Keep it casual with the Casual Inference podcast. Your hosts Lucy D'Agostino McGowan and Ellie Murray talk all things epidemiology, statistics, data science, causal inference, and public health. Sponsored by the American Journal of Epidemiology.
Lucy D'Agostino McGowan and Ellie Murray
The Casual Inference podcast is a hidden gem in the world of data science content. While the topic of causal inference may be underrepresented, this podcast shines a light on its importance. Although it may be epidemiology-focused, data science practitioners will also find value in its discussions and insights. One of the best aspects of this podcast is the hosts themselves - they are both sweet and fun, creating an enjoyable listening experience. It's refreshing to see that they keep things relatively nontechnical, making it accessible to a wide range of listeners.
The variety show format of The Casual Inference podcast adds to its appeal. With interviews, news segments, occasional teaching segments, and Q&A sessions, there's always something new and interesting happening. The only thing missing from this variety show is musical numbers! The hosts do a commendable job in showcasing different disciplinary approaches to inference, demonstrating their ecumenical mindset when it comes to exploring various perspectives. They have an impressive wealth of knowledge in epidemiology, biostatistics, and causal inference, which they share with enthusiasm.
While there aren't many downsides to this podcast, one potential drawback is that it may not cater to those seeking highly technical discussions on causal inference. However, this is easily outweighed by the fact that the hosts make an effort to keep things casual and approachable for all listeners. Additionally, some may argue that having more frequent episodes or longer episodes would be beneficial in order to cover more ground or delve deeper into certain topics.
In conclusion, The Casual Inference podcast is a true treasure for anyone interested in or involved in epidemiology, biostatistics, or data science. It provides valuable insights into state-of-the-art methods while maintaining a lighthearted and approachable tone throughout each episode. The guests are leaders and pioneers in the field who bring their own unique perspectives and expertise. Kudos to the American Journal of Epidemiology for sponsoring this exceptional content. Whether you're new to causal inference or looking to advance your understanding of the methods, this podcast is a must-listen.
Carly Brantner is an assistant professor of Biostatistics & Bioinformatics at Duke University and Duke Clinical Research Institute. Resources from this episode: multicate: R package for estimating conditional average treatment effects across one or more studies using machine learning methods PCORnet® Front Door: Access point for potential investigators, patient groups, and other stakeholders to connect with PCORnet and get support for potential research studies Patient-Centered Outcomes Data Repository (PDOCR): De-identified data from 24 (and counting) PCORI-funded studies Follow along on Bluesky: Carly: @carlybrantner.bsky.social Ellie: @epiellie.bsky.social Lucy: @lucystats.bsky.social
Andrew Heiss is an assistant professor in the Department of Public Management and Policy at the Andrew Young School of Policy Studies at Georgia State University. Vincent's “What is your estimand” section in his {marginaleffects} book: https://marginaleffects.com/chapters/challenge.html#sec-goals_estimand Article on defining estimands: https://doi.org/10.1177/00031224211004187 Andrew's marginal effects post: https://www.andrewheiss.com/blog/2022/05/20/marginalia/ Andrew's post on “fixed effects” and mariginal effects across different disciplines: https://www.andrewheiss.com/blog/2022/11/29/conditional-marginal-marginaleffects/ Follow along on Bluesky: Andrew: @andrew.heiss.phd Ellie: @epiellie.bsky.social Lucy: @lucystats.bsky.social
In this episode Lucy and Ellie dig into a recently publicized paper, "Vaccination and Neurodevelopmental Disorders: A Study of Nine-Year-Old Children Enrolled in Medicaid", which has gained attention after being promoted by RFK Jr. as evidence that vaccines cause autism. Ellie breaks down her Substack critique of the study. Together, she and Lucy discuss the methodological flaws and what a better version of this study might look like. Vaccination and Neurodevelopmental Disorders: A Study of Nine-Year-Old Children Enrolled in Medicaid: https://publichealthpolicyjournal.com/vaccination-and-neurodevelopmental-disorders-a-study-of-nine-year-old-children-enrolled-in-medicaid/ RFK Jr is promoting a new study claiming "vaccines cause autism" but it doesn't add up. Literally [Ellie's substack]: https://epiellie.substack.com/p/rfk-jr-is-promoting-a-new-study-claiming Follow along on Bluesky: Ellie: @epiellie.bsky.social Lucy: @lucystats.bsky.social
