Meeting Calendar

Upcoming events

10/23/2026

Friday 1:30-2:45pm, 

Place: Genome Cafe, Room E3609

Light lunch 1:10-1:30pm

Zoom link: https://jh.zoom.us/j/94338128089

Speaker: Youngjoo Cho, Associate Professor, Department of Applied Statistics, Konkuk University

Title: Transporting randomized trial results to estimate counterfactual survival functions in target populations

Abstract : When the distributions of treatment effect modifiers differ between a randomized trial and an external target population, the sample average treatment effect in the trial may be substantially different from the target population average treatment, and accurate estimation of the latter requires adjusting for the differential distribution of effect modifiers. Despite the increasingly rich literature on transportability, little attention has been devoted to methods for transporting trial results to estimate counterfactual survival functions in target populations, when the primary outcome is time to event and subject to right censoring. In this article, we study inverse probability weighting and doubly robust estimators to estimate counterfactual survival functions and the target average survival treatment effect in the target population, and provide their respective approximate variance estimators. We focus on a common scenario where the target population information is observed only through a complex survey, and elucidate how the survey weights can be incorporated into each estimator we considered. Simulation studies are conducted to examine the finite-sample performances of the proposed estimators in terms of bias, efficiency and coverage, under both correct and incorrect model specifications. Finally, we apply the proposed method to assess transportability of the results in the Action to Control Cardiovascular Risk in Diabetes—Blood Pressure (ACCORD-BP) trial to all adults with Diabetes in the United States.

11/6/2026

Speaker: Rajeshwari Sundaram, Senior Investigator, Biostatistics & Bioinformatics Branch, Eunice Kennedy Shriver National Institutes of Child Health and Human Development, NIH

Title: From Complex Mixtures to Clear Inference: Statistical Methods for Environmental Health

Abstract: Environmental mixture studies present substantial statistical challenges because exposures are highly correlated, may have nonlinear effects, and are frequently measured below the limit of detection (LOD) or maybe lipophilic, while health outcomes may involve longitudinal processes and survival times. We have developed an integrated framework for variable selection and inference that jointly addresses these features within complex environmental mixture analyses. The framework combines a joint model for skewed longitudinal and discrete survival outcomes with shared frailty. We developed Discnet for selecting correlated exposures in discrete time-to-event analyses and conducted principled evaluation of LOD accommodation strategies for downstream mixture methods. These approaches enable selection of important exposures while accounting for dependence between outcomes, exposure correlation, censoring, nonlinear relationships, and measurements below the LOD.

Simulation studies demonstrate reliable variable selection and estimation for the joint model, stable false-positive and false-negative rates, and meaningful differences in mixture-analysis performance across LOD strategies. Applications to the LIFE Study and NHANES illustrate the utility of the framework for identifying important environmental exposures across reproductive and other health outcomes. Together, these methods advance statistical methodology for complex mixture studies by providing a coherent approach to exposure selection and inference across longitudinal, survival, and censored exposure data.

3/5/2027

Speaker: Fan Li, Professor, Dept of Statistical Science, Duke University

Tentative Title: Sample size and power calculation for causal inference with time-to-event outcomes