Biometrics Section Early Career Paper Award Session

Assessing treatment effect moderation is critical in biomedical research and many other fields, as it guides personalized intervention strategies to improve participant's health outcomes. Individual participant–level data meta-analysis (IPD–MA) offers a robust framework for such assessments by leveraging data from multiple studies. However, its performance is often compromised by challenges such as high between-study variability or small magnitude of moderation effect. Traditional Bayesian shrinkage methods have gained popularity, but are less suitable in MA, as their priors do not discern heterogeneous studies. In this paper, we propose the calibrated mixtures of g–priors in IPD–MA to enhance efficiency and reduce risks in the estimation of moderation effects, providing a novel series of priors tailored for multiple studies by incorporating a study–level calibration parameter and a moderator-level shrinkage. This design offers a flexible range of shrinkage levels, allowing practitioners to evaluate moderator importance from both conservative and optimistic perspectives. Compared with existing Bayesian shrinkage methods, our extensive simulation studies demonstrate that the calibrated mixtures of g–priors exhibit superior performances in terms of efficiency and risk metrics, particularly under high between–study variability, high model sparsity, weak moderation effects and correlated design matrices. We further illustrate their application in assessing effect moderators of two active treatments for major depressive disorder, using IPD from four randomized controlled trials. 

We propose a novel inferential procedure for longitudinal function-on-function regression models. The method utilizes a marginal approach consisting of three steps: (1) fit pointwise longitudinal scalar-on-function regression models, (2) apply smoothers along the outcome functional domain, and (3) compute confidence bands for parameter estimates. A simulation study shows this approach provides accurate estimation and inference while being much more computationally efficient than existing approaches. Methods are motivated by a large physical activity study in older adults with data collected over multiple visits. 

We propose generalized conditional functional principal components analysis (GC-FPCA) for the joint modeling of the fixed and random effects of non-Gaussian functional outcomes. The method scales up to very large functional data sets by estimating the principal components of the covariance matrix on the linear predictor scale conditional on the fixed effects. This is achieved by combining three modeling innovations: (1) fit local generalized linear mixed models (GLMMs) conditional on covariates in windows along the functional domain; (2) conduct a functional principal component analysis (FPCA) on the person-specific functional effects obtained by assembling the estimated random effects from the local GLMMs; and (3) fit a joint functional mixed effects model conditional on covariates and the estimated principal components from the previous step. GC-FPCA was motivated by modeling the minute-level active/inactive profiles over the day (1,440 0/1 measurements per person) for 8,700 study participants in the National Health and Nutrition Examination Survey (NHANES) 2011-2014. We show that state-of-the-art approaches cannot handle data of this size and complexity, while GC-FPCA can. 

This session presents the winning paper awards of the 2025 Biometrics Section Early Career Paper Awards:

Lu Yu, doctoral student at Johns Hopkins University
"Generalized Conditional Functional Principal Component Analysis"
(Recipient of the David P. Byar Early Career Award)

Leif Verace, doctoral student at the University of Minnesota
"Marginal Longitudinal Function-on-Function Regression"

Qiao Wang, postdoctoral associate at Duke University
"Bayesian Hierarchical Models with Calibrated Mixtures of g-Priors for Assessing Treatment Effect Moderation in Meta-Analysis"

Ajmery Jaman, doctoral student at McGill University
"Penalized G-Estimation for Effect Modifier Selection in a Structural Nested Mean Model for Repeated Outcomes"
(This paper is being presented in a separate Invited Session)

Congratulations to the 2025 winners!