Bringing nutritional epidemiology into the era of causal inference at #SER2022. It's all about #TargetTrial emulation. Joy Shi (Joy Shi): instrumental variable methods. Yu-Han Chiu (Han Chiu): confounder adjustment methods. Session chairs: Deirdre Tobias, ScD & Martín Lajous

Many Mendelian randomization analyses of pharmaceutically modifiable biomarkers are restricted to non-users of the drug. Joy Shi explains at #SER2022 that this approach is biased and explains how to do it better. Work with Sonja Swanson, Elizabeth Diemer, and Miguel Hernán.

Excited to be leading this initiative with Alejandro Szmulewicz Join us at CAUSALab and let's talk #causalinference

📢 Postdoc opportunity in #cancer research! 📢 ✊ Causal inference 🖥️ Large EHR databases 🎯 Dynamic strategies for cancer control 🤝 Fantastic team Apply here 👇

1/ Six years ago we emulated a #TargetTrial of screening colonoscopy for colorectal #cancer using observational #Medicare data: pubmed.ncbi.nlm.nih.gov/27669524/ Today we publish preliminary findings from a truly randomized trial: nejm.org/doi/full/10.10… Their main results side by side👇

New study! Results from the NORDICC trial, "Effect on Colonoscopy Screenings on Risks of Colorectal Cancer and Related Death" published in NEJM. Study led by Universitetet i Oslo and contributed by CAUSALab researchers Miguel Hernán and Joy Shi. Learn more: causalab.sph.harvard.edu/causalab-news/

Appreciate the shoutout from CAUSALab! I also teach methods for causal inference, and am an advocate for making these methods more accessible to students/researchers. Many of the teaching materials that I've developed are freely available on my website (link in bio).

Interested in #causalinference? This summer, learn from top experts in the field. Four courses will be held both in person at the Harvard T.H. Chan School of Public Health and online. 👉To find out which courses are right for you visit causalab.sph.harvard.edu/courses/

Thank you! Excited to continue working alongside incredible CAUSALab researchers. I'll also be part of an exceptional teaching team for the CAUSALab summer courses—join us for two weeks to learn more about advanced confounding adjustment methods and target trial emulation😉

🚀New CAUSALab summer course!🚀 Advanced Confounding Adjustment Learn to implement g-methods (IP weighting, parametric g-formula) in the settings of time-fixed and time-varying treatment. 4 days of lectures + hands-on sessions in R/SAS. Register here! causalab.sph.harvard.edu/courses/

One of the highlights of my job is getting to work with Barbra Dickerman. If you're looking for a mentor who's thoughtful, kind and REALLY knows her stuff, then 👇

👉 Interested in causal effects for time-varying treatments? Sign up for our new course Advanced Confounding Adjustment. Joy Shi Barbra Dickerman and Miguel Hernán cover the theory and implementation of advanced methods to address bias due to confounding and selection bias.

ACA starts in less than 3 weeks! Excited to be meeting all the folks joining in-person and online. There's still time to register to join us in Boston if you haven't yet😉

For the first time we are hosting two courses simultaneously this year. Our #new course Advanced Confounding Adjustment began yesterday! 🎉 Joy Shi and Barbra Dickerman welcomed 130 participants from across the globe in leading the first couple lectures and hands-on sessions.

Closing out day 1 of our Causal Inference course with Joy Shi 🤯 #epitwitter #causalinference

Please join us in congratulating Joy Shi for her new role as Assistant Professor in the Mongan Institute at MassGeneral News! We are thrilled to continue working together at CAUSALab.