Vincent Arel-Bundock is a professor at the Université de Montréal, where he studies comparative and international political economy. Vincent's website: https://arelbundock.com/ Vincent's book "Model to Meaning: How to Interpret Statistical Models With marginaleffects for R and Python": https://marginaleffects.com/ Follow along on Bluesky: Vincent: @vincentab.bsky.social Ellie: @epiellie.bsky.social Lucy: @lucystats.bsky.social
Noah Greifer is a statistical consultant and programmer at Harvard University. Episode notes: WeightIt package: https://ngreifer.github.io/WeightIt/ MatchIt package: https://kosukeimai.github.io/MatchIt/ Noah's awesome Stack Exchange post: https://stats.stackexchange.com/a/544958 Follow along on Bluesky: Noah: @noahgreifer.bsky.social Ellie: @EpiEllie.bsky.social Lucy: @LucyStats.bsky.social
Lucy and Ellie chat about large language models, chat interfaces, and causal inference. Do LLMs Act as Repositories of Causal Knowledge?: https://arxiv.org/html/2412.10635v1 Follow along on Twitter: The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats
Lucy chats with Len Testa about a recent analysis he did which combined over 150 publicly available data sources to answer a question about the affordability of Disney World. Len's Deep Dive Post on the Touring Plans Blog [Blog Post] Wall Street Journal Artcile, "Even Disney Is Worried About the High Cost of a Disney Vacation" [Article] Follow along on Bluesky: Len: @lentesta.bsky.social Ellie: @EpiEllie.bsky.social Lucy: @LucyStats.bsky.social
Alyssa Bilinski, Peterson Family Assistant Professor of Health Policy, and Assistant Professor of Biostatistics, at Brown University School of Public Health. Her research focuses on developing novel methods for policy evaluation and applying these to identify interventions that most efficiently improve population health and well-being. Episode notes: PNAS paper: https://www.pnas.org/doi/full/10.1073/pnas.2302528120 Shuo Feng's pre-print: https://www.medrxiv.org/content/10.1101/2024.04.08.24305335v1 Our uncertainty paper: https://pubmed.ncbi.nlm.nih.gov/33475686/ Follow along on Twitter: Alyssa: @ambilinski The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats
Edward Kennedy Associate Professor, Department of Statistics & Data Science, Carnegie Mellon. Episode notes: ehkennedy.com Evaluating a Targeted Minimum Loss-Based Estimator for Capture-Recapture Analysis: An Application to HIV Surveillance in San Francisco, California: https://academic.oup.com/aje/article/193/4/673/7425624 Doubly Robust Capture-Recapture Methods for Estimating Population Size: https://www.tandfonline.com/doi/full/10.1080/01621459.2023.2187814 Follow along on Twitter: The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats
Sheree Bekker & Stephen Mumford are Co-directors of the Feminist Sport Lab and have a book coming soon: “Open Play: the case for feminist sport”, coming Spring 2025. Reaktion Books (UK), University of Chicago Press (US). Sheree Bekker: Associate Professor, University of Bath, Department for Health, Centre for Qualitative Research Centre for Health and Injury and Illness Prevention in Sport Stephen Mumford, Professor of Metaphysics, Durham University A Author of Dispositions (Oxford, 1998), Russell on Metaphysics (Routledge, 2003), Laws in Nature (Routledge, 2004), David Armstrong (Acumen, 2007), Watching Sport: Aesthetics, Ethics and Emotion (Routledge, 2011), Getting Causes from Powers (Oxford, 2011 with Rani Lill Anjum), Metaphysics: a Very Short Introduction (Oxford, 2012) and Causation: a Very Short Introduction (Oxford, 2013 with Rani Lill Anjum). I was editor of George Molnar's posthumous Powers: a Study in Metaphysics (Oxford, 2003) and Metaphysics and Science (Oxford, 2013 with Matthew Tugby). Episode notes: Feminist Sport Lab: https://www.feministsportlab.com Causation: A Very Short Introduction by Stephen Mumford & Rani Lill Anjum: https://academic.oup.com/book/616 Faye Norby, Iditarod champion & epidemiologist: https://www.kfyrtv.com/2024/03/28/faye-norby-finishes-iditarod-trail-womens-foot-champion/?outputType=amp Follow along on Twitter: The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats
Erick Scott is founder of cStructure, a causal science startup. Erick has expertise in medicine, public health, and computational biology. info@cStructure.io “A causal roadmap for generating high-quality real-world evidence” https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10603361/ Follow along on Twitter: The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats
