Associate Professor, Department of Biostatistics @ysphbiostat@YaleSPH. Biostatistics, clinical trials, observational studies, causal inference, clustered data.
At the Center for Methods in Implementation and Prevention Science (CMIPS) at the Yale School of Public Health @YaleSPH, we develop and apply rigorous biostatistical and epidemiological methods to address these gaps and accelerate real-world impact.
Big data is transforming public health and society like never before.
This summer, we are launching the Big Data Summer Immersion at Yale (BDSY), an interdisciplinary training, research, and professional development program in biostatistics.
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youtu.be/uYbe9gL2KbY
Ready to transform your analytical skills in the BIG data era? Join us at @YaleSPH for the Big Data Summer Immersion program! This 6-week program introduces undergraduate students to cutting-edge challenges in Big Data, Statistics, and Human Health. Applications open on Dec 15.
. @Yuting_Qian_ presents work “Multilevel Factors Associated With Racial and Ethnic Disparities in Timely Diagnosis of Dementia in US Older Adults” in a session on #Alzheimer's disease and related dementias.
Joint work w @FanLi90@geronsociety@YaleSPH @Yale_OAIC @YaleHPM
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In this new paper led by @BingkaiWang, we showed that stepped wedge randomization can guard against covariate, random-effects, and error structure misspecifications, though treatment effect structure still requires careful consideration.
academic.oup.com/biometrics/…
Excited to share our new paper in Biometrika! We make quantile causal inference accessible by introducing an inverse estimating equations approach that extends causal estimation from mean potential outcomes to their quantiles. academic.oup.com/biomet/arti…
Excited to announce that @cards_lab is launching the DETECT-AS Study w R01 funding from @NIH/@NIHAging
Details: cards-lab.org/detect-as
- Multicenter RCT of AI-enabled automated detection of aortic stenosis on ECG POCUS
- precision prognostication w DASSi
Excited to share that our new paper is now online! We formalized average treatment effect estimands in stepped wedge cluster randomized trials and show that linear regression can provide robust & estimand-aligned inference under informative cluster sizes
onlinelibrary.wiley.com/doi/…
Members of our Biostatistics and Study Design Core tested the performance of various analytic models for individually randomized group treatment trials in which complex clustering arises from participants interacting with multiple intervention agents.
➡️ duke.is/pctnews20240912
Does the hazard ratio have a causal interpretation? Should Cox modeling be used in analyzing clinical trials? Read all about it in this point-counterpoint of Michael Fay and Fan Li versus Dan Heitjan. @SCTorg@YaleSPH@SouthernMethod@FanLi90
Chao Cheng and colleagues show how to examine causal effects on the survival probability scale in the presence of treatment noncompliance in clinical trials. @FanLi90@YaleSPH@SCTorg#clinicaltrials
📢Abstract deadline extension: the deadline for abstracts to the 10th Annual Meeting on Current Developments in Cluster Randomised Trials & Stepped Wedge Designs (13-14 Nov, Birmingham UK) has been extended to 5th July. Submit as Word/PDF to crt-swd@contacts.bham.ac.uk.
If you are interested in causal inference with time-dependent treatments beyond the mean, please check out at our most recent paper on marginal structural quantile models. This work is led by my PhD student Chao Cheng, and jointly with @lyhuStatreeacademic.oup.com/biometrics/…
Worry about random-effects misspecification in stepped wedge trials? Then consider the cluster-robust sandwich variance estimator as part of the primary analysis. We offer some practical recommendations in this simulation study led by @douyang3journals.sagepub.com/doi/10.…