much jargon!) Pros & Cons of RCT paper:  Fernainy, P., Cohen, A.A., Murray, E. et al. Rethinking the pros and cons of randomized controlled trials and observational studies in the era of big data and advanced methods: a panel discussion. BMC Proc 18 (Suppl 2), 1 (2024). https://doi.org/10.1186/s12919-023-00285-8 Follow along on Twitter: The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats 🎶 Our intro/outro music is courtesy of Joseph McDadeEdited by Cameron Bopp

We are re-releasing an episode from 2021 in remembrance of Ralph D'Agostino, Sr.  Ellie Murray and Lucy D’Agostino McGowan chat with Ralph D’Agostino Sr. and Ralph D’Agostino Jr. about their careers in statistics, looking back at how things have developed and forward at where they see the world of statistics and epidemiology going.  Ralph D’Agostino Sr. was a professor of Mathematics/Statistics, Biostatistics, and Epidemiology at Boston University. He was the lead biostatistician for the Framingham Heart Study, a biostatistical consultant to The New England Journal of Medicine, an editor of Statistics in Medicine and lead editor of their Tutorials, and a member and consultant on FDA committees. His major fields of research were clinical trials, prognostic models, longitudinal analysis, multivariate analysis, robustness, and outcomes/effectiveness research.  Ralph D’Agostino Jr. is a professor in the Department of Biostatistics and Data Science at Wake Forest University where he is the Director of the Biostatistics Core of the Comprehensive Cancer Center. Methodologically his research includes developing statistical techniques for evaluating data from observational settings, handling missing data in applied problems, and developing predictive functions to identify prospectively patients at elevated risk for future negative outcomes. Some of his recent work includes the development of methods using propensity score models to identify safety signals in large retrospective databases. 

Ellie and Lucy chat with Dr. Cat Hicks, VP of Research Insights and Director of Developer Success Lab at Pluralsight Flow, about evidence science.    Follow along on Twitter: Cat: @grimalkina The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats 🎶 Our intro/outro music is courtesy of Joseph McDade Edited by Quinn Rose: aspiringrobot.com

Lucy D'Agostino McGowan and Ellie Murray chat about a "Causal Quartet" and spend some extra time on M-Bias!   Lucy, Travis, & Malcom's Causal Quartet Paper Lucy's quartets R package Follow along on Twitter: The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats 🎶 Our intro/outro music is courtesy of Joseph McDade Edited by Quinn Rose: aspiringrobot.com

Lucy D'Agostino McGowan and Ellie Murray chat about ENAR 2023 and Targeted Learning! Targeted Learning in R Handbook Follow along on Twitter: The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats 🎶 Our intro/outro music is courtesy of Joseph McDade Edited by Quinn Rose: aspiringrobot.com