Nima Hejazi is an assistant professor in biostatistics at Harvard University. His methodological work often draws upon tools and ideas from semi- and non-parametric inference, high-dimensional and large-scale inference, targeted or debiased machine learning (e.g., targeted minimum loss estimation, method of sieves), and computational statistics. Surprised by the Hot Hand Fallacy? A Truth in the Law of Small Numbers by Joshua B. Miller & Adam Sanjurjo: https://www.jstor.org/stable/44955325 Nima is on Twitter/X as @nshejazi (https://twitter.com/nshejazi) and my academic webpage is https://nimahejazi.org Recent translational review paper (intended for the infectious disease science community) I was involved in describing some causal/statistical frameworks for evaluating immune markers as mediators / surrogate endpoints: https://pubmed.ncbi.nlm.nih.gov/38458870/ The tlverse software ecosystem is on GitHub at https://github.com/tlverse and the tlverse handbook is freely available at https://tlverse.org/tlverse-handbook/ Dr. Hejazi annually co-teaches a causal mediation analysis workshop at SER, and notes from the latest offering are freely available at https://codex.nimahejazi.org/ser2023_mediation_workshop/ Follow along on Twitter: The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats
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 Follow along on Twitter: The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats
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
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
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
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
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
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
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
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
Lucy D'Agostino McGowan and Ellie Murray chat with #EpiCookieChallenge winner, Viktoria Gastens! Follow along on Twitter: The American Journal of Epidemiology: @AmJEpi Viktoria: @VikiGastens Viktoria's Lab: @PopHealthLabCH Ellie: @EpiEllie Lucy: @LucyStats
Lucy D'Agostino McGowan and Ellie Murray chat about confounding! ✍️ Lucy's new paper: Sensitivity Analyses for Unmeasured Confounders Follow along on Twitter: The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats
Lucy D'Agostino McGowan and Ellie Murray chat about randomized controlled trials, thinking about efficacy vs effectiveness and saftey vs safetiness. ✍️ Frank Harrell's blog post "Randomized Clinical Trials Do Not Mimic Clinical Practice, Thank Goodness" Follow along on Twitter: The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats
Lucy D'Agostino McGowan and Ellie Murray chat with Maria Glymour, Professor of Epidemiology & Biostatstics at UCSF and incoming chair of the Department of Epidemiology at Boston University. Maria successfully convinces Ellie and Lucy that instrumental variables can be very useful in epidemiology. Follow up: ✍️ Andrew Heiss's blog post on marginal and conditional effects for GLMMs Follow along on Twitter: Maria Glymour: @MariaGlymour The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats
Lucy D'Agostino McGowan and Ellie Murray chat about critiquing methods research, average treatment effects, and positivity violations! Follow along on Twitter: The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats
Lucy D'Agostino McGowan and Ellie Murray chat with Travis Gerke, Director of Data Science at The Prostate Cancer Clinical Trials Consortium (PCCTC). This episode has lots of hot takes and lots of love for logistic regression! Follow along on Twitter: Travis Gerke: @travisgerke The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats
Lucy D'Agostino McGowan and Ellie Murray chat about counterfactuals! Follow along on Twitter: The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats
In this episode Ellie Murray and Lucy D'Agostino McGowan chat with Enrique Schisterman, Perelman Professor and Chair of the Department of Biostatistics, Epidemiology, and Informatics at the University of Pennsylvania, about the future of epidemiology. Follow along on Twitter: Enrique: @eschisterman1 The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats
In this episode we play the audio from a recent panel discussion co-sponsored by UNC TraCS, Duke University and Wake Forest U CTSA Biostatistics, Epidemiology and Research Design (BERD) Cores. The panelists were Charles Poole (Associate Professor of Epidemiology, UNC) Lucy D'Agostino McGowan, and Charles Scales (Associate Professor of Surgery, Duke University) and it was facilitated by Marcella Boynton (Assistant Professor, General Internal Medicine, UNC/NC TraCS).
In this episode Lucy D'Agostino McGowan and Ellie Murray chat with Sander Greenland, Emeritus Professor of Epidemiology and Statistics at UCLA. Follow along on Twitter: The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats
In this episode Lucy D'Agostino McGowan and Ellie Murray chat with Toyya Pujol, Operations Researcher at RAND Corporation. Follow along on Twitter: Toyya: @toyyapujol The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats
In this episode Lucy D'Agostino McGowan and Ellie Murray chat with Maggie Makar, Presidential postdoctoral fellow and assistant professor in Computer Science and Engineering at the University of Michigan. Follow along on Twitter: Maggie: @Maggiemakar The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats Slide link: https://bit.ly/3DnQai5
In this episode Lucy D'Agostino McGowan and Ellie Murray chat with Judea Pearl, Chancellor professor of computer science and statistics at the University of California, Los Angeles.
