主 题：Multiplicative Structural Nested Mean Model for Zero-Inflated Outcomes
主讲人：Lu Wenbin Professor，Statistics at North Carolina State University
地 点：线上Zoom会议ID: 86860610732密码：721918
Zero-inflated nonnegative outcomes are common in many applications. In this work, motivated by freemium mobile game data, we propose a class of multiplicative structural nested mean models for zero-inflated nonnegative outcomes, which flexibly describes the joint effect of a sequence of treatments in the presence of time-varying confounders. The proposed estimator solves a doubly robust estimating equation, where the nuisance functions, propensity score and conditional outcome means given confounders, are estimated parametrically or nonparametrically. To improve the accuracy, we leverage the characteristic of zero-inflated outcomes by estimating the conditional means in two parts, that is, separately modeling the probability of having positive outcomes given confounders and the mean outcome conditional on its being positive and confounders. We show that the proposed estimator is consistent and asymptotically normal as either the sample size or the follow-up time goes to infinity. Moreover, the typical sandwich formula can be used to estimate the variance of treatment effect estimators consistently, without accounting for the variation due to estimating nuisance functions. Simulation studies and an application to a freemium mobile game dataset are presented to demonstrate the empirical performance of the proposed method and support our theoretical findings.
Wenbin Lu is Professor of Statistics at North Carolina State University. He obtained his Ph.D. from the Department of Statistics at Columbia University in 2003. His research interests include biostatistics, high-dimensional data analysis, statistical and machine learning methods for precision medicine, and network data analysis. He has published more than 100 papers in a variety of statistical journals, including Biometrika, Journal of the American Statistical Association, Journal of the Royal Statistical Society (Series B), Annals of Statistics, and Journal of Machine Learning Research. His research is partly funded by several grants from the National Institute of Health. He is an Associate Editor for Biostatistics, Biometrics and Statistica Sinica, and a fellow of American Statistical Association.