Biostatistics Seminar - Dr. Bingkai Wang on Model-robust and efficient covariate adjustment for cluster-randomized trials
When & Where
February 7, 2023
12:00 PM - 1:00 PM
WebEx ( View in Google Map)
Contact
- Scott Dyson
- [email protected]
Event Description
Presenter:
Bingkai Wang, Ph.D.
Department of Statistics and Data Science
The Wharton School, University of Pennsylvania
Location:
WebEx, see event website to connect. Password: 7VYxEnPUS65
Abstract:
Cluster-randomized trials are increasingly used to evaluate interventions in routine practice conditions, and researchers often adopt model-based methods with covariate adjustment in the statistical analyses. However, the validity of model-based covariate adjustment is unclear when the working models are misspecified, leading to ambiguity of estimands and risk of bias. In this talk, I will first introduce an adaption of two conventional model-based methods, generalized estimating equations and linear mixed models, with weighted g-computation to achieve robust inference for cluster-average and individual-average treatment effects. Then, I will introduce an efficient estimator for each estimand that allows for flexible covariate adjustment and additionally addresses cluster size variation dependent on treatment assignment and other cluster characteristics. Such cluster size variations often occur post-randomization and, if ignored, can lead to bias of model-based estimators. In the end, I will briefly discuss my work on other aspects of cluster-randomized trials, including covariate-adaptive randomization, survival analysis, stepped-wedge design, and test-negative design.
Event Site Link
https://uthealth.webex.com/uthealth/j.php?MTID=m14268d1c749933a0c7c8a1a79cca4b1b
Additional Information
Presenter:
Bingkai Wang, Ph.D.
Department of Statistics and Data Science
The Wharton School, University of Pennsylvania
Location:
WebEx, see event website to connect. Password: 7VYxEnPUS65
Abstract:
Cluster-randomized trials are increasingly used to evaluate interventions in routine practice conditions, and researchers often adopt model-based methods with covariate adjustment in the statistical analyses. However, the validity of model-based covariate adjustment is unclear when the working models are misspecified, leading to ambiguity of estimands and risk of bias. In this talk, I will first introduce an adaption of two conventional model-based methods, generalized estimating equations and linear mixed models, with weighted g-computation to achieve robust inference for cluster-average and individual-average treatment effects. Then, I will introduce an efficient estimator for each estimand that allows for flexible covariate adjustment and additionally addresses cluster size variation dependent on treatment assignment and other cluster characteristics. Such cluster size variations often occur post-randomization and, if ignored, can lead to bias of model-based estimators. In the end, I will briefly discuss my work on other aspects of cluster-randomized trials, including covariate-adaptive randomization, survival analysis, stepped-wedge design, and test-negative design.