Today, Curriculum Fellows Sarah Robertson & Lawson led wonderful hands-on sessions 👏

For CICI, Sarah covered composite #datasets & balance assessment. For TTE, Lawson delved into estimating effects in target trials vs emulated target trials. Learn more about our fellows below ⬇️

So excited for the second week of CAUSALab summer courses! Looking forward to meeting participants of our Target Trial Emulation course today 🎉

Had a great time serving as a Teaching Fellow for the ACA course.

Big thanks to the instructors Barbra Dickerman , Joy Shi, and Miguel Hernán , my co-fellows Sophia Rein , Naiyu Chen , and Sanjana Bhaskar , as well as to all participants for this insightful week!

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.

#SER2023 attendees interested in cancer & causal inference, check out Joy Shi's work to emulate target trials of PSA-based screening strategies & prostate cancer mortality using EHRs:

Poster P278, 6:30-7:30p today

Harvard Epidemiology CAUSALab

At #SER2023 ?

Catch Emma McGee's talk on estimating long-term effects of lifestyle interventions for cancer survivors using target trial emulation!

'Cancer epidemiology: A life course perspective in the causal era,' 1:45-3:15p today, Salon E

Harvard Epidemiology CAUSALab

Meet Joy Shi, Instructor Harvard Epidemiology. Her work focuses on the development of novel causal inference methods—primarily for instrumental variable analysis—and their applications to chronic diseases.

👉 Expand your knowledge by learning from Joy Shi in ACA or TTE!

A big congratulations to CAUSALab's doctoral graduates, Chelsea Messinger, PhD, David Bellamy, Kᴀᴛʜᴇʀɪɴᴇ Guilin Li, and @KerollosWanis!

We are so proud of your accomplishments and wish you many more successes in the future. Congratulations!

I am thrilled that @ConnorBGrady will be joining the PhD in Population Health Sciences Harvard T.H. Chan School of Public Health Harvard Epidemiology in Fall 2023! Can't wait to team up on cancer-causal projects.

🚀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/

Please join us in congratulating CAUSALab researcher Joy Shi who was promoted to Instructor of Epidemiology at Harvard T.H. Chan School of Public Health & Harvard Epidemiology!

Congratulations, Joy! We are lucky to have you part of the CAUSALab team.

#Postdoc candidates! Interested in #CausalInference and #EHRdata for #CancerResearch ? Work with Barbra Dickerman.

Projects on
💊Drug repurposing
🔎Smarter screening strategies
⚕️Optimal timing and sequencing of therapy
(+ more!)

Apply👇
causalab.sph.harvard.edu/postdoctoral/

📢 Postdoc opportunity in #cancer research! 📢

✊ Causal inference
🖥️ Large EHR databases
🎯 Dynamic strategies for cancer control
🤝 Fantastic team

Apply here 👇

Excited to be leading this initiative with Alejandro Szmulewicz

Join us at CAUSALab and let's talk #causalinference

1/
TODAY: We compared the safety of #COVID19 mRNA vaccines in the largest healthcare system in the US.

TAKE-HOME MESSAGE: Both Moderna and Pfizer vaccines are quite safe. If interested in vaccination, you can confidently use whichever one is available.
jamanetwork.com/journals/jamai…

Week 2 of the CAUSALab summer courses is all about how to emulate a #TargetTrial using healthcare databases.

A combo of lectures + hands-on sessions in R/SAS.

Come join us!

Registration now open: ow.ly/H50b50I6hFU

Get ready! Registration for the 2022 CAUSALab summer courses will open Monday, February 28th at 4:00 pm EST. Both weeks will be held in person at the Harvard T.H. Chan School of Public Health.

Course details and logistics can be found here: causalab.sph.harvard.edu/courses/

My paper on Mendelian randomization (MR) with time-varying exposures came out a while ago and I’m finally writing a tweetorial. Many thanks to collaborators and CAUSALab for their contributions.

So let’s talk about MR and what it's estimating. (1/x)
x.com/epidemiologylw…

Do you love working with code/programming, big data, and public health? Is SAS or R your second language? Come check out this Senior Programmer role with CAUSALab in the Department of Epidemiology Harvard T.H. Chan School of Public Health! ow.ly/CbRI50Hu8t9