In this episode Lucy D'Agostino McGowan and Ellie Murray chat with #EpiCookieChallenge winner, Chris Schaich about the epidemiologist John Snow. Dr. Schaich is an assistant professor at Wake Forest School of Medicine in the Hypertension and Vascular Research Center. Follow along on Twitter: Chris: @Chris_Schaich The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats Slide link: https://bit.ly/3DnQai5
In this episode Lucy D'Agostino McGowan and Ellie Murray chat about their Spotify Wrapped for Casual Inference, and Ellie Murray talks about causal inference for complex data with the University of Minnesota's epidemiology department. Follow along on Twitter: The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats Slide link: https://bit.ly/3DnQai5 Transcript (auto-generated): https://bit.ly/3y4OskQ
In this episode Lucy D'Agostino McGowan and Ellie Murray chat about the history of causal inference, tracing the origins across disciplines from statistics to economics, epidemiology, and computer science, discussing contributions from Rubin, Robins, Pearl, and more! Follow along on Twitter: The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats
In this episode Lucy D'Agostino McGowan and Ellie Murray chat with Hilary Parker about design thinking for data analysis, the Dunning-Kruger effect, and the potential data behind baby Yoda. Follow along on Twitter: Hilary: @hspter The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats
In this episode Lucy D'Agostino McGowan and Ellie Murray chat with Noah Haber about metascience, causal language in the literature, and more!
In this episode Lucy D'Agostino McGowan and Ellie Murray chat with Len Testa, president of TouringPlans, about solving optimization problems in travel and healthcare.
In this episode Lucy D'Agostino McGowan and Ellie Murray chat with Ashley Buchanan about causal inference with a focus on networks. Dr. Buchanan is an assistant professor of Biostatistics in the Department of Pharmacy Practice at the University of Rhode Island.
In this episode Ellie Murray and Lucy D'Agostino McGowan do a series recap and then discuss sensitivity, specificity, and appropriate messaging in the context of coronavirus rapid tests.
In this 23rd episode of Casual Inference Ellie Murray and Lucy D'Agostino McGowan chat about fixed vs random effect, complete a statistics challenge, and talk about DAGs.
In this episode Ellie Murray and Lucy D’Agostino McGowan chat with Julia Raifman about health policy, a recent study on unemployment insurance and food insecurity, and anti racism in academia. Dr. Raifman is an assistant professor of Health Law, Policy, and Management at Boston University. Her research focuses on how health and social policies drive population health and health disparities.
In this episode 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. We’re excited to kick off the 100th year of the American Journal of Epidemiology with this episode. Ralph D’Agostino Sr. is a professor of Mathematics/Statistics, Biostatistics, and Epidemiology at Boston University. He has been 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 are 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. It also turns out they are Lucy’s father and grandfather, so we have 3 generations of statisticians on the pod! We also have Amit Sasson on to discuss the winning cookie from the #EpiCookieChallenge as well as her work in causal inference! Follow along on Twitter: The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats
In honor of the Society for Epidemiologic Research 2020 Meeting, the hosts of four epidemiology podcasts came together to record the first ever “crossover event” to talk about their experiences recording our shows and what podcasting can bring to the table for the field of epidemiology. Join the hosts of Epidemiology Counts (Bryan James), SERiousEPi (Matt Fox, Hailey Banack), Casual Inference (Lucy D’Agostino McGowan), and Shiny Epi People (Lisa Bodnar) as they engage in a fun and informative (we hope!) conversation of the burgeoning field of epidemiology podcasting, emceed by Geetika Kalloo.
Ellie Murray and Lucy D'Agostino McGowan chat about ecological studies, the new Pfizer vaccine interim analysis, and more!
Ellie Murray and Lucy D'Agostino McGowan chat about communicating uncertainty, how air pollution policy is determined, and whether causal inference is a fad with Dr. Roger Peng from Johns Hopkins Bloomberg School of Public Health. Follow along on Twitter: The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats Roger: @rdpeng
Ellie Murray and Lucy D'Agostino McGowan talk about the causal questions linked to schools opening during the COVID-19 pandemic. Then they have Dr. Emily Oster, professor of economics at Brown University, on to discuss her thoughts on and contributions to this area.
Ellie Murray and Lucy D'Agostino McGowan casually discuss linear versus logistic regression, prediction versus inference, generalized linear models, and more!
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