Title
Model Misspecification in Statistical Analysis
Speaker
Qian Zhou, Harvard
Abstract
In general, model misspecification can lead to invalid inference for parameter estimation and risk prediction. In the context of quasi-likelihood inference, most of the existing statistical methods primarily focus on assessing the validity of the mean structure. However, limited work addresses the adequacy of the variance/covariance (Var/Cov) structure, and more specifically, there lacks a powerful systematic statistical test for model misspecification in Var/Cov. In this talk, I will introduce a novel and unified framework for testing such misspecification. Our method shows substantial improvement and is more robust in comparison to several popular existing statistical methods.
In the context of risk prediction, I will talk about some challenges that arise in the evaluation of the incremental value in prediction accuracy by adding new biomarkers. In light of these challenges, we have proposed novel statistical procedures for systematically identifying potential subgroups that can benefit from the measurement of additional markers. Notably, our method is robust against possible model misspecification. Finally, I will discuss developing and evaluating absolute risk prediction models with newly identified biomarkers under nested case-control sampling design; here, measurement of biomarkers on the whole study population is neither feasible nor cost-effective.
